Hospital Medicine

Should physicians be subject to random drug testing? It’s a controversial topic. One in 10 Americans suffer from a drug use disorder at some point in their lives.¹ Although physicians engaging in drug diversion is very rare, we recognize, in the context of rising rates of opiate use, that drug misuse and addiction can involve physicians.^2,3 When it occurs, addiction can drive behaviors that endanger both clinicians and patients. Media reports on drug diversion describe an anesthesiologist who died of overdose from diverted fentanyl and a surgical technician with HIV who used and replaced opioids in the operating room, resulting in thousands of patients needing to be tested for infection.⁴ Multiple outbreaks of hepatitis C involving more than a dozen hospitals in eight states were traced to a single health care provider diverting narcotics.⁵ An investigation of outbreaks at various medical centers in the United States over a 10-year period identified nearly 30,000 patients that were potentially exposed and more than 100 iatrogenic infections.⁶

The profession of medicine holds a special place in the esteem of the public, with healthcare providers being among the most trusted professions. Patients rely on us to keep them safe when they are at their most vulnerable. This trust is predicated on the belief that the profession of medicine will self-regulate. Drug diversion by clinicians is a violation of this trust.

Our hospital utilizes existing structures to address substance use disorder; such structures include regular education on recognizing impairment for the medical staff, an impaired clinician policy for suspicion of impairment, and a state physician health program that provides nonpunitive evaluation and treatment for substance use by clinicians. In response to the imperative to mitigate the potential for drug diversion, our health system undertook a number of additional initiatives. These initiatives, included inventory control and tracking of controlled substances, and random testing and trigger-based audits of returned medications to ensure the entire amount had been accounted for. As part of this system-wide initiative, UCHealth began random drug testing of employees in safety-sensitive positions (for whom impairment would represent the potential for harm to others). Medical staff are not employees of the health system and were not initially subject to testing. The key questions at the time included the following:

• Is our organization doing everything possible to prevent drug diversion?
• If nurses and other staff are subject to random drug testing, why would physicians be exempt?

The University of Colorado Hospital (UCH) is the academic medical center within UCHealth. The structure of the relationship between the hospital and its medical staff requires the question of drug testing for physicians to be addressed by the UCH Medical Board (Medical Executive Committee). Medical staff leadership and key opinion leaders were engaged in the process of considering random drug testing of the medical staff. In the process, medical staff leadership raised additional questions about the process of decision making:

• “How should this issue be handled in the context of physician autonomy?”
• “How do we assure the concerns of the medical staff are heard and addressed?”

The guiding principles considered by the medical staff leadership in the implementation of random drug testing included the following: (1) as a matter of medical professionalism, for random drug testing to be implemented, the medical staff must elect to submit to mandatory testing; (2) the random drug testing program must be designed to minimize harm; and (3) the process for random drug testing program design needs to engage front-line clinicians. This resulted in a series of communications, meetings, and outreach to groups within the medical staff.

From front-line medical staff members, we heard overwhelming consensus for the moral case to prevent patient harm resulting from drug diversion, our professional duty to address the issue, and the need to maintain public trust in the institution of medicine. At the same time, medical staff members often expressed skepticism regarding the efficacy of random drug testing as a tactic, concerns about operational implementation, and fears regarding the unintended consequences:

• How strong is the evidence that random drug testing prevents drug diversion?
• How can we be confident that false-positive tests will not cause innocent clinicians to be incorrectly accused of drug use?

The efficacy of random drug testing in preventing drug diversion is not settled. The discussion of how to proceed in the absence of well-designed studies on the tactic was robust. One common principle we heard from members of the medical staff was that our response be driven by an authentic organizational desire to reduce patient harm. They expressed that the process of testing needs to respect the boundaries between work and home life and to avoid the disruption of clinical responsibilities. Whether targeting testing to “higher risk” groups of clinicians is appropriate and whether or not alcohol and/or marijuana would be tested came up often.

Other concerns expressed also included the intrusion of the institution into the private medical conditions of the medical staff members, breach of confidentiality, or accessibility of the information obtained as a result of the program for unrelated legal proceedings. One of the most prominent fears expressed was the possible impact of false-positive tests on the clinicians’ careers.

Following the listening tour by the medical staff and hospital leadership and extensive discussions, the Medical Board voted to approve a policy to implement random drug testing. The deliberative process lasted for approximately eight months. We sought input from other healthcare systems, such as the Veterans Administration and Cleveland Clinic, that conduct random drug tests on employed physicians. A physician from Massachusetts General Hospital who led the 2004 implementation of random drug testing for anesthesiologists was invited to come to Colorado to give grand rounds about the experience in his department and answer questions about the implementation of random drug testing at a Medical Board meeting.⁷ The policy went into effect January 2017.

The design of the program sought to explicitly address the issues raised by the front-line clinicians. In the interest of equity, all specialties, including Radiology and Pathology, are subject to testing. Medical staff are selected for testing using a random number generator and retained in the random selection pool at all times, regardless of previous selection for testing. Consistent with the underlying objective of identifying drug diversion, testing is limited to drugs at higher risk for diversion (eg, amphetamine, barbiturate, benzodiazepine, butorphanol, cocaine metabolite, fentanyl, ketamine, meperidine, methadone, nalbuphine, opiates, oxycodone, and tramadol). Although alcohol and marijuana are substances of abuse, they are not substances of healthcare diversion and thus are excluded from random drug testing (although included in testing for impairment). Random drug testing is conducted only for medical staff who are onsite and providing clinical services. The individuals selected for random drug testing are notified by Employee Health, or their clinical supervisor, to present to Employee Health that day to provide a urine sample. The involvement of the clinical supervisor in specific departments and the flexibility in time of presentation was implemented to address the concerns of the medical staff regarding harm from the disruption of acute patient care.

To address the concern regarding false-positive tests, an external medical laboratory that performs testing compliant with Substance Abuse and Mental Health Services and governmental standards is used. Samples are split providing the ability to perform independent testing of two samples. The thresholds are set to minimize false-positive tests. Positive results are sent to an independent medical review officer who confidentially contacts the medical staff member to assess for valid prescriptions to explain the test results. Unexplained positive test results trigger the testing of the second half of the split sample.

To address issues of dignity, privacy, and confidentiality, Employee Health discretely oversees the urine collection. The test results are not part of the individual’s medical record. Only the coordinator for random drug testing in Human Resources compliance can access the test results, which are stored in a separate, secure database. The medical review officer shares no information about the medical staff members’ medical conditions. A positive drug assay attributable to a valid medical explanation is reported as a negative test.

Positive test results, which would be reported to the President of the Medical Staff, would trigger further investigation, potential Medical Board action consistent with medical staff bylaws, and reporting to licensing bodies as appropriate. We recognize that most addiction is not associated with diversion, and all individuals struggling with substance use need support. The medical staff and hospital leadership committed through this process to connecting medical staff members who are identified by random drug testing to help for substance use disorder, starting with the State Physician Health Program.

The Medical Executive Committees of all hospitals within UCHealth have also approved random drug testing of medical staff. We are not the first healthcare organization to tackle the potential for drug diversion by healthcare workers. To our knowledge, we are the largest health system to have nonemployed medical staff leadership vote for the entire medical staff to be subject to random drug testing. Along the journey, the approach of random drug testing for physicians was vigorously debated. In this regard, we proffer one final question:

• How would you have voted?

Disclosures

The authors have nothing to disclose.

Should physicians be subject to random drug testing? It’s a controversial topic. One in 10 Americans suffer from a drug use disorder at some point in their lives.¹ Although physicians engaging in drug diversion is very rare, we recognize, in the context of rising rates of opiate use, that drug misuse and addiction can involve physicians.^2,3 When it occurs, addiction can drive behaviors that endanger both clinicians and patients. Media reports on drug diversion describe an anesthesiologist who died of overdose from diverted fentanyl and a surgical technician with HIV who used and replaced opioids in the operating room, resulting in thousands of patients needing to be tested for infection.⁴ Multiple outbreaks of hepatitis C involving more than a dozen hospitals in eight states were traced to a single health care provider diverting narcotics.⁵ An investigation of outbreaks at various medical centers in the United States over a 10-year period identified nearly 30,000 patients that were potentially exposed and more than 100 iatrogenic infections.⁶

The profession of medicine holds a special place in the esteem of the public, with healthcare providers being among the most trusted professions. Patients rely on us to keep them safe when they are at their most vulnerable. This trust is predicated on the belief that the profession of medicine will self-regulate. Drug diversion by clinicians is a violation of this trust.

Our hospital utilizes existing structures to address substance use disorder; such structures include regular education on recognizing impairment for the medical staff, an impaired clinician policy for suspicion of impairment, and a state physician health program that provides nonpunitive evaluation and treatment for substance use by clinicians. In response to the imperative to mitigate the potential for drug diversion, our health system undertook a number of additional initiatives. These initiatives, included inventory control and tracking of controlled substances, and random testing and trigger-based audits of returned medications to ensure the entire amount had been accounted for. As part of this system-wide initiative, UCHealth began random drug testing of employees in safety-sensitive positions (for whom impairment would represent the potential for harm to others). Medical staff are not employees of the health system and were not initially subject to testing. The key questions at the time included the following:

• Is our organization doing everything possible to prevent drug diversion?
• If nurses and other staff are subject to random drug testing, why would physicians be exempt?

The University of Colorado Hospital (UCH) is the academic medical center within UCHealth. The structure of the relationship between the hospital and its medical staff requires the question of drug testing for physicians to be addressed by the UCH Medical Board (Medical Executive Committee). Medical staff leadership and key opinion leaders were engaged in the process of considering random drug testing of the medical staff. In the process, medical staff leadership raised additional questions about the process of decision making:

• “How should this issue be handled in the context of physician autonomy?”
• “How do we assure the concerns of the medical staff are heard and addressed?”

The guiding principles considered by the medical staff leadership in the implementation of random drug testing included the following: (1) as a matter of medical professionalism, for random drug testing to be implemented, the medical staff must elect to submit to mandatory testing; (2) the random drug testing program must be designed to minimize harm; and (3) the process for random drug testing program design needs to engage front-line clinicians. This resulted in a series of communications, meetings, and outreach to groups within the medical staff.

From front-line medical staff members, we heard overwhelming consensus for the moral case to prevent patient harm resulting from drug diversion, our professional duty to address the issue, and the need to maintain public trust in the institution of medicine. At the same time, medical staff members often expressed skepticism regarding the efficacy of random drug testing as a tactic, concerns about operational implementation, and fears regarding the unintended consequences:

• How strong is the evidence that random drug testing prevents drug diversion?
• How can we be confident that false-positive tests will not cause innocent clinicians to be incorrectly accused of drug use?

The efficacy of random drug testing in preventing drug diversion is not settled. The discussion of how to proceed in the absence of well-designed studies on the tactic was robust. One common principle we heard from members of the medical staff was that our response be driven by an authentic organizational desire to reduce patient harm. They expressed that the process of testing needs to respect the boundaries between work and home life and to avoid the disruption of clinical responsibilities. Whether targeting testing to “higher risk” groups of clinicians is appropriate and whether or not alcohol and/or marijuana would be tested came up often.

Other concerns expressed also included the intrusion of the institution into the private medical conditions of the medical staff members, breach of confidentiality, or accessibility of the information obtained as a result of the program for unrelated legal proceedings. One of the most prominent fears expressed was the possible impact of false-positive tests on the clinicians’ careers.

Following the listening tour by the medical staff and hospital leadership and extensive discussions, the Medical Board voted to approve a policy to implement random drug testing. The deliberative process lasted for approximately eight months. We sought input from other healthcare systems, such as the Veterans Administration and Cleveland Clinic, that conduct random drug tests on employed physicians. A physician from Massachusetts General Hospital who led the 2004 implementation of random drug testing for anesthesiologists was invited to come to Colorado to give grand rounds about the experience in his department and answer questions about the implementation of random drug testing at a Medical Board meeting.⁷ The policy went into effect January 2017.

The design of the program sought to explicitly address the issues raised by the front-line clinicians. In the interest of equity, all specialties, including Radiology and Pathology, are subject to testing. Medical staff are selected for testing using a random number generator and retained in the random selection pool at all times, regardless of previous selection for testing. Consistent with the underlying objective of identifying drug diversion, testing is limited to drugs at higher risk for diversion (eg, amphetamine, barbiturate, benzodiazepine, butorphanol, cocaine metabolite, fentanyl, ketamine, meperidine, methadone, nalbuphine, opiates, oxycodone, and tramadol). Although alcohol and marijuana are substances of abuse, they are not substances of healthcare diversion and thus are excluded from random drug testing (although included in testing for impairment). Random drug testing is conducted only for medical staff who are onsite and providing clinical services. The individuals selected for random drug testing are notified by Employee Health, or their clinical supervisor, to present to Employee Health that day to provide a urine sample. The involvement of the clinical supervisor in specific departments and the flexibility in time of presentation was implemented to address the concerns of the medical staff regarding harm from the disruption of acute patient care.

To address the concern regarding false-positive tests, an external medical laboratory that performs testing compliant with Substance Abuse and Mental Health Services and governmental standards is used. Samples are split providing the ability to perform independent testing of two samples. The thresholds are set to minimize false-positive tests. Positive results are sent to an independent medical review officer who confidentially contacts the medical staff member to assess for valid prescriptions to explain the test results. Unexplained positive test results trigger the testing of the second half of the split sample.

To address issues of dignity, privacy, and confidentiality, Employee Health discretely oversees the urine collection. The test results are not part of the individual’s medical record. Only the coordinator for random drug testing in Human Resources compliance can access the test results, which are stored in a separate, secure database. The medical review officer shares no information about the medical staff members’ medical conditions. A positive drug assay attributable to a valid medical explanation is reported as a negative test.

Positive test results, which would be reported to the President of the Medical Staff, would trigger further investigation, potential Medical Board action consistent with medical staff bylaws, and reporting to licensing bodies as appropriate. We recognize that most addiction is not associated with diversion, and all individuals struggling with substance use need support. The medical staff and hospital leadership committed through this process to connecting medical staff members who are identified by random drug testing to help for substance use disorder, starting with the State Physician Health Program.

The Medical Executive Committees of all hospitals within UCHealth have also approved random drug testing of medical staff. We are not the first healthcare organization to tackle the potential for drug diversion by healthcare workers. To our knowledge, we are the largest health system to have nonemployed medical staff leadership vote for the entire medical staff to be subject to random drug testing. Along the journey, the approach of random drug testing for physicians was vigorously debated. In this regard, we proffer one final question:

• How would you have voted?

Disclosures

The authors have nothing to disclose.

Should physicians be subject to random drug testing? It’s a controversial topic. One in 10 Americans suffer from a drug use disorder at some point in their lives.¹ Although physicians engaging in drug diversion is very rare, we recognize, in the context of rising rates of opiate use, that drug misuse and addiction can involve physicians.^2,3 When it occurs, addiction can drive behaviors that endanger both clinicians and patients. Media reports on drug diversion describe an anesthesiologist who died of overdose from diverted fentanyl and a surgical technician with HIV who used and replaced opioids in the operating room, resulting in thousands of patients needing to be tested for infection.⁴ Multiple outbreaks of hepatitis C involving more than a dozen hospitals in eight states were traced to a single health care provider diverting narcotics.⁵ An investigation of outbreaks at various medical centers in the United States over a 10-year period identified nearly 30,000 patients that were potentially exposed and more than 100 iatrogenic infections.⁶

The profession of medicine holds a special place in the esteem of the public, with healthcare providers being among the most trusted professions. Patients rely on us to keep them safe when they are at their most vulnerable. This trust is predicated on the belief that the profession of medicine will self-regulate. Drug diversion by clinicians is a violation of this trust.

Our hospital utilizes existing structures to address substance use disorder; such structures include regular education on recognizing impairment for the medical staff, an impaired clinician policy for suspicion of impairment, and a state physician health program that provides nonpunitive evaluation and treatment for substance use by clinicians. In response to the imperative to mitigate the potential for drug diversion, our health system undertook a number of additional initiatives. These initiatives, included inventory control and tracking of controlled substances, and random testing and trigger-based audits of returned medications to ensure the entire amount had been accounted for. As part of this system-wide initiative, UCHealth began random drug testing of employees in safety-sensitive positions (for whom impairment would represent the potential for harm to others). Medical staff are not employees of the health system and were not initially subject to testing. The key questions at the time included the following:

• Is our organization doing everything possible to prevent drug diversion?
• If nurses and other staff are subject to random drug testing, why would physicians be exempt?

The University of Colorado Hospital (UCH) is the academic medical center within UCHealth. The structure of the relationship between the hospital and its medical staff requires the question of drug testing for physicians to be addressed by the UCH Medical Board (Medical Executive Committee). Medical staff leadership and key opinion leaders were engaged in the process of considering random drug testing of the medical staff. In the process, medical staff leadership raised additional questions about the process of decision making:

• “How should this issue be handled in the context of physician autonomy?”
• “How do we assure the concerns of the medical staff are heard and addressed?”

The guiding principles considered by the medical staff leadership in the implementation of random drug testing included the following: (1) as a matter of medical professionalism, for random drug testing to be implemented, the medical staff must elect to submit to mandatory testing; (2) the random drug testing program must be designed to minimize harm; and (3) the process for random drug testing program design needs to engage front-line clinicians. This resulted in a series of communications, meetings, and outreach to groups within the medical staff.

From front-line medical staff members, we heard overwhelming consensus for the moral case to prevent patient harm resulting from drug diversion, our professional duty to address the issue, and the need to maintain public trust in the institution of medicine. At the same time, medical staff members often expressed skepticism regarding the efficacy of random drug testing as a tactic, concerns about operational implementation, and fears regarding the unintended consequences:

• How strong is the evidence that random drug testing prevents drug diversion?
• How can we be confident that false-positive tests will not cause innocent clinicians to be incorrectly accused of drug use?

The efficacy of random drug testing in preventing drug diversion is not settled. The discussion of how to proceed in the absence of well-designed studies on the tactic was robust. One common principle we heard from members of the medical staff was that our response be driven by an authentic organizational desire to reduce patient harm. They expressed that the process of testing needs to respect the boundaries between work and home life and to avoid the disruption of clinical responsibilities. Whether targeting testing to “higher risk” groups of clinicians is appropriate and whether or not alcohol and/or marijuana would be tested came up often.

Other concerns expressed also included the intrusion of the institution into the private medical conditions of the medical staff members, breach of confidentiality, or accessibility of the information obtained as a result of the program for unrelated legal proceedings. One of the most prominent fears expressed was the possible impact of false-positive tests on the clinicians’ careers.

Following the listening tour by the medical staff and hospital leadership and extensive discussions, the Medical Board voted to approve a policy to implement random drug testing. The deliberative process lasted for approximately eight months. We sought input from other healthcare systems, such as the Veterans Administration and Cleveland Clinic, that conduct random drug tests on employed physicians. A physician from Massachusetts General Hospital who led the 2004 implementation of random drug testing for anesthesiologists was invited to come to Colorado to give grand rounds about the experience in his department and answer questions about the implementation of random drug testing at a Medical Board meeting.⁷ The policy went into effect January 2017.

The design of the program sought to explicitly address the issues raised by the front-line clinicians. In the interest of equity, all specialties, including Radiology and Pathology, are subject to testing. Medical staff are selected for testing using a random number generator and retained in the random selection pool at all times, regardless of previous selection for testing. Consistent with the underlying objective of identifying drug diversion, testing is limited to drugs at higher risk for diversion (eg, amphetamine, barbiturate, benzodiazepine, butorphanol, cocaine metabolite, fentanyl, ketamine, meperidine, methadone, nalbuphine, opiates, oxycodone, and tramadol). Although alcohol and marijuana are substances of abuse, they are not substances of healthcare diversion and thus are excluded from random drug testing (although included in testing for impairment). Random drug testing is conducted only for medical staff who are onsite and providing clinical services. The individuals selected for random drug testing are notified by Employee Health, or their clinical supervisor, to present to Employee Health that day to provide a urine sample. The involvement of the clinical supervisor in specific departments and the flexibility in time of presentation was implemented to address the concerns of the medical staff regarding harm from the disruption of acute patient care.

To address the concern regarding false-positive tests, an external medical laboratory that performs testing compliant with Substance Abuse and Mental Health Services and governmental standards is used. Samples are split providing the ability to perform independent testing of two samples. The thresholds are set to minimize false-positive tests. Positive results are sent to an independent medical review officer who confidentially contacts the medical staff member to assess for valid prescriptions to explain the test results. Unexplained positive test results trigger the testing of the second half of the split sample.

To address issues of dignity, privacy, and confidentiality, Employee Health discretely oversees the urine collection. The test results are not part of the individual’s medical record. Only the coordinator for random drug testing in Human Resources compliance can access the test results, which are stored in a separate, secure database. The medical review officer shares no information about the medical staff members’ medical conditions. A positive drug assay attributable to a valid medical explanation is reported as a negative test.

Positive test results, which would be reported to the President of the Medical Staff, would trigger further investigation, potential Medical Board action consistent with medical staff bylaws, and reporting to licensing bodies as appropriate. We recognize that most addiction is not associated with diversion, and all individuals struggling with substance use need support. The medical staff and hospital leadership committed through this process to connecting medical staff members who are identified by random drug testing to help for substance use disorder, starting with the State Physician Health Program.

The Medical Executive Committees of all hospitals within UCHealth have also approved random drug testing of medical staff. We are not the first healthcare organization to tackle the potential for drug diversion by healthcare workers. To our knowledge, we are the largest health system to have nonemployed medical staff leadership vote for the entire medical staff to be subject to random drug testing. Along the journey, the approach of random drug testing for physicians was vigorously debated. In this regard, we proffer one final question:

• How would you have voted?

Disclosures

The authors have nothing to disclose.

Patient throughput in healthcare systems is increasingly important to policymakers, hospital leaders, clinicians, and patients alike. In 1983, Congress passed legislation instructing the Centers for Medicare and Medicaid Services (CMS) to implement the “prospective payment system,” which sets reimbursement for CMS hospitalizations to a fixed rate, regardless of the length of stay (LOS). Policy changes such as this coupled with increased market consolidation (ie, fewer hospitals for more patients) and increased patient acuity have created significant challenges for hospital leaders to manage patient throughput and reduce or maintain LOS.¹ Additionally, emergency department (ED) overcrowding and intensive care unit (ICU) capacity strain studies have demonstrated associations with adverse patient outcomes and quality of care.^2-5 Finally, and perhaps most importantly, the impact of these forces on clinicians and patients has compromised the patient-clinician relationship and patient experience. As patient throughput is important to multiple stakeholders, novel approaches to understanding and mitigating bottlenecks are imperative.

The article by Mishra and colleagues in this month’s issue of the Journal of Hospital Medicine (JHM) describes one such novel methodology to evaluate patient throughput at a major academic hospital.⁶ The authors utilized process mapping, time and motion study, and hospital data to simulate four discrete future states for internal medicine patients that were under consideration for implementation at their institution: (1) localizing housestaff teams and patients to specific wards; (2) adding an additional 26-bed ward; (3) adding an additional hospitalist team; and (4) adding an additional ward and team and allowing for four additional patient admissions per day. Each of these approaches improved certain metrics with the tradeoff of worsening other metrics. Interestingly, geographic localization of housestaff teams and patients alone (Future State 1) resulted in decreased rounding time and patient dispersion but increased LOS and ED boarding time. Adding an additional ward (Future State 2) had the opposite effect (ie, decreased LOS and ED boarding time but increased rounding time and patient dispersion). Adding an additional hospitalist team (Future State 3) did not change LOS or ED boarding time but reduced patient dispersion and team census. Finally, adding both a ward and hospitalist team (Future State 4) reduced LOS and ED boarding time but increased rounding time and patient dispersion. These results provide a compelling case for modeling changes in clinical operations to weigh the risks and benefits of each approach with hospital priorities prior to implementation of one strategy versus another.

This study is an important step forward in bringing a rigorous scientific approach to clinical operations. If every academic center, or potentially every hospital, were to implement the approach described in this study, the potential for improvement in patient outcomes, quality metrics, and cost reduction that have been the intents of policymakers for over 30 years could be dramatic. But even if this approach were implemented (or possibly as a result of implementation), additional aspects of hospital operations might be uncovered given the infancy of this critical field. Indeed, we can think of at least five additional factors and approaches to consider as next steps to move this field forward. First, as the authors noted, multiple additional simulation inputs could be considered, including multidisciplinary workflow (eg, housestaff, hospitalists, nurses, clinical pharmacists, respiratory therapists, social workers, case managers, physical and occupational therapists, speech and language pathologists, etc.) and allowing for patients to transfer wards and teams during their hospitalizations. Second, qualitative investigation regarding clinician burnout, multidisciplinary cohesiveness, and patient satisfaction are crucial to implementation success. Third, repeat time and motion studies would aid in assessing for changes in time spent with patients and for educational purposes under the new care models. Fourth, medicine wards and teams do not operate in isolation within a hospital. It would be important to evaluate the impact of such changes on other wards and services, as all hospital wards and services are interdependent. And finally, determining costs associated with these models is critical for hospital leadership, resource allocation, implementation, and sustainability. For example, Future State 4 would increase admissions by 1,080 per year, but would that offset the cost of opening a new ward and hiring additional clinicians?

In addition, the authors feature the profoundly important concept of “geographic localization.” This construct has been investigated primarily among critically ill patients. Geographic dispersion has been shown to be associated with adverse clinical outcomes and quality metrics.⁷ Although this has begun to be studied among ward patients,⁸ the authors take this a step further by modeling future states incorporating geographic localization. Future State 4 resulted in the best overall outcomes but increased rounding time and patient dispersion, although these differences were not statistically significant. This piques our curiosity about the possibility of a fifth future state: adding geographic localization to Future State 4. Adding a new ward and new clinician team might provide a unique opportunity to geographically localize patients and to study the collective impact. Additionally, it is possible that geographic localization only improves outcomes if all teams (ie, house-staff and hospitalist teams) have geographically localized patients rather than exclusively housestaff having geographically localized patients.

Indeed, these results raise much broader and interesting questions surrounding ward capacity strain, that is, when patients’ demand for clinical resources exceeds availability.⁹ At our institution, we conducted a study to define the construct of ward capacity strain and demonstrated that among patients admitted to wards from EDs and ICUs in three University of Pennsylvania Health System hospitals, selected measures of patient volume, staff workload, and overall acuity were associated with longer ED and ICU boarding times. These same factors accounted for decreased patient throughput to varying, but sometimes large, degrees.¹⁰ We subsequently used this same definition of ward capacity strain to evaluate the association with 30-day hospital readmissions. We demonstrated that ward capacity strain metrics improved prediction of 30-day hospital readmission risk in nearly one out of three hospital wards, with medications administered, hospital discharges, and census being three of the five strongest predictors of 30-day hospital readmissions.¹¹ These findings from our own institution further underscore the importance of the work by Mishra et al. and suggest future directions that could combine different measures of hospital throughput and patient outcomes into a more data-driven process for optimizing hospital resources, supporting the efforts of clinicians, and providing high-quality patient care.

This study is a breakthrough in the scientific rigor of hospital operations. It will lay the groundwork for a multitude of subsequent questions and studies that will move clinical operations into evidence-based practices. We find this work exciting and inspiring. We look forward to additional work from Mishra et al. and look forward to applying similar approaches to clinical operations at our institution.

Disclosures

The authors have nothing to disclose.

Funding

Dr. Kohn was supported by NIH/NHLBI F32 HL139107-01.

Patient throughput in healthcare systems is increasingly important to policymakers, hospital leaders, clinicians, and patients alike. In 1983, Congress passed legislation instructing the Centers for Medicare and Medicaid Services (CMS) to implement the “prospective payment system,” which sets reimbursement for CMS hospitalizations to a fixed rate, regardless of the length of stay (LOS). Policy changes such as this coupled with increased market consolidation (ie, fewer hospitals for more patients) and increased patient acuity have created significant challenges for hospital leaders to manage patient throughput and reduce or maintain LOS.¹ Additionally, emergency department (ED) overcrowding and intensive care unit (ICU) capacity strain studies have demonstrated associations with adverse patient outcomes and quality of care.^2-5 Finally, and perhaps most importantly, the impact of these forces on clinicians and patients has compromised the patient-clinician relationship and patient experience. As patient throughput is important to multiple stakeholders, novel approaches to understanding and mitigating bottlenecks are imperative.

The article by Mishra and colleagues in this month’s issue of the Journal of Hospital Medicine (JHM) describes one such novel methodology to evaluate patient throughput at a major academic hospital.⁶ The authors utilized process mapping, time and motion study, and hospital data to simulate four discrete future states for internal medicine patients that were under consideration for implementation at their institution: (1) localizing housestaff teams and patients to specific wards; (2) adding an additional 26-bed ward; (3) adding an additional hospitalist team; and (4) adding an additional ward and team and allowing for four additional patient admissions per day. Each of these approaches improved certain metrics with the tradeoff of worsening other metrics. Interestingly, geographic localization of housestaff teams and patients alone (Future State 1) resulted in decreased rounding time and patient dispersion but increased LOS and ED boarding time. Adding an additional ward (Future State 2) had the opposite effect (ie, decreased LOS and ED boarding time but increased rounding time and patient dispersion). Adding an additional hospitalist team (Future State 3) did not change LOS or ED boarding time but reduced patient dispersion and team census. Finally, adding both a ward and hospitalist team (Future State 4) reduced LOS and ED boarding time but increased rounding time and patient dispersion. These results provide a compelling case for modeling changes in clinical operations to weigh the risks and benefits of each approach with hospital priorities prior to implementation of one strategy versus another.

This study is an important step forward in bringing a rigorous scientific approach to clinical operations. If every academic center, or potentially every hospital, were to implement the approach described in this study, the potential for improvement in patient outcomes, quality metrics, and cost reduction that have been the intents of policymakers for over 30 years could be dramatic. But even if this approach were implemented (or possibly as a result of implementation), additional aspects of hospital operations might be uncovered given the infancy of this critical field. Indeed, we can think of at least five additional factors and approaches to consider as next steps to move this field forward. First, as the authors noted, multiple additional simulation inputs could be considered, including multidisciplinary workflow (eg, housestaff, hospitalists, nurses, clinical pharmacists, respiratory therapists, social workers, case managers, physical and occupational therapists, speech and language pathologists, etc.) and allowing for patients to transfer wards and teams during their hospitalizations. Second, qualitative investigation regarding clinician burnout, multidisciplinary cohesiveness, and patient satisfaction are crucial to implementation success. Third, repeat time and motion studies would aid in assessing for changes in time spent with patients and for educational purposes under the new care models. Fourth, medicine wards and teams do not operate in isolation within a hospital. It would be important to evaluate the impact of such changes on other wards and services, as all hospital wards and services are interdependent. And finally, determining costs associated with these models is critical for hospital leadership, resource allocation, implementation, and sustainability. For example, Future State 4 would increase admissions by 1,080 per year, but would that offset the cost of opening a new ward and hiring additional clinicians?

In addition, the authors feature the profoundly important concept of “geographic localization.” This construct has been investigated primarily among critically ill patients. Geographic dispersion has been shown to be associated with adverse clinical outcomes and quality metrics.⁷ Although this has begun to be studied among ward patients,⁸ the authors take this a step further by modeling future states incorporating geographic localization. Future State 4 resulted in the best overall outcomes but increased rounding time and patient dispersion, although these differences were not statistically significant. This piques our curiosity about the possibility of a fifth future state: adding geographic localization to Future State 4. Adding a new ward and new clinician team might provide a unique opportunity to geographically localize patients and to study the collective impact. Additionally, it is possible that geographic localization only improves outcomes if all teams (ie, house-staff and hospitalist teams) have geographically localized patients rather than exclusively housestaff having geographically localized patients.

Indeed, these results raise much broader and interesting questions surrounding ward capacity strain, that is, when patients’ demand for clinical resources exceeds availability.⁹ At our institution, we conducted a study to define the construct of ward capacity strain and demonstrated that among patients admitted to wards from EDs and ICUs in three University of Pennsylvania Health System hospitals, selected measures of patient volume, staff workload, and overall acuity were associated with longer ED and ICU boarding times. These same factors accounted for decreased patient throughput to varying, but sometimes large, degrees.¹⁰ We subsequently used this same definition of ward capacity strain to evaluate the association with 30-day hospital readmissions. We demonstrated that ward capacity strain metrics improved prediction of 30-day hospital readmission risk in nearly one out of three hospital wards, with medications administered, hospital discharges, and census being three of the five strongest predictors of 30-day hospital readmissions.¹¹ These findings from our own institution further underscore the importance of the work by Mishra et al. and suggest future directions that could combine different measures of hospital throughput and patient outcomes into a more data-driven process for optimizing hospital resources, supporting the efforts of clinicians, and providing high-quality patient care.

This study is a breakthrough in the scientific rigor of hospital operations. It will lay the groundwork for a multitude of subsequent questions and studies that will move clinical operations into evidence-based practices. We find this work exciting and inspiring. We look forward to additional work from Mishra et al. and look forward to applying similar approaches to clinical operations at our institution.

Disclosures

The authors have nothing to disclose.

Funding

Dr. Kohn was supported by NIH/NHLBI F32 HL139107-01.

Patient throughput in healthcare systems is increasingly important to policymakers, hospital leaders, clinicians, and patients alike. In 1983, Congress passed legislation instructing the Centers for Medicare and Medicaid Services (CMS) to implement the “prospective payment system,” which sets reimbursement for CMS hospitalizations to a fixed rate, regardless of the length of stay (LOS). Policy changes such as this coupled with increased market consolidation (ie, fewer hospitals for more patients) and increased patient acuity have created significant challenges for hospital leaders to manage patient throughput and reduce or maintain LOS.¹ Additionally, emergency department (ED) overcrowding and intensive care unit (ICU) capacity strain studies have demonstrated associations with adverse patient outcomes and quality of care.^2-5 Finally, and perhaps most importantly, the impact of these forces on clinicians and patients has compromised the patient-clinician relationship and patient experience. As patient throughput is important to multiple stakeholders, novel approaches to understanding and mitigating bottlenecks are imperative.

The article by Mishra and colleagues in this month’s issue of the Journal of Hospital Medicine (JHM) describes one such novel methodology to evaluate patient throughput at a major academic hospital.⁶ The authors utilized process mapping, time and motion study, and hospital data to simulate four discrete future states for internal medicine patients that were under consideration for implementation at their institution: (1) localizing housestaff teams and patients to specific wards; (2) adding an additional 26-bed ward; (3) adding an additional hospitalist team; and (4) adding an additional ward and team and allowing for four additional patient admissions per day. Each of these approaches improved certain metrics with the tradeoff of worsening other metrics. Interestingly, geographic localization of housestaff teams and patients alone (Future State 1) resulted in decreased rounding time and patient dispersion but increased LOS and ED boarding time. Adding an additional ward (Future State 2) had the opposite effect (ie, decreased LOS and ED boarding time but increased rounding time and patient dispersion). Adding an additional hospitalist team (Future State 3) did not change LOS or ED boarding time but reduced patient dispersion and team census. Finally, adding both a ward and hospitalist team (Future State 4) reduced LOS and ED boarding time but increased rounding time and patient dispersion. These results provide a compelling case for modeling changes in clinical operations to weigh the risks and benefits of each approach with hospital priorities prior to implementation of one strategy versus another.

This study is an important step forward in bringing a rigorous scientific approach to clinical operations. If every academic center, or potentially every hospital, were to implement the approach described in this study, the potential for improvement in patient outcomes, quality metrics, and cost reduction that have been the intents of policymakers for over 30 years could be dramatic. But even if this approach were implemented (or possibly as a result of implementation), additional aspects of hospital operations might be uncovered given the infancy of this critical field. Indeed, we can think of at least five additional factors and approaches to consider as next steps to move this field forward. First, as the authors noted, multiple additional simulation inputs could be considered, including multidisciplinary workflow (eg, housestaff, hospitalists, nurses, clinical pharmacists, respiratory therapists, social workers, case managers, physical and occupational therapists, speech and language pathologists, etc.) and allowing for patients to transfer wards and teams during their hospitalizations. Second, qualitative investigation regarding clinician burnout, multidisciplinary cohesiveness, and patient satisfaction are crucial to implementation success. Third, repeat time and motion studies would aid in assessing for changes in time spent with patients and for educational purposes under the new care models. Fourth, medicine wards and teams do not operate in isolation within a hospital. It would be important to evaluate the impact of such changes on other wards and services, as all hospital wards and services are interdependent. And finally, determining costs associated with these models is critical for hospital leadership, resource allocation, implementation, and sustainability. For example, Future State 4 would increase admissions by 1,080 per year, but would that offset the cost of opening a new ward and hiring additional clinicians?

In addition, the authors feature the profoundly important concept of “geographic localization.” This construct has been investigated primarily among critically ill patients. Geographic dispersion has been shown to be associated with adverse clinical outcomes and quality metrics.⁷ Although this has begun to be studied among ward patients,⁸ the authors take this a step further by modeling future states incorporating geographic localization. Future State 4 resulted in the best overall outcomes but increased rounding time and patient dispersion, although these differences were not statistically significant. This piques our curiosity about the possibility of a fifth future state: adding geographic localization to Future State 4. Adding a new ward and new clinician team might provide a unique opportunity to geographically localize patients and to study the collective impact. Additionally, it is possible that geographic localization only improves outcomes if all teams (ie, house-staff and hospitalist teams) have geographically localized patients rather than exclusively housestaff having geographically localized patients.

Indeed, these results raise much broader and interesting questions surrounding ward capacity strain, that is, when patients’ demand for clinical resources exceeds availability.⁹ At our institution, we conducted a study to define the construct of ward capacity strain and demonstrated that among patients admitted to wards from EDs and ICUs in three University of Pennsylvania Health System hospitals, selected measures of patient volume, staff workload, and overall acuity were associated with longer ED and ICU boarding times. These same factors accounted for decreased patient throughput to varying, but sometimes large, degrees.¹⁰ We subsequently used this same definition of ward capacity strain to evaluate the association with 30-day hospital readmissions. We demonstrated that ward capacity strain metrics improved prediction of 30-day hospital readmission risk in nearly one out of three hospital wards, with medications administered, hospital discharges, and census being three of the five strongest predictors of 30-day hospital readmissions.¹¹ These findings from our own institution further underscore the importance of the work by Mishra et al. and suggest future directions that could combine different measures of hospital throughput and patient outcomes into a more data-driven process for optimizing hospital resources, supporting the efforts of clinicians, and providing high-quality patient care.

This study is a breakthrough in the scientific rigor of hospital operations. It will lay the groundwork for a multitude of subsequent questions and studies that will move clinical operations into evidence-based practices. We find this work exciting and inspiring. We look forward to additional work from Mishra et al. and look forward to applying similar approaches to clinical operations at our institution.

Disclosures

The authors have nothing to disclose.

Funding

Dr. Kohn was supported by NIH/NHLBI F32 HL139107-01.

The United States spends approximately 18% of its gross domestic product on healthcare, nearly double the average expenditure by other high-income countries.¹ This increased financial investment does not consistently correlate with better care, as quality outcomes in the US rank well below many developed nations that spend far less on clinical care on a per capita basis.^1,2 These troubling and unsustainable spending trends have compelled national and regional policymakers, health system leaders, and researchers to search for ways to curb healthcare spending and improve healthcare value.

Approximately 32% of overall healthcare spending in the US occurs in hospitals,³ and there is broad acknowledgment that inpatient care can be delivered more cost effectively.⁴ In recent years, numerous policy interventions – including Medicare’s hospital readmission reductions program, hospital-acquired condition reductions program, hospital value-based purchasing program, and the Bundled Payment for Care Improvement program – have been implemented in an effort to improve the quality and costs of inpatient care.^4,5

These policies attempt to increase care value by utilizing innovative reimbursement techniques designed to hold clinical systems financially accountable for outcomes and spending. They are designed to move our system away from the traditional fee-for-service paradigm, which encourages overuse and has been identified as a major driver of bloated healthcare costs in the US.^6,7 The success of certain national payment reform pilots, such as the Comprehensive Care for Joint Replacement Model, indicate that payment models which hold clinicians and systems accountable hold promise for both reducing costs and improving outcomes.⁸

However, to influence clinical outcomes and costs, these national payment reforms must prompt local changes in how care is delivered and financed. Understanding systems- and clinician-level factors that enable the delivery of higher value care is, therefore, paramount for effectively translating national policies into local improvements in care value. Among hospitalists and hospital-based clinicians, institutional and clinical cultures represent an important lever for influencing physician practice patterns and, by extension, the quality and costs of care. Hospital and departmental cultures have been shown to influence physician behaviors profoundly in ways that improve quality and value, primarily via top-down initiatives focused on education and improving awareness. Examples of cultural success stories include efforts to reduce unnecessary utilization of diagnostic testing,⁹ improve adoption of hand-washing techniques on wards,¹⁰ and translate education about high-value care into sustained increases in the delivery of high-value clinical services.¹¹

In “The Association of Hospitals Productivity Payments and High-Value Care Culture,” Gupta et al. present the results of a study examining associations between how hospitals compensate their hospitalists – specifically the provision of performance-based incentives – and the strength of a hospital’s high-value care culture.¹² The authors administered the High-Value Care Culture Survey^TM (HVCCS), a validated survey instrument designed to assess the degree to which a hospital’s culture promotes the delivery of high-value care, to 255 hospitalists across 12 hospitals, including safety-net, community, and university-based hospitals. The hospitals’ predominant physician compensation models were grouped into three categories: salary model (no performance-based bonus), salary model with a productivity adjustment (ie, a bonus based on clinical volumes), and a salary model with a quality/value adjustment (ie, a bonus for delivering higher value care). The authors found that hospitalists who were salaried but also received productivity adjustments reported significantly lower mean HVCCS scores than salaried hospitalists who did not receive bonuses or adjustments. Compared with salaried hospitalists, hospitalists receiving compensation via salary plus value-based adjustments were nonsignificantly more likely to have higher HVCCS scores.

How are we to interpret these results? While we must be exceedingly careful about presuming causal mechanisms underlying these associations, they are nonetheless intriguing and should prompt further discussion about the relationship between payment incentives, provider behavior, and organizational culture. One potential explanation for these findings is that hospitals that rely on high clinical volumes to drive their financial performance may use productivity bonuses as a way to align hospitalists’ incentives with those of their institution, thereby promoting volume at the expense of value.

Behavioral economics theory provides an alternative lens through which to interpret the work of Gupta et al. The relationship between incentives and nonfinancial sources of personal motivation remain an important consideration in financial incentive design.¹³ A basic concept in behavioral economics is that there are two fundamental types of motivation of human behavior: extrinsic motivation, where people are motivated to act by the prospect of material rewards or punishments, and intrinsic motivation, a source of motivation that leads people to behave in ways that do not produce an obvious personal or material reward.¹³ Substantial evidence indicates that external rewards can have counterproductive effects on an individual’s intrinsic motivation, leading to a “crowding-out” effect that decreases the individual’s internal drive. When the “crowding-out” effect occurs, behaviors may be motivated by a desire to follow the rules, rather than true intrinsic drive. This change in the underlying forces motivating behavior can have a negative impact on self-esteem and result in a perceived loss of professional autonomy.^13,14 Perhaps more than any other professional group, healthcare professionals are fueled by intrinsic motivation and a yearning for professional autonomy. It is therefore plausible that doctors are particularly sensitive to, and disturbed by, the feeling that external rewards are “crowding out” this internal drive. Thus, the inverse association between productivity payments – volume-based rewards – and HVCCS scores may reflect this tension between intrinsic and extrinsic drives.

Of course, we need to interpret the authors’ findings cautiously in light of the cross-sectional study design and the potential for residual confounding. Indeed, the presence of an association between how hospitalists are compensated and their perceptions of the degree to which their institution’s culture promotes the delivery of high-value care does not prove that these two things are causally linked. Additionally, the small sample size limits the generalizability of these findings and efforts to draw robust conclusions from this work regarding the interplay between how a hospital pays its physicians, hospital culture, and the value of care delivered in this institution. Moreover, a more rigorous characterization of the nature of productivity payments compared with value-based performance payments and pure salaried wages would have been extremely useful to help interpret the likelihood that these payment models influenced the behavior of clinicians and perceptions of culture. In particular, how payment models define “productivity” and “quality” thresholds for achieving performance-based payments and the degree of control that physicians have on achieving them are critical determinants of the power of these incentives to influence clinician behavior and of clinicians’ perceptions of the degree to which their institution cultivates a high-value culture.¹⁴

Despite these limitations, this study raises a number of interesting hypotheses regarding the relationship between clinician payment models, incentive design, and clinical culture that warrant further investigation. For example, how do financial incentives designed to improve the value of inpatient care actually influence the practice patterns of hospitalists? Surprisingly little is known about this topic. Does the physician payment model design generally and implementation of targeted financial incentives for delivering higher value care in particular directly influence clinical culture? If so, how? Also, does the cultural effect actually undermine the goals of the financial incentive?

More broadly, systematic efforts to evaluate how clinical and hospital cultures impact the ability of financial incentives to motivate desired changes in clinicians’ behaviors will help healthcare leaders use financial incentives more effectively to motivate the delivery of higher quality, more cost-effective care. Increasing use and evaluation of different alternative payment models across hospitals nationwide represents an opportunity to characterize associations between different payment models and the delivery of high-quality, cost-effective care.¹⁵ Parallel efforts to characterize the clinical culture of these hospitals could help to better understand if and how hospital culture mediates this relationship. Moreover, because inpatient care is increasing and, in many hospitals, primarily provided by multidisciplinary teams, additional research is needed to understand how different payment models influence inpatient clinical team performance.

The connection between culture, financial incentives, and value-based care remains difficult to determine, but essential to clarify. Gupta et al. demonstrated that how a clinical system pays its physicians appears to be associated with physicians’ perceptions of how strongly the hospital’s culture emphasizes the delivery of high-value care. Work culture is a profound determinant of employee happiness, satisfaction, and productivity. The consistent delivery of high-value care is undoubtedly harder in clinical cultures that do not prize and support this end. Health system leaders focused on improving care value would be wise to pay close attention to their employees’ perceptions of their culture – and use these perceptions as one of several measures of their progress toward enabling their organization to deliver higher value care consistently.

Disclosures

Dr. Blumenthal is the Associate Chief Medical Officer of Devoted Health. Dr. Bergethon has nothing to disclose.

The United States spends approximately 18% of its gross domestic product on healthcare, nearly double the average expenditure by other high-income countries.¹ This increased financial investment does not consistently correlate with better care, as quality outcomes in the US rank well below many developed nations that spend far less on clinical care on a per capita basis.^1,2 These troubling and unsustainable spending trends have compelled national and regional policymakers, health system leaders, and researchers to search for ways to curb healthcare spending and improve healthcare value.

Approximately 32% of overall healthcare spending in the US occurs in hospitals,³ and there is broad acknowledgment that inpatient care can be delivered more cost effectively.⁴ In recent years, numerous policy interventions – including Medicare’s hospital readmission reductions program, hospital-acquired condition reductions program, hospital value-based purchasing program, and the Bundled Payment for Care Improvement program – have been implemented in an effort to improve the quality and costs of inpatient care.^4,5

These policies attempt to increase care value by utilizing innovative reimbursement techniques designed to hold clinical systems financially accountable for outcomes and spending. They are designed to move our system away from the traditional fee-for-service paradigm, which encourages overuse and has been identified as a major driver of bloated healthcare costs in the US.^6,7 The success of certain national payment reform pilots, such as the Comprehensive Care for Joint Replacement Model, indicate that payment models which hold clinicians and systems accountable hold promise for both reducing costs and improving outcomes.⁸

However, to influence clinical outcomes and costs, these national payment reforms must prompt local changes in how care is delivered and financed. Understanding systems- and clinician-level factors that enable the delivery of higher value care is, therefore, paramount for effectively translating national policies into local improvements in care value. Among hospitalists and hospital-based clinicians, institutional and clinical cultures represent an important lever for influencing physician practice patterns and, by extension, the quality and costs of care. Hospital and departmental cultures have been shown to influence physician behaviors profoundly in ways that improve quality and value, primarily via top-down initiatives focused on education and improving awareness. Examples of cultural success stories include efforts to reduce unnecessary utilization of diagnostic testing,⁹ improve adoption of hand-washing techniques on wards,¹⁰ and translate education about high-value care into sustained increases in the delivery of high-value clinical services.¹¹

In “The Association of Hospitals Productivity Payments and High-Value Care Culture,” Gupta et al. present the results of a study examining associations between how hospitals compensate their hospitalists – specifically the provision of performance-based incentives – and the strength of a hospital’s high-value care culture.¹² The authors administered the High-Value Care Culture Survey^TM (HVCCS), a validated survey instrument designed to assess the degree to which a hospital’s culture promotes the delivery of high-value care, to 255 hospitalists across 12 hospitals, including safety-net, community, and university-based hospitals. The hospitals’ predominant physician compensation models were grouped into three categories: salary model (no performance-based bonus), salary model with a productivity adjustment (ie, a bonus based on clinical volumes), and a salary model with a quality/value adjustment (ie, a bonus for delivering higher value care). The authors found that hospitalists who were salaried but also received productivity adjustments reported significantly lower mean HVCCS scores than salaried hospitalists who did not receive bonuses or adjustments. Compared with salaried hospitalists, hospitalists receiving compensation via salary plus value-based adjustments were nonsignificantly more likely to have higher HVCCS scores.

How are we to interpret these results? While we must be exceedingly careful about presuming causal mechanisms underlying these associations, they are nonetheless intriguing and should prompt further discussion about the relationship between payment incentives, provider behavior, and organizational culture. One potential explanation for these findings is that hospitals that rely on high clinical volumes to drive their financial performance may use productivity bonuses as a way to align hospitalists’ incentives with those of their institution, thereby promoting volume at the expense of value.

Behavioral economics theory provides an alternative lens through which to interpret the work of Gupta et al. The relationship between incentives and nonfinancial sources of personal motivation remain an important consideration in financial incentive design.¹³ A basic concept in behavioral economics is that there are two fundamental types of motivation of human behavior: extrinsic motivation, where people are motivated to act by the prospect of material rewards or punishments, and intrinsic motivation, a source of motivation that leads people to behave in ways that do not produce an obvious personal or material reward.¹³ Substantial evidence indicates that external rewards can have counterproductive effects on an individual’s intrinsic motivation, leading to a “crowding-out” effect that decreases the individual’s internal drive. When the “crowding-out” effect occurs, behaviors may be motivated by a desire to follow the rules, rather than true intrinsic drive. This change in the underlying forces motivating behavior can have a negative impact on self-esteem and result in a perceived loss of professional autonomy.^13,14 Perhaps more than any other professional group, healthcare professionals are fueled by intrinsic motivation and a yearning for professional autonomy. It is therefore plausible that doctors are particularly sensitive to, and disturbed by, the feeling that external rewards are “crowding out” this internal drive. Thus, the inverse association between productivity payments – volume-based rewards – and HVCCS scores may reflect this tension between intrinsic and extrinsic drives.

Of course, we need to interpret the authors’ findings cautiously in light of the cross-sectional study design and the potential for residual confounding. Indeed, the presence of an association between how hospitalists are compensated and their perceptions of the degree to which their institution’s culture promotes the delivery of high-value care does not prove that these two things are causally linked. Additionally, the small sample size limits the generalizability of these findings and efforts to draw robust conclusions from this work regarding the interplay between how a hospital pays its physicians, hospital culture, and the value of care delivered in this institution. Moreover, a more rigorous characterization of the nature of productivity payments compared with value-based performance payments and pure salaried wages would have been extremely useful to help interpret the likelihood that these payment models influenced the behavior of clinicians and perceptions of culture. In particular, how payment models define “productivity” and “quality” thresholds for achieving performance-based payments and the degree of control that physicians have on achieving them are critical determinants of the power of these incentives to influence clinician behavior and of clinicians’ perceptions of the degree to which their institution cultivates a high-value culture.¹⁴

Despite these limitations, this study raises a number of interesting hypotheses regarding the relationship between clinician payment models, incentive design, and clinical culture that warrant further investigation. For example, how do financial incentives designed to improve the value of inpatient care actually influence the practice patterns of hospitalists? Surprisingly little is known about this topic. Does the physician payment model design generally and implementation of targeted financial incentives for delivering higher value care in particular directly influence clinical culture? If so, how? Also, does the cultural effect actually undermine the goals of the financial incentive?

More broadly, systematic efforts to evaluate how clinical and hospital cultures impact the ability of financial incentives to motivate desired changes in clinicians’ behaviors will help healthcare leaders use financial incentives more effectively to motivate the delivery of higher quality, more cost-effective care. Increasing use and evaluation of different alternative payment models across hospitals nationwide represents an opportunity to characterize associations between different payment models and the delivery of high-quality, cost-effective care.¹⁵ Parallel efforts to characterize the clinical culture of these hospitals could help to better understand if and how hospital culture mediates this relationship. Moreover, because inpatient care is increasing and, in many hospitals, primarily provided by multidisciplinary teams, additional research is needed to understand how different payment models influence inpatient clinical team performance.

The connection between culture, financial incentives, and value-based care remains difficult to determine, but essential to clarify. Gupta et al. demonstrated that how a clinical system pays its physicians appears to be associated with physicians’ perceptions of how strongly the hospital’s culture emphasizes the delivery of high-value care. Work culture is a profound determinant of employee happiness, satisfaction, and productivity. The consistent delivery of high-value care is undoubtedly harder in clinical cultures that do not prize and support this end. Health system leaders focused on improving care value would be wise to pay close attention to their employees’ perceptions of their culture – and use these perceptions as one of several measures of their progress toward enabling their organization to deliver higher value care consistently.

Disclosures

Dr. Blumenthal is the Associate Chief Medical Officer of Devoted Health. Dr. Bergethon has nothing to disclose.

The United States spends approximately 18% of its gross domestic product on healthcare, nearly double the average expenditure by other high-income countries.¹ This increased financial investment does not consistently correlate with better care, as quality outcomes in the US rank well below many developed nations that spend far less on clinical care on a per capita basis.^1,2 These troubling and unsustainable spending trends have compelled national and regional policymakers, health system leaders, and researchers to search for ways to curb healthcare spending and improve healthcare value.

Approximately 32% of overall healthcare spending in the US occurs in hospitals,³ and there is broad acknowledgment that inpatient care can be delivered more cost effectively.⁴ In recent years, numerous policy interventions – including Medicare’s hospital readmission reductions program, hospital-acquired condition reductions program, hospital value-based purchasing program, and the Bundled Payment for Care Improvement program – have been implemented in an effort to improve the quality and costs of inpatient care.^4,5

These policies attempt to increase care value by utilizing innovative reimbursement techniques designed to hold clinical systems financially accountable for outcomes and spending. They are designed to move our system away from the traditional fee-for-service paradigm, which encourages overuse and has been identified as a major driver of bloated healthcare costs in the US.^6,7 The success of certain national payment reform pilots, such as the Comprehensive Care for Joint Replacement Model, indicate that payment models which hold clinicians and systems accountable hold promise for both reducing costs and improving outcomes.⁸

However, to influence clinical outcomes and costs, these national payment reforms must prompt local changes in how care is delivered and financed. Understanding systems- and clinician-level factors that enable the delivery of higher value care is, therefore, paramount for effectively translating national policies into local improvements in care value. Among hospitalists and hospital-based clinicians, institutional and clinical cultures represent an important lever for influencing physician practice patterns and, by extension, the quality and costs of care. Hospital and departmental cultures have been shown to influence physician behaviors profoundly in ways that improve quality and value, primarily via top-down initiatives focused on education and improving awareness. Examples of cultural success stories include efforts to reduce unnecessary utilization of diagnostic testing,⁹ improve adoption of hand-washing techniques on wards,¹⁰ and translate education about high-value care into sustained increases in the delivery of high-value clinical services.¹¹

In “The Association of Hospitals Productivity Payments and High-Value Care Culture,” Gupta et al. present the results of a study examining associations between how hospitals compensate their hospitalists – specifically the provision of performance-based incentives – and the strength of a hospital’s high-value care culture.¹² The authors administered the High-Value Care Culture Survey^TM (HVCCS), a validated survey instrument designed to assess the degree to which a hospital’s culture promotes the delivery of high-value care, to 255 hospitalists across 12 hospitals, including safety-net, community, and university-based hospitals. The hospitals’ predominant physician compensation models were grouped into three categories: salary model (no performance-based bonus), salary model with a productivity adjustment (ie, a bonus based on clinical volumes), and a salary model with a quality/value adjustment (ie, a bonus for delivering higher value care). The authors found that hospitalists who were salaried but also received productivity adjustments reported significantly lower mean HVCCS scores than salaried hospitalists who did not receive bonuses or adjustments. Compared with salaried hospitalists, hospitalists receiving compensation via salary plus value-based adjustments were nonsignificantly more likely to have higher HVCCS scores.

How are we to interpret these results? While we must be exceedingly careful about presuming causal mechanisms underlying these associations, they are nonetheless intriguing and should prompt further discussion about the relationship between payment incentives, provider behavior, and organizational culture. One potential explanation for these findings is that hospitals that rely on high clinical volumes to drive their financial performance may use productivity bonuses as a way to align hospitalists’ incentives with those of their institution, thereby promoting volume at the expense of value.

Behavioral economics theory provides an alternative lens through which to interpret the work of Gupta et al. The relationship between incentives and nonfinancial sources of personal motivation remain an important consideration in financial incentive design.¹³ A basic concept in behavioral economics is that there are two fundamental types of motivation of human behavior: extrinsic motivation, where people are motivated to act by the prospect of material rewards or punishments, and intrinsic motivation, a source of motivation that leads people to behave in ways that do not produce an obvious personal or material reward.¹³ Substantial evidence indicates that external rewards can have counterproductive effects on an individual’s intrinsic motivation, leading to a “crowding-out” effect that decreases the individual’s internal drive. When the “crowding-out” effect occurs, behaviors may be motivated by a desire to follow the rules, rather than true intrinsic drive. This change in the underlying forces motivating behavior can have a negative impact on self-esteem and result in a perceived loss of professional autonomy.^13,14 Perhaps more than any other professional group, healthcare professionals are fueled by intrinsic motivation and a yearning for professional autonomy. It is therefore plausible that doctors are particularly sensitive to, and disturbed by, the feeling that external rewards are “crowding out” this internal drive. Thus, the inverse association between productivity payments – volume-based rewards – and HVCCS scores may reflect this tension between intrinsic and extrinsic drives.

Of course, we need to interpret the authors’ findings cautiously in light of the cross-sectional study design and the potential for residual confounding. Indeed, the presence of an association between how hospitalists are compensated and their perceptions of the degree to which their institution’s culture promotes the delivery of high-value care does not prove that these two things are causally linked. Additionally, the small sample size limits the generalizability of these findings and efforts to draw robust conclusions from this work regarding the interplay between how a hospital pays its physicians, hospital culture, and the value of care delivered in this institution. Moreover, a more rigorous characterization of the nature of productivity payments compared with value-based performance payments and pure salaried wages would have been extremely useful to help interpret the likelihood that these payment models influenced the behavior of clinicians and perceptions of culture. In particular, how payment models define “productivity” and “quality” thresholds for achieving performance-based payments and the degree of control that physicians have on achieving them are critical determinants of the power of these incentives to influence clinician behavior and of clinicians’ perceptions of the degree to which their institution cultivates a high-value culture.¹⁴

Despite these limitations, this study raises a number of interesting hypotheses regarding the relationship between clinician payment models, incentive design, and clinical culture that warrant further investigation. For example, how do financial incentives designed to improve the value of inpatient care actually influence the practice patterns of hospitalists? Surprisingly little is known about this topic. Does the physician payment model design generally and implementation of targeted financial incentives for delivering higher value care in particular directly influence clinical culture? If so, how? Also, does the cultural effect actually undermine the goals of the financial incentive?

More broadly, systematic efforts to evaluate how clinical and hospital cultures impact the ability of financial incentives to motivate desired changes in clinicians’ behaviors will help healthcare leaders use financial incentives more effectively to motivate the delivery of higher quality, more cost-effective care. Increasing use and evaluation of different alternative payment models across hospitals nationwide represents an opportunity to characterize associations between different payment models and the delivery of high-quality, cost-effective care.¹⁵ Parallel efforts to characterize the clinical culture of these hospitals could help to better understand if and how hospital culture mediates this relationship. Moreover, because inpatient care is increasing and, in many hospitals, primarily provided by multidisciplinary teams, additional research is needed to understand how different payment models influence inpatient clinical team performance.

The connection between culture, financial incentives, and value-based care remains difficult to determine, but essential to clarify. Gupta et al. demonstrated that how a clinical system pays its physicians appears to be associated with physicians’ perceptions of how strongly the hospital’s culture emphasizes the delivery of high-value care. Work culture is a profound determinant of employee happiness, satisfaction, and productivity. The consistent delivery of high-value care is undoubtedly harder in clinical cultures that do not prize and support this end. Health system leaders focused on improving care value would be wise to pay close attention to their employees’ perceptions of their culture – and use these perceptions as one of several measures of their progress toward enabling their organization to deliver higher value care consistently.

Disclosures

Dr. Blumenthal is the Associate Chief Medical Officer of Devoted Health. Dr. Bergethon has nothing to disclose.

Hospitalists have become well versed in campaigns championing safe, efficient, and timely discharges, as well as in the pragmatic challenges of achieving them. Successfully discharging a patient from the hospital requires synchronizing several elements; as a result, improvement efforts focus on promoting shared mental models and team identification of early discharges. The urgency for timely discharges, much like (and unlike¹) hotel check-out times, becomes increasingly relevant when hospitals are functioning at or beyond full capacity. As inpatient medical care grows increasingly more specialized, promoting high-quality discharges theoretically allows for not only more beds, but also that the right bed is available for the right patient at the right time. In addition, financial realities in terms of reimbursement and the high cost of adding capacity imply that hospitals need to maximize throughput from the beds they already have. For these reasons, hospital administrators and operational leaders have focused on early discharges as a goal—and have often used discharge before noon (DCBN) as the metric to measure performance.

In this issue of the Journal of Hospital Medicine, Destino et al. reported that it is possible to achieve a higher percentage of early discharges, which allowed for decompression of post-anesthesia care and emergency areas without a measurable negative impact on patient or family satisfaction or length of stay (LOS).² The improvement they report is remarkable. However, it will be important for them to report back, as quality improvement projects often revert to prior state unless the processes are reinforced and embedded in hospital culture. In addition, what goes unreported in Destino et al. are the unmeasured and unanticipated outcomes related to focusing on a single, laudable goal. This study and others have yet to confirm that systems have enough resiliency to improve discharge timeliness without diverting resources from other aspects of care.³ In other words, can inpatient teams do everything at the same time without sacrificing quality; ie, improve discharge timeliness, accept and admit new patients faster, respond to deteriorating patients, spend enough time with patients and families to meet their needs (and validated survey expectations), and in educational settings, meet the learning needs of trainees?⁴ This may prove to be true if implementation techniques are individualized to hospitals, services, and units and are incorporated into existing workflows, minimizing extraneous “asks” on already overtaxed providers. Evidence to support this would go a long way in engaging stakeholders to prioritize quality discharges.

In this issue, too, James, et al. ask the question “if DCBN is a good indicator of shorter LOS or is DCBN an arbitrary indicator.”⁵ The answer may be yes, no, both, maybe, and it depends. Certainly, no pathophysiological reasons exist for a certain time of day to be the “right” time for discharge. The key question for hospitalists and health systems leaders is whether setting time goals leads clinicians to delay discharges of medically and logistically ready patients in the afternoon or evening, particularly if the metric is linked to monetary performance incentives. This is also likely a matter of degrees, ie, set the DCBN goal at 80%-100% and gaming is much more likely; set the goal at 20%-30% and this might reflect a realistic range and be less likely to incentivize gaming. Notably, the hospital in the James study did not have a DCBN goal. It would be interesting to see what would happen in that hospital or another hospital before and after implementing a DCBN goal—and further assess a dose-response curve. Another approach would be to perform qualitative analysis of readiness for discharge via chart reviews and determine if patients could have left in the afternoon or evening but might have been delayed to buff up the performance on the DCBN metric.

James et al. additionally demonstrate differences for medical and surgical patients, underscoring that a DCBN goal is unlikely to yield the same results in different patient cohorts or settings. The authors note several workflow reasons for this variation, but other considerations are regularity of timelines for recovery being different for surgical patients, role of elective admissions scheduled in advance, and the potential use of conditional orders (ie, orders entered before dawn that nurses can activate as patients meet criteria).

What both studies highlight is that although morning discharges can help with patient flow, hospitalists and hospital leaders need to be mindful and seek more information before implementing DCBN programs. One strategy that can promote efficient discharge regardless of the position of the sun in the sky, account for variation in patient populations and individual patients, and mitigate the potential for gaming the system is to strive toward measuring time from medical readiness to the time of discharge. Although some institutions have had success with this work,⁶ it remains challenging to implement this across all patient populations. Criteria for medical readiness need to be agreed upon and validated, and then a real-time way of identifying when criteria are met needs to be developed. In this regard, hospitals may have to invest individually or collectively to build such systems, but the benefit would be to enable and promote performance of timely discharge for all patients at all times of day.

Much as we have adopted cultural changes over the years to raise awareness regarding patient safety such as nosocomial infections and hand hygiene, an emphasis on high-quality discharges too needs to become integral to hospital practices to sustain performance and any associated metrics. As to what to measure? A validated “medical readiness to discharge” may be the gold standard but may be difficult to attain. Until then, carefully constructed approaches to prioritizing early discharges through proactive planning, shared mental models, interdisciplinary teamwork, and appropriate incentives to those who do it well could yield the results we want as hospitalists, as patients, and as families.

Disclosures

Dr. Kane and Dr. Fieldston have nothing to disclose.

Hospitalists have become well versed in campaigns championing safe, efficient, and timely discharges, as well as in the pragmatic challenges of achieving them. Successfully discharging a patient from the hospital requires synchronizing several elements; as a result, improvement efforts focus on promoting shared mental models and team identification of early discharges. The urgency for timely discharges, much like (and unlike¹) hotel check-out times, becomes increasingly relevant when hospitals are functioning at or beyond full capacity. As inpatient medical care grows increasingly more specialized, promoting high-quality discharges theoretically allows for not only more beds, but also that the right bed is available for the right patient at the right time. In addition, financial realities in terms of reimbursement and the high cost of adding capacity imply that hospitals need to maximize throughput from the beds they already have. For these reasons, hospital administrators and operational leaders have focused on early discharges as a goal—and have often used discharge before noon (DCBN) as the metric to measure performance.

In this issue of the Journal of Hospital Medicine, Destino et al. reported that it is possible to achieve a higher percentage of early discharges, which allowed for decompression of post-anesthesia care and emergency areas without a measurable negative impact on patient or family satisfaction or length of stay (LOS).² The improvement they report is remarkable. However, it will be important for them to report back, as quality improvement projects often revert to prior state unless the processes are reinforced and embedded in hospital culture. In addition, what goes unreported in Destino et al. are the unmeasured and unanticipated outcomes related to focusing on a single, laudable goal. This study and others have yet to confirm that systems have enough resiliency to improve discharge timeliness without diverting resources from other aspects of care.³ In other words, can inpatient teams do everything at the same time without sacrificing quality; ie, improve discharge timeliness, accept and admit new patients faster, respond to deteriorating patients, spend enough time with patients and families to meet their needs (and validated survey expectations), and in educational settings, meet the learning needs of trainees?⁴ This may prove to be true if implementation techniques are individualized to hospitals, services, and units and are incorporated into existing workflows, minimizing extraneous “asks” on already overtaxed providers. Evidence to support this would go a long way in engaging stakeholders to prioritize quality discharges.

In this issue, too, James, et al. ask the question “if DCBN is a good indicator of shorter LOS or is DCBN an arbitrary indicator.”⁵ The answer may be yes, no, both, maybe, and it depends. Certainly, no pathophysiological reasons exist for a certain time of day to be the “right” time for discharge. The key question for hospitalists and health systems leaders is whether setting time goals leads clinicians to delay discharges of medically and logistically ready patients in the afternoon or evening, particularly if the metric is linked to monetary performance incentives. This is also likely a matter of degrees, ie, set the DCBN goal at 80%-100% and gaming is much more likely; set the goal at 20%-30% and this might reflect a realistic range and be less likely to incentivize gaming. Notably, the hospital in the James study did not have a DCBN goal. It would be interesting to see what would happen in that hospital or another hospital before and after implementing a DCBN goal—and further assess a dose-response curve. Another approach would be to perform qualitative analysis of readiness for discharge via chart reviews and determine if patients could have left in the afternoon or evening but might have been delayed to buff up the performance on the DCBN metric.

James et al. additionally demonstrate differences for medical and surgical patients, underscoring that a DCBN goal is unlikely to yield the same results in different patient cohorts or settings. The authors note several workflow reasons for this variation, but other considerations are regularity of timelines for recovery being different for surgical patients, role of elective admissions scheduled in advance, and the potential use of conditional orders (ie, orders entered before dawn that nurses can activate as patients meet criteria).

What both studies highlight is that although morning discharges can help with patient flow, hospitalists and hospital leaders need to be mindful and seek more information before implementing DCBN programs. One strategy that can promote efficient discharge regardless of the position of the sun in the sky, account for variation in patient populations and individual patients, and mitigate the potential for gaming the system is to strive toward measuring time from medical readiness to the time of discharge. Although some institutions have had success with this work,⁶ it remains challenging to implement this across all patient populations. Criteria for medical readiness need to be agreed upon and validated, and then a real-time way of identifying when criteria are met needs to be developed. In this regard, hospitals may have to invest individually or collectively to build such systems, but the benefit would be to enable and promote performance of timely discharge for all patients at all times of day.

Much as we have adopted cultural changes over the years to raise awareness regarding patient safety such as nosocomial infections and hand hygiene, an emphasis on high-quality discharges too needs to become integral to hospital practices to sustain performance and any associated metrics. As to what to measure? A validated “medical readiness to discharge” may be the gold standard but may be difficult to attain. Until then, carefully constructed approaches to prioritizing early discharges through proactive planning, shared mental models, interdisciplinary teamwork, and appropriate incentives to those who do it well could yield the results we want as hospitalists, as patients, and as families.

Disclosures

Dr. Kane and Dr. Fieldston have nothing to disclose.

Hospitalists have become well versed in campaigns championing safe, efficient, and timely discharges, as well as in the pragmatic challenges of achieving them. Successfully discharging a patient from the hospital requires synchronizing several elements; as a result, improvement efforts focus on promoting shared mental models and team identification of early discharges. The urgency for timely discharges, much like (and unlike¹) hotel check-out times, becomes increasingly relevant when hospitals are functioning at or beyond full capacity. As inpatient medical care grows increasingly more specialized, promoting high-quality discharges theoretically allows for not only more beds, but also that the right bed is available for the right patient at the right time. In addition, financial realities in terms of reimbursement and the high cost of adding capacity imply that hospitals need to maximize throughput from the beds they already have. For these reasons, hospital administrators and operational leaders have focused on early discharges as a goal—and have often used discharge before noon (DCBN) as the metric to measure performance.

In this issue of the Journal of Hospital Medicine, Destino et al. reported that it is possible to achieve a higher percentage of early discharges, which allowed for decompression of post-anesthesia care and emergency areas without a measurable negative impact on patient or family satisfaction or length of stay (LOS).² The improvement they report is remarkable. However, it will be important for them to report back, as quality improvement projects often revert to prior state unless the processes are reinforced and embedded in hospital culture. In addition, what goes unreported in Destino et al. are the unmeasured and unanticipated outcomes related to focusing on a single, laudable goal. This study and others have yet to confirm that systems have enough resiliency to improve discharge timeliness without diverting resources from other aspects of care.³ In other words, can inpatient teams do everything at the same time without sacrificing quality; ie, improve discharge timeliness, accept and admit new patients faster, respond to deteriorating patients, spend enough time with patients and families to meet their needs (and validated survey expectations), and in educational settings, meet the learning needs of trainees?⁴ This may prove to be true if implementation techniques are individualized to hospitals, services, and units and are incorporated into existing workflows, minimizing extraneous “asks” on already overtaxed providers. Evidence to support this would go a long way in engaging stakeholders to prioritize quality discharges.

In this issue, too, James, et al. ask the question “if DCBN is a good indicator of shorter LOS or is DCBN an arbitrary indicator.”⁵ The answer may be yes, no, both, maybe, and it depends. Certainly, no pathophysiological reasons exist for a certain time of day to be the “right” time for discharge. The key question for hospitalists and health systems leaders is whether setting time goals leads clinicians to delay discharges of medically and logistically ready patients in the afternoon or evening, particularly if the metric is linked to monetary performance incentives. This is also likely a matter of degrees, ie, set the DCBN goal at 80%-100% and gaming is much more likely; set the goal at 20%-30% and this might reflect a realistic range and be less likely to incentivize gaming. Notably, the hospital in the James study did not have a DCBN goal. It would be interesting to see what would happen in that hospital or another hospital before and after implementing a DCBN goal—and further assess a dose-response curve. Another approach would be to perform qualitative analysis of readiness for discharge via chart reviews and determine if patients could have left in the afternoon or evening but might have been delayed to buff up the performance on the DCBN metric.

James et al. additionally demonstrate differences for medical and surgical patients, underscoring that a DCBN goal is unlikely to yield the same results in different patient cohorts or settings. The authors note several workflow reasons for this variation, but other considerations are regularity of timelines for recovery being different for surgical patients, role of elective admissions scheduled in advance, and the potential use of conditional orders (ie, orders entered before dawn that nurses can activate as patients meet criteria).

What both studies highlight is that although morning discharges can help with patient flow, hospitalists and hospital leaders need to be mindful and seek more information before implementing DCBN programs. One strategy that can promote efficient discharge regardless of the position of the sun in the sky, account for variation in patient populations and individual patients, and mitigate the potential for gaming the system is to strive toward measuring time from medical readiness to the time of discharge. Although some institutions have had success with this work,⁶ it remains challenging to implement this across all patient populations. Criteria for medical readiness need to be agreed upon and validated, and then a real-time way of identifying when criteria are met needs to be developed. In this regard, hospitals may have to invest individually or collectively to build such systems, but the benefit would be to enable and promote performance of timely discharge for all patients at all times of day.

Much as we have adopted cultural changes over the years to raise awareness regarding patient safety such as nosocomial infections and hand hygiene, an emphasis on high-quality discharges too needs to become integral to hospital practices to sustain performance and any associated metrics. As to what to measure? A validated “medical readiness to discharge” may be the gold standard but may be difficult to attain. Until then, carefully constructed approaches to prioritizing early discharges through proactive planning, shared mental models, interdisciplinary teamwork, and appropriate incentives to those who do it well could yield the results we want as hospitalists, as patients, and as families.

Disclosures

Dr. Kane and Dr. Fieldston have nothing to disclose.

Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.¹ Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.

The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.^2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.^4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.⁶ For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.⁷ Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.⁸ Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.

One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.⁹ Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.^10-13

A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.¹⁴ This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.¹⁵ Additionally, coordination of care may become more challenging for discharge planning.¹⁶ Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.^15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers. We identified no studies in the literature that applied simulation modeling to general medicine inpatients to evaluate the impact of these different decisions.

This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible strategic decisions around the care of general medicine inpatients. Through the application of systems engineering techniques, we modeled four future states that illustrate the following: (1) the complexities of a large health delivery system, (2) the intended and unintended consequences of implementing different changes in the process of care delivery, and (3) how the simulation modeling might be used to inform decision making.

Setting and Present State

Virginia Commonwealth University (VCU) is a 865-bed tertiary academic medical center, with inpatient care activities spread between four connected buildings and 50 different NUs. The occupancy rate had been over 92% during the time period of this project with admission volume limited primarily by the capacity of the facility. Three of the NUs were primarily allocated to general medicine (GIM) patients. However, over the years, GIM inpatients grew to over 7500 admissions annually, resulting in nearly 50% of GIM patients being admitted to a non-GIM nursing unit.

Additionally, patients on each medical team had a high degree of spread across NUs due to several factors. Admissions and discharges from the hospital did not align across the day. While discharges clumped in the late afternoon, admission occurred throughout the day with a surge in the later afternoon. This mismatch frequently led to patients waiting in the ED for a bed, medical team, or both, and patients were typically assigned to the first available bed and team. For medical team assignments, newly admitted patients were distributed relatively equally across five hospitalist teams and five housestaff teams (that include residents, interns, and medical students). This steady distribution of patients through the day supported meeting housestaff work-hour restrictions of 80 hours each week.¹⁸ Yet, as a result of the high occupancy rate, the patterns of patient admissions and discharges, and the distribution of patients among medical teams and across NUs, medical teams and NUs rarely shared more than a few patients.

Leaders at our institution outlined several possible options to address these challenges, including aligning medical teams with NU, adding an additional hospitalist team, or adding an additional nursing unit. In addition, institutional leaders were concerned about the impact of continued growth in admission volume and the impact of patient dispersion on trainees and students. The overall goal of creating a simulation model was to determine the impact of an increased volume of patients and these possible strategic decisions on operational metrics, including number of patients waiting in the ED, ED boarding time per patient, time in system per patient (ED boarding time plus inpatient LOS), team utilization, and rounding travel time.

Simulation Modeling

To model the impact of some possible system changes on patient care, we applied Kelton and Law’s simulation study framework,¹⁹ including data collection; model building and validation; and what-if scenario testing (Figure 1).

Data Collection

Process Flow Map

We created a complex process flow map of patient care activities on medical teams. The map was developed by four general medicine physicians (R.C., H.M., V.M., and S.P.T.) who all provided medical care on the hospital-based services and ensured expert input on the patient care activities captured by the simulation modeling.

Time and Motion Studies

Time and motion study is a well-established technique used to evaluate the efficiency of work processes.^20,21 Originally applied to increase productivity in manufacturing, this technique uses first-hand observations to measure the time allotted to different work tasks to systematically analyze workflow.²² Workflow in healthcare, like manufacturing tasks, tends to have a repetitive pattern, making time and motion studies a highly applicable tool.

A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,²³ began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.

Hospital Data

We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.

Model Building and Internal Validation

On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.

What-If Scenario Testing

We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:

• constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
• increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
• increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
• modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).

For Future States 1-3, admission volume was held constant. The model generated nursing unit LOS using a random continuous exponential probability distribution with a mean of 133 hours to match the LOS distribution derived from health system data. As patients entered the system for admission, the model assigned a bed to the patient, but the patient could not move to the assigned bed until a bed and care team were both available. We were only interested in the steady-state behavior of the system, so collecting performance statistics only after the model had been populated and steady state had been achieved was important.

Table 1 summarizes the input data, fixed, and dynamic variable for each future state model.

Time and Motion Data

The mean time spent with each patient was nine minutes. The mean time traveling between NUs Healthcare Quality for Children and Adolescents with Suicidality Admitted to Acute Care Hospitals in the United States was five minutes. Average rounding time was noted to be two hours, 53 minutes. Thirty-seven minutes, about ~21% of the time, was wasted in traveling. Each team, on average, traveled to seven different NUs to round on their daily census, averaging 1.6 patients in each nursing unit.

Hospital Data

Between January 1, 2011 to December 31, 2013, a total of 7,902 patients were admitted to the general medicine teams, spanning 23 NU. The average number of admissions per day was 21.6, and the average nursing unit LOS was 133 hours. Average team census was derived from historical data across all GIM team for 2013 and was noted to be 11.5 patients per team, and these patients were spread over seven NU.

Model Validation

The mean number of patients admitted to different NUs was estimated from the simulation model then compared with the EHR data from 2013. None were statistically different (P > .05), which signified that the validated simulation model is similar to the EHR data from 2013 despite the underlying assumptions.

Model Outputs

Analysis of the models indicated that steady-state (based upon hospital census) was realized at approximately 800 hours or after 680 patients were admitted to the GIM teams. Statistics collection, therefore, was started after 800 hours of simulated time and reflected the admission of the remaining 7222 patients in the model validation sample (Table 2).

In the model, the total daily general medicine patient census was 119.26. Average time in the system per patient was noted to be 147.37 hours, which was 14.37 hours more than the average nursing unit LOS of 133 hours. Average number of patients waiting for a bed was noted to be 11.31, while the average wait time for a patient to get a bed was 12.39 hours.

Average housestaff team and hospitalist team utilization were 76.06% and 73.02%, respectively, with average team utilization of 74.54% (range: 72.88%-76.19%). Housestaff team and hospitalist team averaged 12.17 and 11.68 patients per care team, respectively. General medicine teams had patients on 7.30 NUs on average. GIM teams rounding travel time was 36.5 minutes.

What-If Scenario Testing

Simulation outputs for the four future states are summarized in Table 2. With Future State 1, through which patients were selectively assigned to housestaff teams aligned with three NUs, the average time in the system per patient increased by 2.35 hours, with 1.87 more patients waiting for a bed and waiting for 2.03 more hours as compared with the present state. A marked disparity was observed in hospitalist and housestaff team utilization of 62.22% and 86.55% respectively. Patient dispersion to various NUs significantly decreased, and rounding time correspondingly decreased by approximately 41%.

Future State 2, adding a nursing unit, decreased average time in the system per patient by 9.86 hours, with 9.32 fewer patients waiting for a bed as compared with the present state. A slight increase in patient dispersion and rounding time was observed. Overall, patients spent 137.51 hours in the system, which demonstrated improved efficiency of the system.

Future State 3, adding an additional medical team, interestingly did not have a significant effect on patients’ average time in system or the number of patients waiting for a bed even though a decrease occurred in average team census, team utilization, and patient dispersion.

Finally, Future State 4, increasing admissions while also adding a nursing unit and a hospitalist team, resulted in an increase in admission volume while maintaining similar utilization rates for teams and NU. Patients spent about 2.48 hours less in the system, while only 9.94 patients were noted to be waiting for a bed as compared with 11.21 patients in the present state model. The total daily general medicine patient census was noted to be 137.19. Average team census and average team utilization were noted to be similar to those of the present state model, while admissions were up by approximately 1,080 per year. Both patient dispersion and rounding were slightly worsened.

Sensitivity Analysis

Overall, average time in system was most affected by the number of patient arrivals. This became particularly significant as the volume of patient arrivals approached and exceeded the capacity of the rounding teams. Adding a nursing unit had more impact on decreasing average time in the system than adding a medical team or aligning teams with NUs under the conditions defined by the model. However, under different conditions, such as increasing admission volume, the relative benefit of different approaches may vary.

Given that hospitals are large, complex systems,² the impact of system-level changes can have unpredictable and potentially deleterious effects. Simulation provides a technique for modeling the impact of changes to understand the ramifications of these interventions more thoroughly.³ In this study, we describe the process of building a simulation model for the admission and discharge of patients from general medicine services in a tertiary care hospital, internally validating this model, and examining the outcomes from several potential changes to the system.

The outcomes for these what-if scenarios provided some important insights about the secondary effect of system changes and the need for multiple, simultaneous interventions. Given that hospitals often function at near capacity, adding a hospitalist team or nursing unit might be seen as a reasonable strategy to improve the system metrics, number of patient discharges, or average LOS. On the basis of our analysis, adding a nursing unit would have more benefit than adding a hospitalist team. Leaders who want to increase capacity may need to consider both adding a hospitalist team and a nursing unit, and model the impact of each choice as described with a simulation.

Additionally, assigning patients to medical teams aligned with NUs seems theoretically appealing to improve interprofessional communication and decrease the time spent in transit between patients by physicians. While our findings supported a decrease in rounding time and patient dispersion, the teams not aligned with a nursing unit (ie, the hospitalists) exceeded 80% utilization, the threshold at which efficiency is known to decrease.²⁴ Potentially, benefits resulting from teams being aligned with NUs were offset by decrements in performance of the teams not aligned with NU. If medical teams and NUs become aligned, then a higher number of teams may be necessary to maintain patient throughput.

Simulation models identify these unexpected consequences prior to investing resources in a significant change; however, modeling is not simple. Simulation models depend on the characteristics of the model and the quality of the input data. For example, we used an expert approach to map physician workflow as an underpinning of the model, but we may have missed an important variation in physician workflow. Understanding this variation could strengthen the model and provide some testable variables for future study. Likewise, understanding nursing workflow and how variation in physician workflow shapes nursing workflow, and vice versa, is worth exploring.

Other data could also be added to, and help interpret, the outputs of this model. For example, the impact of various levels of team and unit utilization on diversion time for the hospital ED may help determine whether adding team capacity or unit capacity is more beneficial for the system. Likewise, aligning medical teams with NUs seems to hinder patient throughput on this analysis, but benefits in patient satisfaction or decreased readmissions might improve reimbursement and outweigh the revenue lost from throughput. Underpinning each of these types of decisions is a need to model the system well and thoughtfully choose the inputs, processes, and outputs. Pursuing a new strategic decision usually involves cost; simulation modeling provides data to help leaders weigh the benefits in terms of the needed investment.

The major limitations of the study stem from these choices. Our study focused on matching capacity and demand while limiting other changes in the system, such as changes in nursing unit LOS. Future work to quantify the relationship of other variables on parameters, such as the impact of decreased team dispersion on LOS, early discharges, and decreasing care variation, would make future models more robust. This model does not consider other strategies to improve patient flow, such as shaping demand, adaptive team assignment algorithms, or creating surge capacity. We also used only hospitalist time and motion data in our model; housestaff workflow is likely different. In addition, we modeled all patients as having a general level of nursing care and did not account for admissions or transfers to intensive care units or other services. These parameters could be added in future iterations. Finally, the biggest limitation in any simulation is the underlying assumptions made to construct the model. While we validated the model retrospectively, prospective validation and refinement should also be performed with attention to how the model functions under extreme conditions, such as a very high patient load.

Major system changes are expensive and must be made carefully. Systems engineering techniques, such as DES, provide techniques to estimate the impact of changes on pertinent care delivery variables. Results from this study underscore the complexity of patient care delivery and how simulation models can integrate multiple system components to provide a data-driven approach to inform decision making in a complex system.

Acknowledgments

The simulation software used in this study was awarded as an educational software grant from SIMIO®. We would like to acknowledge support from the Department of Internal Medicine at Virginia Commonwealth University for this project and thank Lena Rivera for her assistance with the manuscript preparation.

Dislosures

Dr. Heim recived a consulting fee for programming guidance from Virginia Commonwealth University. All other authors have nothing to disclose.

Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.¹ Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.

The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.^2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.^4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.⁶ For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.⁷ Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.⁸ Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.

One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.⁹ Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.^10-13

A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.¹⁴ This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.¹⁵ Additionally, coordination of care may become more challenging for discharge planning.¹⁶ Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.^15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers. We identified no studies in the literature that applied simulation modeling to general medicine inpatients to evaluate the impact of these different decisions.

This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible strategic decisions around the care of general medicine inpatients. Through the application of systems engineering techniques, we modeled four future states that illustrate the following: (1) the complexities of a large health delivery system, (2) the intended and unintended consequences of implementing different changes in the process of care delivery, and (3) how the simulation modeling might be used to inform decision making.

Setting and Present State

Virginia Commonwealth University (VCU) is a 865-bed tertiary academic medical center, with inpatient care activities spread between four connected buildings and 50 different NUs. The occupancy rate had been over 92% during the time period of this project with admission volume limited primarily by the capacity of the facility. Three of the NUs were primarily allocated to general medicine (GIM) patients. However, over the years, GIM inpatients grew to over 7500 admissions annually, resulting in nearly 50% of GIM patients being admitted to a non-GIM nursing unit.

Additionally, patients on each medical team had a high degree of spread across NUs due to several factors. Admissions and discharges from the hospital did not align across the day. While discharges clumped in the late afternoon, admission occurred throughout the day with a surge in the later afternoon. This mismatch frequently led to patients waiting in the ED for a bed, medical team, or both, and patients were typically assigned to the first available bed and team. For medical team assignments, newly admitted patients were distributed relatively equally across five hospitalist teams and five housestaff teams (that include residents, interns, and medical students). This steady distribution of patients through the day supported meeting housestaff work-hour restrictions of 80 hours each week.¹⁸ Yet, as a result of the high occupancy rate, the patterns of patient admissions and discharges, and the distribution of patients among medical teams and across NUs, medical teams and NUs rarely shared more than a few patients.

Leaders at our institution outlined several possible options to address these challenges, including aligning medical teams with NU, adding an additional hospitalist team, or adding an additional nursing unit. In addition, institutional leaders were concerned about the impact of continued growth in admission volume and the impact of patient dispersion on trainees and students. The overall goal of creating a simulation model was to determine the impact of an increased volume of patients and these possible strategic decisions on operational metrics, including number of patients waiting in the ED, ED boarding time per patient, time in system per patient (ED boarding time plus inpatient LOS), team utilization, and rounding travel time.

Simulation Modeling

To model the impact of some possible system changes on patient care, we applied Kelton and Law’s simulation study framework,¹⁹ including data collection; model building and validation; and what-if scenario testing (Figure 1).

Data Collection

Process Flow Map

We created a complex process flow map of patient care activities on medical teams. The map was developed by four general medicine physicians (R.C., H.M., V.M., and S.P.T.) who all provided medical care on the hospital-based services and ensured expert input on the patient care activities captured by the simulation modeling.

Time and Motion Studies

Time and motion study is a well-established technique used to evaluate the efficiency of work processes.^20,21 Originally applied to increase productivity in manufacturing, this technique uses first-hand observations to measure the time allotted to different work tasks to systematically analyze workflow.²² Workflow in healthcare, like manufacturing tasks, tends to have a repetitive pattern, making time and motion studies a highly applicable tool.

A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,²³ began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.

Hospital Data

We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.

Model Building and Internal Validation

On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.

What-If Scenario Testing

We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:

• constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
• increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
• increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
• modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).

For Future States 1-3, admission volume was held constant. The model generated nursing unit LOS using a random continuous exponential probability distribution with a mean of 133 hours to match the LOS distribution derived from health system data. As patients entered the system for admission, the model assigned a bed to the patient, but the patient could not move to the assigned bed until a bed and care team were both available. We were only interested in the steady-state behavior of the system, so collecting performance statistics only after the model had been populated and steady state had been achieved was important.

Table 1 summarizes the input data, fixed, and dynamic variable for each future state model.

Time and Motion Data

The mean time spent with each patient was nine minutes. The mean time traveling between NUs Healthcare Quality for Children and Adolescents with Suicidality Admitted to Acute Care Hospitals in the United States was five minutes. Average rounding time was noted to be two hours, 53 minutes. Thirty-seven minutes, about ~21% of the time, was wasted in traveling. Each team, on average, traveled to seven different NUs to round on their daily census, averaging 1.6 patients in each nursing unit.

Hospital Data

Between January 1, 2011 to December 31, 2013, a total of 7,902 patients were admitted to the general medicine teams, spanning 23 NU. The average number of admissions per day was 21.6, and the average nursing unit LOS was 133 hours. Average team census was derived from historical data across all GIM team for 2013 and was noted to be 11.5 patients per team, and these patients were spread over seven NU.

Model Validation

The mean number of patients admitted to different NUs was estimated from the simulation model then compared with the EHR data from 2013. None were statistically different (P > .05), which signified that the validated simulation model is similar to the EHR data from 2013 despite the underlying assumptions.

Model Outputs

Analysis of the models indicated that steady-state (based upon hospital census) was realized at approximately 800 hours or after 680 patients were admitted to the GIM teams. Statistics collection, therefore, was started after 800 hours of simulated time and reflected the admission of the remaining 7222 patients in the model validation sample (Table 2).

In the model, the total daily general medicine patient census was 119.26. Average time in the system per patient was noted to be 147.37 hours, which was 14.37 hours more than the average nursing unit LOS of 133 hours. Average number of patients waiting for a bed was noted to be 11.31, while the average wait time for a patient to get a bed was 12.39 hours.

Average housestaff team and hospitalist team utilization were 76.06% and 73.02%, respectively, with average team utilization of 74.54% (range: 72.88%-76.19%). Housestaff team and hospitalist team averaged 12.17 and 11.68 patients per care team, respectively. General medicine teams had patients on 7.30 NUs on average. GIM teams rounding travel time was 36.5 minutes.

What-If Scenario Testing

Simulation outputs for the four future states are summarized in Table 2. With Future State 1, through which patients were selectively assigned to housestaff teams aligned with three NUs, the average time in the system per patient increased by 2.35 hours, with 1.87 more patients waiting for a bed and waiting for 2.03 more hours as compared with the present state. A marked disparity was observed in hospitalist and housestaff team utilization of 62.22% and 86.55% respectively. Patient dispersion to various NUs significantly decreased, and rounding time correspondingly decreased by approximately 41%.

Future State 2, adding a nursing unit, decreased average time in the system per patient by 9.86 hours, with 9.32 fewer patients waiting for a bed as compared with the present state. A slight increase in patient dispersion and rounding time was observed. Overall, patients spent 137.51 hours in the system, which demonstrated improved efficiency of the system.

Future State 3, adding an additional medical team, interestingly did not have a significant effect on patients’ average time in system or the number of patients waiting for a bed even though a decrease occurred in average team census, team utilization, and patient dispersion.

Finally, Future State 4, increasing admissions while also adding a nursing unit and a hospitalist team, resulted in an increase in admission volume while maintaining similar utilization rates for teams and NU. Patients spent about 2.48 hours less in the system, while only 9.94 patients were noted to be waiting for a bed as compared with 11.21 patients in the present state model. The total daily general medicine patient census was noted to be 137.19. Average team census and average team utilization were noted to be similar to those of the present state model, while admissions were up by approximately 1,080 per year. Both patient dispersion and rounding were slightly worsened.

Sensitivity Analysis

Overall, average time in system was most affected by the number of patient arrivals. This became particularly significant as the volume of patient arrivals approached and exceeded the capacity of the rounding teams. Adding a nursing unit had more impact on decreasing average time in the system than adding a medical team or aligning teams with NUs under the conditions defined by the model. However, under different conditions, such as increasing admission volume, the relative benefit of different approaches may vary.

Given that hospitals are large, complex systems,² the impact of system-level changes can have unpredictable and potentially deleterious effects. Simulation provides a technique for modeling the impact of changes to understand the ramifications of these interventions more thoroughly.³ In this study, we describe the process of building a simulation model for the admission and discharge of patients from general medicine services in a tertiary care hospital, internally validating this model, and examining the outcomes from several potential changes to the system.

The outcomes for these what-if scenarios provided some important insights about the secondary effect of system changes and the need for multiple, simultaneous interventions. Given that hospitals often function at near capacity, adding a hospitalist team or nursing unit might be seen as a reasonable strategy to improve the system metrics, number of patient discharges, or average LOS. On the basis of our analysis, adding a nursing unit would have more benefit than adding a hospitalist team. Leaders who want to increase capacity may need to consider both adding a hospitalist team and a nursing unit, and model the impact of each choice as described with a simulation.

Additionally, assigning patients to medical teams aligned with NUs seems theoretically appealing to improve interprofessional communication and decrease the time spent in transit between patients by physicians. While our findings supported a decrease in rounding time and patient dispersion, the teams not aligned with a nursing unit (ie, the hospitalists) exceeded 80% utilization, the threshold at which efficiency is known to decrease.²⁴ Potentially, benefits resulting from teams being aligned with NUs were offset by decrements in performance of the teams not aligned with NU. If medical teams and NUs become aligned, then a higher number of teams may be necessary to maintain patient throughput.

Simulation models identify these unexpected consequences prior to investing resources in a significant change; however, modeling is not simple. Simulation models depend on the characteristics of the model and the quality of the input data. For example, we used an expert approach to map physician workflow as an underpinning of the model, but we may have missed an important variation in physician workflow. Understanding this variation could strengthen the model and provide some testable variables for future study. Likewise, understanding nursing workflow and how variation in physician workflow shapes nursing workflow, and vice versa, is worth exploring.

Other data could also be added to, and help interpret, the outputs of this model. For example, the impact of various levels of team and unit utilization on diversion time for the hospital ED may help determine whether adding team capacity or unit capacity is more beneficial for the system. Likewise, aligning medical teams with NUs seems to hinder patient throughput on this analysis, but benefits in patient satisfaction or decreased readmissions might improve reimbursement and outweigh the revenue lost from throughput. Underpinning each of these types of decisions is a need to model the system well and thoughtfully choose the inputs, processes, and outputs. Pursuing a new strategic decision usually involves cost; simulation modeling provides data to help leaders weigh the benefits in terms of the needed investment.

The major limitations of the study stem from these choices. Our study focused on matching capacity and demand while limiting other changes in the system, such as changes in nursing unit LOS. Future work to quantify the relationship of other variables on parameters, such as the impact of decreased team dispersion on LOS, early discharges, and decreasing care variation, would make future models more robust. This model does not consider other strategies to improve patient flow, such as shaping demand, adaptive team assignment algorithms, or creating surge capacity. We also used only hospitalist time and motion data in our model; housestaff workflow is likely different. In addition, we modeled all patients as having a general level of nursing care and did not account for admissions or transfers to intensive care units or other services. These parameters could be added in future iterations. Finally, the biggest limitation in any simulation is the underlying assumptions made to construct the model. While we validated the model retrospectively, prospective validation and refinement should also be performed with attention to how the model functions under extreme conditions, such as a very high patient load.

Major system changes are expensive and must be made carefully. Systems engineering techniques, such as DES, provide techniques to estimate the impact of changes on pertinent care delivery variables. Results from this study underscore the complexity of patient care delivery and how simulation models can integrate multiple system components to provide a data-driven approach to inform decision making in a complex system.

Acknowledgments

The simulation software used in this study was awarded as an educational software grant from SIMIO®. We would like to acknowledge support from the Department of Internal Medicine at Virginia Commonwealth University for this project and thank Lena Rivera for her assistance with the manuscript preparation.

Dislosures

Dr. Heim recived a consulting fee for programming guidance from Virginia Commonwealth University. All other authors have nothing to disclose.

Hospitals are complex adaptive systems within which practitioners, technology, physical resources, and other components adapt interdependently to attempt to best meet the needs of patients.¹ Hospitals must provide a stable, dependable level of care while also surging to respond to times of high demand, such as patient emergencies or swells in patient volume. Given the critical and resource-intensive nature of this work, optimizing the system is essential; however, because of the complexity of the system, making changes can result in unexpected and possibly deleterious effects. We need to approach change in hospital processes carefully and thoughtfully.

The Institute of Medicine, the National Academy of Engineering, and the President’s Council of Advisors on Science and Technology have recommended the application of systems engineering approaches to improve health care delivery.^2,3 Systems engineering seeks to coordinate, synchronize, and integrate complex systems of people, information, materials, technology, and financial resources.^4,5 To determine how complex systems can be improved, engineers apply analytic methods to describe how such systems operate and what the impact of changes might be. These methodologies have improved patient care and reduced costs at several hospitals.⁶ For example, a decision support system that combined simulation, optimization, and machine learning methods in an emergency department (ED) resulted in a 33% reduction in length of stay (LOS) and a 28% decrease in ED readmissions.⁷ Other strategies to improve patient flow include shaping demand (decreasing variation in surgical scheduling, relocating low acuity care ED visit to primary care, etc.), redesigning systems (early discharges, improving efficiency, and coordination of hospital discharge process, decreasing care variation, etc.), or aligning capacity and demand. Another approach, real-time demand capacity (RTDC), is based on management principles and queuing and constraint theory and has been implemented successfully in a variety of health care organizations. RTDC represents a promising approach to improve hospitalwide patient flow and can be integrated into current bed management processes.⁸ Unfortunately, many of these approaches are not well known to clinicians and would benefit from greater awareness and input from healthcare practitioners.

One systems engineering tool that can be used to describe, analyze, and evaluate proposed changes in care is simulation.⁹ Simulation creates a model within which what-if scenarios (ie, adjusting various inputs into the simulation) allow researchers to define the likelihood of consequences from various courses of action and determine the optimal change to a system. Such analyses can predict the impact of a proposed change on patients and healthcare practitioners.^10-13

A critical concern for hospitals that simulation may help address is managing the volume of inpatients. A high inpatient census is necessary for financial solvency, yet too high a census of inpatients or an unexpected surge in acuity can overwhelm hospital resources. Many hospitals, pressured by growing numbers of increasingly complex patients, have seen medical inpatients spread across multiple nonmedical nursing units (NUs) of their institution such that a particular medical team may have only a couple patients assigned to each nursing unit.¹⁴ This dispersion may hinder communication between physicians and nurses and limits the time physicians have to interact with patients.¹⁵ Additionally, coordination of care may become more challenging for discharge planning.¹⁶ Aligning medical teams with NUs may benefit the quality and efficiency of care or may create a barrier to patient flow, which worsens these problems.^15,17 Alternatively, hospitals might meet the increasing demands for care by choosing to add capacity by opening new NUs or hiring additional healthcare providers. We identified no studies in the literature that applied simulation modeling to general medicine inpatients to evaluate the impact of these different decisions.

This article describes the application of simulation to model the interconnected variables and subsequent future states created by several possible strategic decisions around the care of general medicine inpatients. Through the application of systems engineering techniques, we modeled four future states that illustrate the following: (1) the complexities of a large health delivery system, (2) the intended and unintended consequences of implementing different changes in the process of care delivery, and (3) how the simulation modeling might be used to inform decision making.

Setting and Present State

Virginia Commonwealth University (VCU) is a 865-bed tertiary academic medical center, with inpatient care activities spread between four connected buildings and 50 different NUs. The occupancy rate had been over 92% during the time period of this project with admission volume limited primarily by the capacity of the facility. Three of the NUs were primarily allocated to general medicine (GIM) patients. However, over the years, GIM inpatients grew to over 7500 admissions annually, resulting in nearly 50% of GIM patients being admitted to a non-GIM nursing unit.

Additionally, patients on each medical team had a high degree of spread across NUs due to several factors. Admissions and discharges from the hospital did not align across the day. While discharges clumped in the late afternoon, admission occurred throughout the day with a surge in the later afternoon. This mismatch frequently led to patients waiting in the ED for a bed, medical team, or both, and patients were typically assigned to the first available bed and team. For medical team assignments, newly admitted patients were distributed relatively equally across five hospitalist teams and five housestaff teams (that include residents, interns, and medical students). This steady distribution of patients through the day supported meeting housestaff work-hour restrictions of 80 hours each week.¹⁸ Yet, as a result of the high occupancy rate, the patterns of patient admissions and discharges, and the distribution of patients among medical teams and across NUs, medical teams and NUs rarely shared more than a few patients.

Leaders at our institution outlined several possible options to address these challenges, including aligning medical teams with NU, adding an additional hospitalist team, or adding an additional nursing unit. In addition, institutional leaders were concerned about the impact of continued growth in admission volume and the impact of patient dispersion on trainees and students. The overall goal of creating a simulation model was to determine the impact of an increased volume of patients and these possible strategic decisions on operational metrics, including number of patients waiting in the ED, ED boarding time per patient, time in system per patient (ED boarding time plus inpatient LOS), team utilization, and rounding travel time.

Simulation Modeling

To model the impact of some possible system changes on patient care, we applied Kelton and Law’s simulation study framework,¹⁹ including data collection; model building and validation; and what-if scenario testing (Figure 1).

Data Collection

Process Flow Map

We created a complex process flow map of patient care activities on medical teams. The map was developed by four general medicine physicians (R.C., H.M., V.M., and S.P.T.) who all provided medical care on the hospital-based services and ensured expert input on the patient care activities captured by the simulation modeling.

Time and Motion Studies

Time and motion study is a well-established technique used to evaluate the efficiency of work processes.^20,21 Originally applied to increase productivity in manufacturing, this technique uses first-hand observations to measure the time allotted to different work tasks to systematically analyze workflow.²² Workflow in healthcare, like manufacturing tasks, tends to have a repetitive pattern, making time and motion studies a highly applicable tool.

A research assistant observed a total of 30 hospitalist work cycles to describe the work of our inpatient clinicians. A work cycle, defined as one complete process flow,²³ began when the hospitalist started a daytime shift of patient care and concluded after the physician “signed out” to the physician who was assuming responsibility for ongoing medical care of the patients (ie, cross-coverage). Time spent on different activities identified by the process flow map was captured throughout the cycle. These activities included time spent traveling to evaluate patients located on different NUs. To minimize disruptions in patient care and adhere to privacy standards, no observations were conducted in patient rooms, and details of computer work were not recorded. To ensure stable estimates of the mean and standard deviation of the time spent at each step, at least 30 cycles of observation are recommended. Thus, 300 hours of observations over the course of 30 separate days were collected.

Hospital Data

We extracted admission and discharge data from the electronic health records (EHR) for general medicine patients admitted from the ED for the calendar year 2013. These records were used to establish means and standard deviations for admission date and time, distribution of patients across NUs, and LOS.

Model Building and Internal Validation

On the basis of these data inputs and using SIMIO® Simulation Software version 7, we constructed a discrete event simulation (DES) model representing the patient care activities of general medicine teams. Each patient was assigned a bed on a nursing unit through a probability distribution based on prior EHR data and then randomly assigned to a general medicine team. We replicated the model 200 times, and each model ran for 365 days. Each team was limited to 16 assigned patients, the maximum number of patients per housestaff team allowed by VCU protocol; henceforth, this number is referred to as team-patient capacity. The model assumed patients remained on the assigned nursing unit and medical team for the entirety of their hospital stay and that each patient was seen by their assigned medical team every day. The results of the present state model, including mean number of patients on each nursing unit, mean team census, patient dispersion (ie, the number of NUs on which each medical team had patients), and team utilization (ie, mean team census divided by team patient capacity), were compared with actual data from 2013 to internally validate the model.

What-If Scenario Testing

We constructed four what-if scenarios based on possible strategic directions identified by leadership. These models evaluated:

• constraining patients on housestaff (but not hospitalist) teams to the three general medicine NUs (Future State 1),
• increasing bed capacity for general medicine patients by adding one additional nursing unit of 26 beds (Future State 2),
• increasing the number of general medicine teams by adding one additional hospitalist team of up to 16 patients (Future State 3),
• modeling the impact of increased patient admissions from 21 per day to 25 per day while also adding a nursing unit and an additional medical team (Future State 4).

For Future States 1-3, admission volume was held constant. The model generated nursing unit LOS using a random continuous exponential probability distribution with a mean of 133 hours to match the LOS distribution derived from health system data. As patients entered the system for admission, the model assigned a bed to the patient, but the patient could not move to the assigned bed until a bed and care team were both available. We were only interested in the steady-state behavior of the system, so collecting performance statistics only after the model had been populated and steady state had been achieved was important.

Table 1 summarizes the input data, fixed, and dynamic variable for each future state model.

Time and Motion Data

The mean time spent with each patient was nine minutes. The mean time traveling between NUs Healthcare Quality for Children and Adolescents with Suicidality Admitted to Acute Care Hospitals in the United States was five minutes. Average rounding time was noted to be two hours, 53 minutes. Thirty-seven minutes, about ~21% of the time, was wasted in traveling. Each team, on average, traveled to seven different NUs to round on their daily census, averaging 1.6 patients in each nursing unit.

Hospital Data

Between January 1, 2011 to December 31, 2013, a total of 7,902 patients were admitted to the general medicine teams, spanning 23 NU. The average number of admissions per day was 21.6, and the average nursing unit LOS was 133 hours. Average team census was derived from historical data across all GIM team for 2013 and was noted to be 11.5 patients per team, and these patients were spread over seven NU.

Model Validation

The mean number of patients admitted to different NUs was estimated from the simulation model then compared with the EHR data from 2013. None were statistically different (P > .05), which signified that the validated simulation model is similar to the EHR data from 2013 despite the underlying assumptions.

Model Outputs

Analysis of the models indicated that steady-state (based upon hospital census) was realized at approximately 800 hours or after 680 patients were admitted to the GIM teams. Statistics collection, therefore, was started after 800 hours of simulated time and reflected the admission of the remaining 7222 patients in the model validation sample (Table 2).

In the model, the total daily general medicine patient census was 119.26. Average time in the system per patient was noted to be 147.37 hours, which was 14.37 hours more than the average nursing unit LOS of 133 hours. Average number of patients waiting for a bed was noted to be 11.31, while the average wait time for a patient to get a bed was 12.39 hours.

Average housestaff team and hospitalist team utilization were 76.06% and 73.02%, respectively, with average team utilization of 74.54% (range: 72.88%-76.19%). Housestaff team and hospitalist team averaged 12.17 and 11.68 patients per care team, respectively. General medicine teams had patients on 7.30 NUs on average. GIM teams rounding travel time was 36.5 minutes.

What-If Scenario Testing

Simulation outputs for the four future states are summarized in Table 2. With Future State 1, through which patients were selectively assigned to housestaff teams aligned with three NUs, the average time in the system per patient increased by 2.35 hours, with 1.87 more patients waiting for a bed and waiting for 2.03 more hours as compared with the present state. A marked disparity was observed in hospitalist and housestaff team utilization of 62.22% and 86.55% respectively. Patient dispersion to various NUs significantly decreased, and rounding time correspondingly decreased by approximately 41%.

Future State 2, adding a nursing unit, decreased average time in the system per patient by 9.86 hours, with 9.32 fewer patients waiting for a bed as compared with the present state. A slight increase in patient dispersion and rounding time was observed. Overall, patients spent 137.51 hours in the system, which demonstrated improved efficiency of the system.

Future State 3, adding an additional medical team, interestingly did not have a significant effect on patients’ average time in system or the number of patients waiting for a bed even though a decrease occurred in average team census, team utilization, and patient dispersion.

Finally, Future State 4, increasing admissions while also adding a nursing unit and a hospitalist team, resulted in an increase in admission volume while maintaining similar utilization rates for teams and NU. Patients spent about 2.48 hours less in the system, while only 9.94 patients were noted to be waiting for a bed as compared with 11.21 patients in the present state model. The total daily general medicine patient census was noted to be 137.19. Average team census and average team utilization were noted to be similar to those of the present state model, while admissions were up by approximately 1,080 per year. Both patient dispersion and rounding were slightly worsened.

Sensitivity Analysis

Overall, average time in system was most affected by the number of patient arrivals. This became particularly significant as the volume of patient arrivals approached and exceeded the capacity of the rounding teams. Adding a nursing unit had more impact on decreasing average time in the system than adding a medical team or aligning teams with NUs under the conditions defined by the model. However, under different conditions, such as increasing admission volume, the relative benefit of different approaches may vary.

Given that hospitals are large, complex systems,² the impact of system-level changes can have unpredictable and potentially deleterious effects. Simulation provides a technique for modeling the impact of changes to understand the ramifications of these interventions more thoroughly.³ In this study, we describe the process of building a simulation model for the admission and discharge of patients from general medicine services in a tertiary care hospital, internally validating this model, and examining the outcomes from several potential changes to the system.

The outcomes for these what-if scenarios provided some important insights about the secondary effect of system changes and the need for multiple, simultaneous interventions. Given that hospitals often function at near capacity, adding a hospitalist team or nursing unit might be seen as a reasonable strategy to improve the system metrics, number of patient discharges, or average LOS. On the basis of our analysis, adding a nursing unit would have more benefit than adding a hospitalist team. Leaders who want to increase capacity may need to consider both adding a hospitalist team and a nursing unit, and model the impact of each choice as described with a simulation.

Additionally, assigning patients to medical teams aligned with NUs seems theoretically appealing to improve interprofessional communication and decrease the time spent in transit between patients by physicians. While our findings supported a decrease in rounding time and patient dispersion, the teams not aligned with a nursing unit (ie, the hospitalists) exceeded 80% utilization, the threshold at which efficiency is known to decrease.²⁴ Potentially, benefits resulting from teams being aligned with NUs were offset by decrements in performance of the teams not aligned with NU. If medical teams and NUs become aligned, then a higher number of teams may be necessary to maintain patient throughput.

Simulation models identify these unexpected consequences prior to investing resources in a significant change; however, modeling is not simple. Simulation models depend on the characteristics of the model and the quality of the input data. For example, we used an expert approach to map physician workflow as an underpinning of the model, but we may have missed an important variation in physician workflow. Understanding this variation could strengthen the model and provide some testable variables for future study. Likewise, understanding nursing workflow and how variation in physician workflow shapes nursing workflow, and vice versa, is worth exploring.

Other data could also be added to, and help interpret, the outputs of this model. For example, the impact of various levels of team and unit utilization on diversion time for the hospital ED may help determine whether adding team capacity or unit capacity is more beneficial for the system. Likewise, aligning medical teams with NUs seems to hinder patient throughput on this analysis, but benefits in patient satisfaction or decreased readmissions might improve reimbursement and outweigh the revenue lost from throughput. Underpinning each of these types of decisions is a need to model the system well and thoughtfully choose the inputs, processes, and outputs. Pursuing a new strategic decision usually involves cost; simulation modeling provides data to help leaders weigh the benefits in terms of the needed investment.

The major limitations of the study stem from these choices. Our study focused on matching capacity and demand while limiting other changes in the system, such as changes in nursing unit LOS. Future work to quantify the relationship of other variables on parameters, such as the impact of decreased team dispersion on LOS, early discharges, and decreasing care variation, would make future models more robust. This model does not consider other strategies to improve patient flow, such as shaping demand, adaptive team assignment algorithms, or creating surge capacity. We also used only hospitalist time and motion data in our model; housestaff workflow is likely different. In addition, we modeled all patients as having a general level of nursing care and did not account for admissions or transfers to intensive care units or other services. These parameters could be added in future iterations. Finally, the biggest limitation in any simulation is the underlying assumptions made to construct the model. While we validated the model retrospectively, prospective validation and refinement should also be performed with attention to how the model functions under extreme conditions, such as a very high patient load.

Major system changes are expensive and must be made carefully. Systems engineering techniques, such as DES, provide techniques to estimate the impact of changes on pertinent care delivery variables. Results from this study underscore the complexity of patient care delivery and how simulation models can integrate multiple system components to provide a data-driven approach to inform decision making in a complex system.

Acknowledgments

The simulation software used in this study was awarded as an educational software grant from SIMIO®. We would like to acknowledge support from the Department of Internal Medicine at Virginia Commonwealth University for this project and thank Lena Rivera for her assistance with the manuscript preparation.

Dislosures

Dr. Heim recived a consulting fee for programming guidance from Virginia Commonwealth University. All other authors have nothing to disclose.

It is 5:45 am. Thousands of diligent interns are roaming inpatient wards, quietly entering hospital rooms, and gently nudging their patients awake. Little do they know that their rounding is part of a system that unintentionally degrades the quantity and quality of patient sleep and may leave patients worse off than the illness that originally brought them to the hospital.¹ A multitude of adverse outcomes has been associated with sleep deprivation, including aberrant glucose metabolism, impaired wound healing, impaired physical function and coordination, and altered cognition.² To put it simply, sleep is vital.³ Restoring normal sleep patterns in hospitalized patients may decrease hospital length of stay, reduce hospital readmissions, and, as such, should be a new priority for quality improvement.⁴

In this edition of the Journal of Hospital Medicine, Arora et al. present a single-center, pre–post analysis of an intervention designed to improve sleep for hospitalized patients.⁵ The SIESTA (Sleep for Inpatients: Empowering Staff to Act) intervention was composed of the following three components: provider education on patient sleep, Electronic Health Record (EHR) promotion of sleep-friendly order entry, and empowerment of nurses to actively protect patient sleep. Education and changes to order entry were implemented in two hospital units, but only one received the additional nurse-empowerment intervention. Results were compared for six months pre- and post-intervention. Although the authors found increases in sleep-friendly orders in both units, nighttime room entries and patient-reported sleep disturbance decreased only in the nurse-empowerment unit.

Previous studies assessing both pharmacologic sleep aids as well as bundled nonpharmacologic interventions have demonstrated mixed results and focused primarily on ICU populations.^6,7 What sets this study apart from prior interventions aimed at improving patient sleep is the novelty and implications of their successful intervention. In this study, the authors used the EHR and nursing huddles to “nudge” providers to protect their patients’ sleep. The “nudge” concept, first studied in behavioral economics and more recently applied to healthcare, represents ways to present choices that positively influence behavior without restricting options.⁸ This study incorporates two distinct nudges, one that utilized the EMR to adjust the default timing of orders for vital sign procurement and delivery of VTE-prophylaxis, and another that made sleep part of the default checklist for nursing huddles. This study suggests that nudges altered both physician and nurse behavior and encouraged improvements in process measures, if not clinical outcomes, around patient sleep.

A key insight and strength of this study was to engage and empower nurses to promote better sleep for patients. In particular, nurses in the sleep-enhanced unit suggested—during the course of the intervention—that sleep protection be added as a default item in daily huddles. As illustrated in the Figure, the timing of this suggestion corresponded with an inflection point in reducing patient room disruptions at night. This simple, low-cost nudge sustained sleep improvement while the effect of the initial higher-cost intervention using pocket cards and posters had begun to fade. This is not a randomized clinical trial, but rather a pragmatic assessment of a rigorous quality improvement initiative. Although more follow-up time, particularly after the nurse-empowerment intervention was adjusted, would be helpful to assess the durability of their intervention, we applaud the authors for demonstrating adaptability and efforts for ongoing engagement, as is needed in real-world quality improvement initiatives.

There are additional factors that disrupt patient sleep that were not targeted in this study but could very well respond to nudges. Recently, Wesselius et al. showed that patient-reported nocturnal awakenings were frequently due to toilet visits and awakening by hospital staff.⁹ Perhaps nudges could be implemented to reduce unnecessary overnight intravenous fluids, prevent late dosing of diuretics, and delay the default timing of standard morning phlebotomy orders.

Although this study by Arora et al. makes a very meaningful contribution to the literature on sleep and hospitalization, it also raises unanswered questions.⁵ First and foremost, while the pragmatic nature of this study should inspire other hospitals to attempt similar sleep promotion interventions, the use of a pre–post design (rather than a randomized, control design) leaves room for future studies to explore causality more rigorously. Second, although this study has demonstrated significant uptake in standardized order sets to improve sleep (and a corresponding decrease in patient-reported disruptions), future studies should also explore more distal and more challenging outcomes of care. These could include length of stay, incidence of delirium (especially in older adults), and frequency of readmission after discharge. Finally, more longitudinal data to explore the sustainability of order set usage and reported or observed interruptions would be useful to guide hospitals that would like to follow the example set by the SIESTA study.

Notwithstanding these limitations, there is an incredible opportunity for nudges and technology to combine to change the paradigms of clinical care. One of the outcomes of this study was to reduce nocturnal room entry for clinical tasks such as obtaining vital signs. It is worth considering whether providers even need to enter patient rooms to obtain vital signs. The technology now exists to measure vitals passively and continuously via low-impact wearable devices. Milani et al. employed the use of such devices, as well as other techniques, including red-enriched light and sensors that warned staff in clinical areas when noises exceeded acceptable thresholds for sleep, and demonstrated decreases in hospital length of stay and readmission rates.⁴

Arora et al. present a compelling study of utilizing nudges to influence physician and nurse behavior.⁵ They show that rigorous quality improvement initiatives can be studied and disseminated in a compelling manner. Their study calls appropriate attention to the need for improving patient sleep and provides us with additional tools that can be used in these efforts. Future research is needed to determine whether the changes observed in process measures will translate into meaningful effects on clinical outcomes and to continue to identify ways to curb some of the toxicities of hospital care.

Disclosures

The authors have nothing to disclose.

It is 5:45 am. Thousands of diligent interns are roaming inpatient wards, quietly entering hospital rooms, and gently nudging their patients awake. Little do they know that their rounding is part of a system that unintentionally degrades the quantity and quality of patient sleep and may leave patients worse off than the illness that originally brought them to the hospital.¹ A multitude of adverse outcomes has been associated with sleep deprivation, including aberrant glucose metabolism, impaired wound healing, impaired physical function and coordination, and altered cognition.² To put it simply, sleep is vital.³ Restoring normal sleep patterns in hospitalized patients may decrease hospital length of stay, reduce hospital readmissions, and, as such, should be a new priority for quality improvement.⁴

In this edition of the Journal of Hospital Medicine, Arora et al. present a single-center, pre–post analysis of an intervention designed to improve sleep for hospitalized patients.⁵ The SIESTA (Sleep for Inpatients: Empowering Staff to Act) intervention was composed of the following three components: provider education on patient sleep, Electronic Health Record (EHR) promotion of sleep-friendly order entry, and empowerment of nurses to actively protect patient sleep. Education and changes to order entry were implemented in two hospital units, but only one received the additional nurse-empowerment intervention. Results were compared for six months pre- and post-intervention. Although the authors found increases in sleep-friendly orders in both units, nighttime room entries and patient-reported sleep disturbance decreased only in the nurse-empowerment unit.

Previous studies assessing both pharmacologic sleep aids as well as bundled nonpharmacologic interventions have demonstrated mixed results and focused primarily on ICU populations.^6,7 What sets this study apart from prior interventions aimed at improving patient sleep is the novelty and implications of their successful intervention. In this study, the authors used the EHR and nursing huddles to “nudge” providers to protect their patients’ sleep. The “nudge” concept, first studied in behavioral economics and more recently applied to healthcare, represents ways to present choices that positively influence behavior without restricting options.⁸ This study incorporates two distinct nudges, one that utilized the EMR to adjust the default timing of orders for vital sign procurement and delivery of VTE-prophylaxis, and another that made sleep part of the default checklist for nursing huddles. This study suggests that nudges altered both physician and nurse behavior and encouraged improvements in process measures, if not clinical outcomes, around patient sleep.

A key insight and strength of this study was to engage and empower nurses to promote better sleep for patients. In particular, nurses in the sleep-enhanced unit suggested—during the course of the intervention—that sleep protection be added as a default item in daily huddles. As illustrated in the Figure, the timing of this suggestion corresponded with an inflection point in reducing patient room disruptions at night. This simple, low-cost nudge sustained sleep improvement while the effect of the initial higher-cost intervention using pocket cards and posters had begun to fade. This is not a randomized clinical trial, but rather a pragmatic assessment of a rigorous quality improvement initiative. Although more follow-up time, particularly after the nurse-empowerment intervention was adjusted, would be helpful to assess the durability of their intervention, we applaud the authors for demonstrating adaptability and efforts for ongoing engagement, as is needed in real-world quality improvement initiatives.

There are additional factors that disrupt patient sleep that were not targeted in this study but could very well respond to nudges. Recently, Wesselius et al. showed that patient-reported nocturnal awakenings were frequently due to toilet visits and awakening by hospital staff.⁹ Perhaps nudges could be implemented to reduce unnecessary overnight intravenous fluids, prevent late dosing of diuretics, and delay the default timing of standard morning phlebotomy orders.

Although this study by Arora et al. makes a very meaningful contribution to the literature on sleep and hospitalization, it also raises unanswered questions.⁵ First and foremost, while the pragmatic nature of this study should inspire other hospitals to attempt similar sleep promotion interventions, the use of a pre–post design (rather than a randomized, control design) leaves room for future studies to explore causality more rigorously. Second, although this study has demonstrated significant uptake in standardized order sets to improve sleep (and a corresponding decrease in patient-reported disruptions), future studies should also explore more distal and more challenging outcomes of care. These could include length of stay, incidence of delirium (especially in older adults), and frequency of readmission after discharge. Finally, more longitudinal data to explore the sustainability of order set usage and reported or observed interruptions would be useful to guide hospitals that would like to follow the example set by the SIESTA study.

Notwithstanding these limitations, there is an incredible opportunity for nudges and technology to combine to change the paradigms of clinical care. One of the outcomes of this study was to reduce nocturnal room entry for clinical tasks such as obtaining vital signs. It is worth considering whether providers even need to enter patient rooms to obtain vital signs. The technology now exists to measure vitals passively and continuously via low-impact wearable devices. Milani et al. employed the use of such devices, as well as other techniques, including red-enriched light and sensors that warned staff in clinical areas when noises exceeded acceptable thresholds for sleep, and demonstrated decreases in hospital length of stay and readmission rates.⁴

Arora et al. present a compelling study of utilizing nudges to influence physician and nurse behavior.⁵ They show that rigorous quality improvement initiatives can be studied and disseminated in a compelling manner. Their study calls appropriate attention to the need for improving patient sleep and provides us with additional tools that can be used in these efforts. Future research is needed to determine whether the changes observed in process measures will translate into meaningful effects on clinical outcomes and to continue to identify ways to curb some of the toxicities of hospital care.

Disclosures

The authors have nothing to disclose.

It is 5:45 am. Thousands of diligent interns are roaming inpatient wards, quietly entering hospital rooms, and gently nudging their patients awake. Little do they know that their rounding is part of a system that unintentionally degrades the quantity and quality of patient sleep and may leave patients worse off than the illness that originally brought them to the hospital.¹ A multitude of adverse outcomes has been associated with sleep deprivation, including aberrant glucose metabolism, impaired wound healing, impaired physical function and coordination, and altered cognition.² To put it simply, sleep is vital.³ Restoring normal sleep patterns in hospitalized patients may decrease hospital length of stay, reduce hospital readmissions, and, as such, should be a new priority for quality improvement.⁴

In this edition of the Journal of Hospital Medicine, Arora et al. present a single-center, pre–post analysis of an intervention designed to improve sleep for hospitalized patients.⁵ The SIESTA (Sleep for Inpatients: Empowering Staff to Act) intervention was composed of the following three components: provider education on patient sleep, Electronic Health Record (EHR) promotion of sleep-friendly order entry, and empowerment of nurses to actively protect patient sleep. Education and changes to order entry were implemented in two hospital units, but only one received the additional nurse-empowerment intervention. Results were compared for six months pre- and post-intervention. Although the authors found increases in sleep-friendly orders in both units, nighttime room entries and patient-reported sleep disturbance decreased only in the nurse-empowerment unit.

Previous studies assessing both pharmacologic sleep aids as well as bundled nonpharmacologic interventions have demonstrated mixed results and focused primarily on ICU populations.^6,7 What sets this study apart from prior interventions aimed at improving patient sleep is the novelty and implications of their successful intervention. In this study, the authors used the EHR and nursing huddles to “nudge” providers to protect their patients’ sleep. The “nudge” concept, first studied in behavioral economics and more recently applied to healthcare, represents ways to present choices that positively influence behavior without restricting options.⁸ This study incorporates two distinct nudges, one that utilized the EMR to adjust the default timing of orders for vital sign procurement and delivery of VTE-prophylaxis, and another that made sleep part of the default checklist for nursing huddles. This study suggests that nudges altered both physician and nurse behavior and encouraged improvements in process measures, if not clinical outcomes, around patient sleep.

A key insight and strength of this study was to engage and empower nurses to promote better sleep for patients. In particular, nurses in the sleep-enhanced unit suggested—during the course of the intervention—that sleep protection be added as a default item in daily huddles. As illustrated in the Figure, the timing of this suggestion corresponded with an inflection point in reducing patient room disruptions at night. This simple, low-cost nudge sustained sleep improvement while the effect of the initial higher-cost intervention using pocket cards and posters had begun to fade. This is not a randomized clinical trial, but rather a pragmatic assessment of a rigorous quality improvement initiative. Although more follow-up time, particularly after the nurse-empowerment intervention was adjusted, would be helpful to assess the durability of their intervention, we applaud the authors for demonstrating adaptability and efforts for ongoing engagement, as is needed in real-world quality improvement initiatives.

There are additional factors that disrupt patient sleep that were not targeted in this study but could very well respond to nudges. Recently, Wesselius et al. showed that patient-reported nocturnal awakenings were frequently due to toilet visits and awakening by hospital staff.⁹ Perhaps nudges could be implemented to reduce unnecessary overnight intravenous fluids, prevent late dosing of diuretics, and delay the default timing of standard morning phlebotomy orders.

Although this study by Arora et al. makes a very meaningful contribution to the literature on sleep and hospitalization, it also raises unanswered questions.⁵ First and foremost, while the pragmatic nature of this study should inspire other hospitals to attempt similar sleep promotion interventions, the use of a pre–post design (rather than a randomized, control design) leaves room for future studies to explore causality more rigorously. Second, although this study has demonstrated significant uptake in standardized order sets to improve sleep (and a corresponding decrease in patient-reported disruptions), future studies should also explore more distal and more challenging outcomes of care. These could include length of stay, incidence of delirium (especially in older adults), and frequency of readmission after discharge. Finally, more longitudinal data to explore the sustainability of order set usage and reported or observed interruptions would be useful to guide hospitals that would like to follow the example set by the SIESTA study.

Notwithstanding these limitations, there is an incredible opportunity for nudges and technology to combine to change the paradigms of clinical care. One of the outcomes of this study was to reduce nocturnal room entry for clinical tasks such as obtaining vital signs. It is worth considering whether providers even need to enter patient rooms to obtain vital signs. The technology now exists to measure vitals passively and continuously via low-impact wearable devices. Milani et al. employed the use of such devices, as well as other techniques, including red-enriched light and sensors that warned staff in clinical areas when noises exceeded acceptable thresholds for sleep, and demonstrated decreases in hospital length of stay and readmission rates.⁴

Arora et al. present a compelling study of utilizing nudges to influence physician and nurse behavior.⁵ They show that rigorous quality improvement initiatives can be studied and disseminated in a compelling manner. Their study calls appropriate attention to the need for improving patient sleep and provides us with additional tools that can be used in these efforts. Future research is needed to determine whether the changes observed in process measures will translate into meaningful effects on clinical outcomes and to continue to identify ways to curb some of the toxicities of hospital care.

Disclosures

The authors have nothing to disclose.

A 33-year-old male nonsmoker with no significant medical history presented to the pulmonary clinic with severe left-sided pleuritic chest pain and mild breathlessness for the past 5 days. He denied fever, chills, cough, phlegm, runny nose, or congestion.

Five days before this visit, he had been seen in the emergency department with mild left-sided pleuritic chest pain. His vital signs at that time had been as follows:

• Blood pressure 141/77 mm Hg
• Heart rate 77 beats/minute
• Respiratory rate 17 breaths/minute
• Temperature 36.8°C (98.2°F)
• Oxygen saturation 98% on room air.

• White blood cell count 6.89 × 10⁹/L (reference range 3.70–11.00)
• Neutrophils 58% (40%–70%)
• Lymphocytes 29.6% (22%–44%)
• Monocytes 10.7% (0–11%)
• Eosinophils 1% (0–4%)
• Basophils 0.6% (0–1%)
• Troponin T and D-dimer levels normal.

DIFFERENTIAL DIAGNOSIS OF PLEURITIC CHEST PAIN

1. What is the most likely cause of his pleuritic chest pain?

• Pleuritis
• Pneumonia
• Pulmonary embolism
• Malignancy

The differential diagnosis of pleuritic chest pain is broad.

The patient’s symptoms at presentation to the emergency department did not suggest an infectious process. There was no fever, cough, or phlegm, and his white blood cell count was normal. Nonetheless, pneumonia could not be ruled out, as the lung parenchyma was not normal on radiography, and the findings could have been consistent with an early or resolving infectious process.

Pulmonary embolism was a possibility, but his normal D-dimer level argued against it. Further, the patient subsequently underwent CT angiography, which ruled out pulmonary embolism.

Malignancy was unlikely in a young nonsmoker, but follow-up imaging would be needed to ensure resolution and rule this out.

The emergency department physician diagnosed inflammatory pleuritis and discharged him home on a nonsteroidal anti-inflammatory drug.

CLINIC VISIT 5 DAYS LATER

At his pulmonary clinic visit 5 days later, the patient reported persistent but stable left-sided pleuritic chest pain and mild breathlessness on exertion. His blood pressure was 137/81 mm Hg, heart rate 109 beats per minute, temperature 37.1°C (98.8°F), and oxygen saturation 97% on room air.

Auscultation of the lungs revealed rales and slightly decreased breath sounds at the left base. No dullness to percussion could be detected.

Because the patient had developed mild tachycardia and breathlessness along with clinical signs that suggested worsening infiltrates, consolidation, or the development of pleural effusion, he underwent further investigation with chest radiography, a complete blood cell count, and measurement of serum inflammatory markers.

• White blood cell count 13.08 × 10⁹/L
• Neutrophils 81%
• Lymphocytes 7.4%
• Monocytes 7.2%
• Eeosinophils 0.2%
• Basophils 0.2%
• Procalcitonin 0.34 µg/L (reference range < 0.09).

Bedside ultrasonography to assess the effusion’s size and characteristics and the need for thoracentesis indicated that the effusion was too small to tap, and there were no fibrinous strands or loculations to suggest empyema.

2. What was the best management strategy for this patient at this time?

• Admit to the hospital for thoracentesis and intravenous antibiotics
• Give oral antibiotics with close follow-up
• Perform thoracentesis on an outpatient basis and give oral antibiotics
• Repeat chest CT

The patient had worsening pleuritic pain with development of a small left pleural effusion. His symptoms had not improved on a nonsteroidal anti-inflammatory drug. He now had an elevated white blood cell count with a “left shift” (ie, an increase in neutrophils, indicating more immature cells in circulation) and elevated procalcitonin. The most likely diagnosis was pneumonia with a resulting pleural effusion, ie, parapneumonic effusion, requiring appropriate antibiotic therapy. Ideally, the pleural effusion should be sampled by thoracentesis, with management on an outpatient or inpatient basis.

5 DAYS LATER, THE EFFUSION HAD BECOME MASSIVE

On follow-up 5 days later, the patient’s chest pain was better, but he was significantly more short of breath. His blood pressure was 137/90 mm Hg, heart rate 117 beats/minute, respiratory rate 16 breaths/minute, oxygen saturation 97% on room air, and temperature 36.9°C (98.4°F). Chest auscultation revealed decreased breath sounds over the left hemithorax, with dullness to percussion and decreased fremitus.

RAPIDLY PROGRESSIVE PLEURAL EFFUSIONS

A rapidly progressive pleural effusion in a healthy patient suggests parapneumonic effusion. The most likely organism is streptococcal.²

Explosive pleuritis is defined as a pleural effusion that increases in size in less than 24 hours. It was first described by Braman and Donat³ in 1986 as an effusion that develops within hours of admission. In 2001, Sharma and Marrie⁴ refined the definition as rapid development of pleural effusion involving more than 90% of the hemithorax within 24 hours, causing compression of pulmonary tissue and a mediastinal shift. It is a medical emergency that requires prompt investigation and treatment with drainage and antibiotics. All reported cases of explosive pleuritis have been parapneumonic effusion.

The organisms implicated in explosive pleuritis include gram-positive cocci such as Streptococcus pneumoniae, S pyogenes, other streptococci, staphylococci, and gram-negative cocci such as Neisseria meningitidis and Moraxella catarrhalis. Gram-negative bacilli include Haemophilus influenzae, Klebsiella pneumoniae, Pseudomonas species, Escherichia coli, Proteus species, Enterobacter species, Bacteroides species, and Legionella species.^4,5 However, malignancy is the most common cause of massive pleural effusion, accounting for 54% of cases; 17% of cases are idiopathic, 13% are parapneumonic, and 12% are hydrothorax related to liver cirrhosis.⁶

CASE CONTINUED

Our patient’s massive effusion needed drainage, and he was admitted to the hospital for further management. Samples of blood and sputum were sent for culture. Intravenous piperacillin-tazobactam was started, and an intercostal chest tube was inserted into the pleural cavity under ultrasonographic guidance to drain turbid fluid.

Multiple pleural fluid samples sent for bacterial, fungal, and acid-fast bacilli culture were negative. Blood and sputum cultures also showed no growth. The administration of oral antibiotics for 5 days on an outpatient basis before pleural fluid culture could have led to sterility of all cultures.

Our patient had inadequate pleural fluid output through his chest tube, and radiography showed that the pleural collections failed to clear. In fact, an apical locule did not appear to be connecting with the lower aspect of the pleural collection. In such cases, instillation of intrapleural agents through the chest tube has become common practice in an attempt to lyse adhesions, to connect various locules or pockets of pleural fluid, and to improve drainage.

3. What was the best management strategy for this loculated empyema?

• Continue intravenous antibiotics and existing chest tube drainage for 5 to 7 days, then reassess
• Continue intravenous antibiotics and instill intrapleural fibrinolytics (eg, tissue plasminogen activator [tPA]) through the existing chest tube
• Continue intravenous antibiotics and instill intrapleural fibrinolytics with deoxyribonuclease (DNase) into the existing chest tube
• Continue intravenous antibiotics, insert a second chest tube into the apical pocket under imaging guidance, and instill tPA and DNase
• Surgical decortication

Continuing antibiotics with existing chest tube drainage and the two options of using single-agent intrapleural fibrinolytics have been shown to be less effective than combining tPA and DNase when managing a loculated empyema. As such, surgical decortication, attempting intrapleural instillation of fibrinolytics and DNase (with or without further chest tube insertion into noncommunicating locules), or both were the most appropriate options at this stage.

MANAGEMENT OF PARAPNEUMONIC PLEURAL EFFUSION IN ADULTS

There are several options for managing parapneumonic effusion, and clinicians can use the classification system in Table 1 to assess the risk of a poor outcome and to plan the management. Based on radiographic findings and pleural fluid sampling, a pleural effusion can be either observed or drained.

Options for drainage of the pleural space include repeat thoracentesis, surgical insertion of a chest tube, or image-guided insertion of a small-bore catheter. Although no randomized trial has been done to compare tube sizes, a large retrospective series showed that small-bore tubes (< 14 F) perform similarly to standard large-bore tubes.⁸ However, in another study, Keeling et al⁹ reported higher failure rates when tubes smaller than 12 F were used. Regular flushing of the chest tube (ideally twice a day) is recommended to keep it patent, particularly with small-bore tubes. Multiloculated empyema may require multiple intercostal chest tubes to drain completely, and therefore small-bore tubes are recommended.

In cases that do not improve radiographically and clinically, one must consider whether the antibiotic choice is adequate, review the position of the chest tube, and assess for loculations. As such, repeating chest CT within 24 to 48 hours of tube insertion and drainage is recommended to confirm adequate tube positioning, assess effective drainage, look for different locules and pockets, and determine the degree of communication between them.

The largest well-powered randomized controlled trials of intrapleural agents in the management of pleural infection, the Multicentre Intrapleural Sepsis Trial (MIST1)¹⁰ and MIST2,¹¹ clearly demonstrated that intrapleural fibrinolytics were not beneficial when used alone compared with placebo. However, in MIST2, the combination of tPA and DNase led to clinically significant benefits including radiologic improvement, shorter hospital stay, and less need for surgical decortication.

At our hospital, we follow the MIST2 protocol using a combination of tPA and DNase given intrapleurally twice daily for 3 days. In our patient, we inserted a chest tube into the apical pocket under ultrasonographic guidance, as 2 instillations of intrapleural tPA and DNase did not result in drainage of the apical locule.

Success rates with intrapleural tPA-DNase for complicated pleural effusion and empyema range from 68% to 92%.^12–15 Pleural thickening and necrotizing pneumonia and abscess are important predictors of failure of tPA-DNase therapy and of the need for surgery.^13,14

Early surgical intervention was another reasonable option in this case. The decision to proceed with surgery is based on need to debride multiloculated empyemas or uniloculated empyemas that fail to resolve with antibiotics and tube thoracostomy drainage. Nonetheless, the decision must be individualized and based on factors such as the patient’s risks vs possible benefit from a surgical procedure under general anesthesia, the patient’s ability to tolerate multiple thoracentesis procedures and chest tubes for a potentially lengthy period, the patient’s pain threshold, the patient’s wishes to avoid a surgical procedure balanced against a longer hospital stay, and cultural norms and beliefs.

Surgical options include video-assisted thoracoscopy, thoracotomy, and open drainage. Decortication can be considered early to control pleural sepsis, or late (after 3 to 6 months) if the lung does not expand. Debate continues on the optimal timing for video-assisted thoracoscopy, with data suggesting that when the procedure is performed later in the course of the disease there is a greater chance of complications and of the need to convert to thoracotomy.

A 2017 Cochrane review¹⁶ of surgical vs nonsurgical management of empyema identified 8 randomized trials, 6 in children and 2 in adults, with a total of 391 patients. The authors compared video-assisted thoracoscopy vs tube thoracotomy, with and without intrapleural fibrinolytics. They noted no difference in rates of mortality or procedural complications. However, the mean length of hospital stay was shorter with video-assisted thoracoscopy than with tube thoracotomy (5.9 vs 15.4 days). They could not assess the impact of fibrinolytic therapy on total cost of treatment in the 2 groups.

A randomized trial is planned to compare early video-assisted thoracoscopy vs treatment with chest tube drainage and t-PA-DNase.¹⁷

At our institution, we use a multidisciplinary approach, discussing cases at weekly meetings with thoracic surgeons, pulmonologists, infectious disease specialists, and interventional radiologists. We generally try conservative management first, with chest tube drainage and intrapleural agents for 5 to 7 days, before considering surgery if the response is unsatisfactory.

THE PATIENT RECOVERED

In our patient, the multiloculated empyema was successfully cleared after intrapleural instillation of 4 doses of tPA and DNAse over 3 days and insertion of a second intercostal chest tube into the noncommunicating apical locule. He completed 14 days of intravenous piperacillin-tazobactam treatment and, after discharge home, completed another 4 weeks of oral amoxicillin-clavulanate. He made a full recovery and was back at work 2 weeks after discharge. Chest radiography 10 weeks after discharge showed normal results.

A 33-year-old male nonsmoker with no significant medical history presented to the pulmonary clinic with severe left-sided pleuritic chest pain and mild breathlessness for the past 5 days. He denied fever, chills, cough, phlegm, runny nose, or congestion.

Five days before this visit, he had been seen in the emergency department with mild left-sided pleuritic chest pain. His vital signs at that time had been as follows:

• Blood pressure 141/77 mm Hg
• Heart rate 77 beats/minute
• Respiratory rate 17 breaths/minute
• Temperature 36.8°C (98.2°F)
• Oxygen saturation 98% on room air.

• White blood cell count 6.89 × 10⁹/L (reference range 3.70–11.00)
• Neutrophils 58% (40%–70%)
• Lymphocytes 29.6% (22%–44%)
• Monocytes 10.7% (0–11%)
• Eosinophils 1% (0–4%)
• Basophils 0.6% (0–1%)
• Troponin T and D-dimer levels normal.

DIFFERENTIAL DIAGNOSIS OF PLEURITIC CHEST PAIN

1. What is the most likely cause of his pleuritic chest pain?

• Pleuritis
• Pneumonia
• Pulmonary embolism
• Malignancy

The differential diagnosis of pleuritic chest pain is broad.

The patient’s symptoms at presentation to the emergency department did not suggest an infectious process. There was no fever, cough, or phlegm, and his white blood cell count was normal. Nonetheless, pneumonia could not be ruled out, as the lung parenchyma was not normal on radiography, and the findings could have been consistent with an early or resolving infectious process.

Pulmonary embolism was a possibility, but his normal D-dimer level argued against it. Further, the patient subsequently underwent CT angiography, which ruled out pulmonary embolism.

Malignancy was unlikely in a young nonsmoker, but follow-up imaging would be needed to ensure resolution and rule this out.

The emergency department physician diagnosed inflammatory pleuritis and discharged him home on a nonsteroidal anti-inflammatory drug.

CLINIC VISIT 5 DAYS LATER

At his pulmonary clinic visit 5 days later, the patient reported persistent but stable left-sided pleuritic chest pain and mild breathlessness on exertion. His blood pressure was 137/81 mm Hg, heart rate 109 beats per minute, temperature 37.1°C (98.8°F), and oxygen saturation 97% on room air.

Auscultation of the lungs revealed rales and slightly decreased breath sounds at the left base. No dullness to percussion could be detected.

Because the patient had developed mild tachycardia and breathlessness along with clinical signs that suggested worsening infiltrates, consolidation, or the development of pleural effusion, he underwent further investigation with chest radiography, a complete blood cell count, and measurement of serum inflammatory markers.

• White blood cell count 13.08 × 10⁹/L
• Neutrophils 81%
• Lymphocytes 7.4%
• Monocytes 7.2%
• Eeosinophils 0.2%
• Basophils 0.2%
• Procalcitonin 0.34 µg/L (reference range < 0.09).

Bedside ultrasonography to assess the effusion’s size and characteristics and the need for thoracentesis indicated that the effusion was too small to tap, and there were no fibrinous strands or loculations to suggest empyema.

2. What was the best management strategy for this patient at this time?

• Admit to the hospital for thoracentesis and intravenous antibiotics
• Give oral antibiotics with close follow-up
• Perform thoracentesis on an outpatient basis and give oral antibiotics
• Repeat chest CT

The patient had worsening pleuritic pain with development of a small left pleural effusion. His symptoms had not improved on a nonsteroidal anti-inflammatory drug. He now had an elevated white blood cell count with a “left shift” (ie, an increase in neutrophils, indicating more immature cells in circulation) and elevated procalcitonin. The most likely diagnosis was pneumonia with a resulting pleural effusion, ie, parapneumonic effusion, requiring appropriate antibiotic therapy. Ideally, the pleural effusion should be sampled by thoracentesis, with management on an outpatient or inpatient basis.

5 DAYS LATER, THE EFFUSION HAD BECOME MASSIVE

On follow-up 5 days later, the patient’s chest pain was better, but he was significantly more short of breath. His blood pressure was 137/90 mm Hg, heart rate 117 beats/minute, respiratory rate 16 breaths/minute, oxygen saturation 97% on room air, and temperature 36.9°C (98.4°F). Chest auscultation revealed decreased breath sounds over the left hemithorax, with dullness to percussion and decreased fremitus.

RAPIDLY PROGRESSIVE PLEURAL EFFUSIONS

A rapidly progressive pleural effusion in a healthy patient suggests parapneumonic effusion. The most likely organism is streptococcal.²

Explosive pleuritis is defined as a pleural effusion that increases in size in less than 24 hours. It was first described by Braman and Donat³ in 1986 as an effusion that develops within hours of admission. In 2001, Sharma and Marrie⁴ refined the definition as rapid development of pleural effusion involving more than 90% of the hemithorax within 24 hours, causing compression of pulmonary tissue and a mediastinal shift. It is a medical emergency that requires prompt investigation and treatment with drainage and antibiotics. All reported cases of explosive pleuritis have been parapneumonic effusion.

The organisms implicated in explosive pleuritis include gram-positive cocci such as Streptococcus pneumoniae, S pyogenes, other streptococci, staphylococci, and gram-negative cocci such as Neisseria meningitidis and Moraxella catarrhalis. Gram-negative bacilli include Haemophilus influenzae, Klebsiella pneumoniae, Pseudomonas species, Escherichia coli, Proteus species, Enterobacter species, Bacteroides species, and Legionella species.^4,5 However, malignancy is the most common cause of massive pleural effusion, accounting for 54% of cases; 17% of cases are idiopathic, 13% are parapneumonic, and 12% are hydrothorax related to liver cirrhosis.⁶

CASE CONTINUED

Our patient’s massive effusion needed drainage, and he was admitted to the hospital for further management. Samples of blood and sputum were sent for culture. Intravenous piperacillin-tazobactam was started, and an intercostal chest tube was inserted into the pleural cavity under ultrasonographic guidance to drain turbid fluid.

Multiple pleural fluid samples sent for bacterial, fungal, and acid-fast bacilli culture were negative. Blood and sputum cultures also showed no growth. The administration of oral antibiotics for 5 days on an outpatient basis before pleural fluid culture could have led to sterility of all cultures.

Our patient had inadequate pleural fluid output through his chest tube, and radiography showed that the pleural collections failed to clear. In fact, an apical locule did not appear to be connecting with the lower aspect of the pleural collection. In such cases, instillation of intrapleural agents through the chest tube has become common practice in an attempt to lyse adhesions, to connect various locules or pockets of pleural fluid, and to improve drainage.

3. What was the best management strategy for this loculated empyema?

• Continue intravenous antibiotics and existing chest tube drainage for 5 to 7 days, then reassess
• Continue intravenous antibiotics and instill intrapleural fibrinolytics (eg, tissue plasminogen activator [tPA]) through the existing chest tube
• Continue intravenous antibiotics and instill intrapleural fibrinolytics with deoxyribonuclease (DNase) into the existing chest tube
• Continue intravenous antibiotics, insert a second chest tube into the apical pocket under imaging guidance, and instill tPA and DNase
• Surgical decortication

Continuing antibiotics with existing chest tube drainage and the two options of using single-agent intrapleural fibrinolytics have been shown to be less effective than combining tPA and DNase when managing a loculated empyema. As such, surgical decortication, attempting intrapleural instillation of fibrinolytics and DNase (with or without further chest tube insertion into noncommunicating locules), or both were the most appropriate options at this stage.

MANAGEMENT OF PARAPNEUMONIC PLEURAL EFFUSION IN ADULTS

There are several options for managing parapneumonic effusion, and clinicians can use the classification system in Table 1 to assess the risk of a poor outcome and to plan the management. Based on radiographic findings and pleural fluid sampling, a pleural effusion can be either observed or drained.

Options for drainage of the pleural space include repeat thoracentesis, surgical insertion of a chest tube, or image-guided insertion of a small-bore catheter. Although no randomized trial has been done to compare tube sizes, a large retrospective series showed that small-bore tubes (< 14 F) perform similarly to standard large-bore tubes.⁸ However, in another study, Keeling et al⁹ reported higher failure rates when tubes smaller than 12 F were used. Regular flushing of the chest tube (ideally twice a day) is recommended to keep it patent, particularly with small-bore tubes. Multiloculated empyema may require multiple intercostal chest tubes to drain completely, and therefore small-bore tubes are recommended.

In cases that do not improve radiographically and clinically, one must consider whether the antibiotic choice is adequate, review the position of the chest tube, and assess for loculations. As such, repeating chest CT within 24 to 48 hours of tube insertion and drainage is recommended to confirm adequate tube positioning, assess effective drainage, look for different locules and pockets, and determine the degree of communication between them.

The largest well-powered randomized controlled trials of intrapleural agents in the management of pleural infection, the Multicentre Intrapleural Sepsis Trial (MIST1)¹⁰ and MIST2,¹¹ clearly demonstrated that intrapleural fibrinolytics were not beneficial when used alone compared with placebo. However, in MIST2, the combination of tPA and DNase led to clinically significant benefits including radiologic improvement, shorter hospital stay, and less need for surgical decortication.

At our hospital, we follow the MIST2 protocol using a combination of tPA and DNase given intrapleurally twice daily for 3 days. In our patient, we inserted a chest tube into the apical pocket under ultrasonographic guidance, as 2 instillations of intrapleural tPA and DNase did not result in drainage of the apical locule.

Success rates with intrapleural tPA-DNase for complicated pleural effusion and empyema range from 68% to 92%.^12–15 Pleural thickening and necrotizing pneumonia and abscess are important predictors of failure of tPA-DNase therapy and of the need for surgery.^13,14

Early surgical intervention was another reasonable option in this case. The decision to proceed with surgery is based on need to debride multiloculated empyemas or uniloculated empyemas that fail to resolve with antibiotics and tube thoracostomy drainage. Nonetheless, the decision must be individualized and based on factors such as the patient’s risks vs possible benefit from a surgical procedure under general anesthesia, the patient’s ability to tolerate multiple thoracentesis procedures and chest tubes for a potentially lengthy period, the patient’s pain threshold, the patient’s wishes to avoid a surgical procedure balanced against a longer hospital stay, and cultural norms and beliefs.

Surgical options include video-assisted thoracoscopy, thoracotomy, and open drainage. Decortication can be considered early to control pleural sepsis, or late (after 3 to 6 months) if the lung does not expand. Debate continues on the optimal timing for video-assisted thoracoscopy, with data suggesting that when the procedure is performed later in the course of the disease there is a greater chance of complications and of the need to convert to thoracotomy.

A 2017 Cochrane review¹⁶ of surgical vs nonsurgical management of empyema identified 8 randomized trials, 6 in children and 2 in adults, with a total of 391 patients. The authors compared video-assisted thoracoscopy vs tube thoracotomy, with and without intrapleural fibrinolytics. They noted no difference in rates of mortality or procedural complications. However, the mean length of hospital stay was shorter with video-assisted thoracoscopy than with tube thoracotomy (5.9 vs 15.4 days). They could not assess the impact of fibrinolytic therapy on total cost of treatment in the 2 groups.

A randomized trial is planned to compare early video-assisted thoracoscopy vs treatment with chest tube drainage and t-PA-DNase.¹⁷

At our institution, we use a multidisciplinary approach, discussing cases at weekly meetings with thoracic surgeons, pulmonologists, infectious disease specialists, and interventional radiologists. We generally try conservative management first, with chest tube drainage and intrapleural agents for 5 to 7 days, before considering surgery if the response is unsatisfactory.

THE PATIENT RECOVERED

In our patient, the multiloculated empyema was successfully cleared after intrapleural instillation of 4 doses of tPA and DNAse over 3 days and insertion of a second intercostal chest tube into the noncommunicating apical locule. He completed 14 days of intravenous piperacillin-tazobactam treatment and, after discharge home, completed another 4 weeks of oral amoxicillin-clavulanate. He made a full recovery and was back at work 2 weeks after discharge. Chest radiography 10 weeks after discharge showed normal results.

A 33-year-old male nonsmoker with no significant medical history presented to the pulmonary clinic with severe left-sided pleuritic chest pain and mild breathlessness for the past 5 days. He denied fever, chills, cough, phlegm, runny nose, or congestion.

Five days before this visit, he had been seen in the emergency department with mild left-sided pleuritic chest pain. His vital signs at that time had been as follows:

• Blood pressure 141/77 mm Hg
• Heart rate 77 beats/minute
• Respiratory rate 17 breaths/minute
• Temperature 36.8°C (98.2°F)
• Oxygen saturation 98% on room air.

• White blood cell count 6.89 × 10⁹/L (reference range 3.70–11.00)
• Neutrophils 58% (40%–70%)
• Lymphocytes 29.6% (22%–44%)
• Monocytes 10.7% (0–11%)
• Eosinophils 1% (0–4%)
• Basophils 0.6% (0–1%)
• Troponin T and D-dimer levels normal.

DIFFERENTIAL DIAGNOSIS OF PLEURITIC CHEST PAIN

1. What is the most likely cause of his pleuritic chest pain?

• Pleuritis
• Pneumonia
• Pulmonary embolism
• Malignancy

The differential diagnosis of pleuritic chest pain is broad.

The patient’s symptoms at presentation to the emergency department did not suggest an infectious process. There was no fever, cough, or phlegm, and his white blood cell count was normal. Nonetheless, pneumonia could not be ruled out, as the lung parenchyma was not normal on radiography, and the findings could have been consistent with an early or resolving infectious process.

Pulmonary embolism was a possibility, but his normal D-dimer level argued against it. Further, the patient subsequently underwent CT angiography, which ruled out pulmonary embolism.

Malignancy was unlikely in a young nonsmoker, but follow-up imaging would be needed to ensure resolution and rule this out.

The emergency department physician diagnosed inflammatory pleuritis and discharged him home on a nonsteroidal anti-inflammatory drug.

CLINIC VISIT 5 DAYS LATER

At his pulmonary clinic visit 5 days later, the patient reported persistent but stable left-sided pleuritic chest pain and mild breathlessness on exertion. His blood pressure was 137/81 mm Hg, heart rate 109 beats per minute, temperature 37.1°C (98.8°F), and oxygen saturation 97% on room air.

Auscultation of the lungs revealed rales and slightly decreased breath sounds at the left base. No dullness to percussion could be detected.

Because the patient had developed mild tachycardia and breathlessness along with clinical signs that suggested worsening infiltrates, consolidation, or the development of pleural effusion, he underwent further investigation with chest radiography, a complete blood cell count, and measurement of serum inflammatory markers.

• White blood cell count 13.08 × 10⁹/L
• Neutrophils 81%
• Lymphocytes 7.4%
• Monocytes 7.2%
• Eeosinophils 0.2%
• Basophils 0.2%
• Procalcitonin 0.34 µg/L (reference range < 0.09).

Bedside ultrasonography to assess the effusion’s size and characteristics and the need for thoracentesis indicated that the effusion was too small to tap, and there were no fibrinous strands or loculations to suggest empyema.

2. What was the best management strategy for this patient at this time?

• Admit to the hospital for thoracentesis and intravenous antibiotics
• Give oral antibiotics with close follow-up
• Perform thoracentesis on an outpatient basis and give oral antibiotics
• Repeat chest CT

The patient had worsening pleuritic pain with development of a small left pleural effusion. His symptoms had not improved on a nonsteroidal anti-inflammatory drug. He now had an elevated white blood cell count with a “left shift” (ie, an increase in neutrophils, indicating more immature cells in circulation) and elevated procalcitonin. The most likely diagnosis was pneumonia with a resulting pleural effusion, ie, parapneumonic effusion, requiring appropriate antibiotic therapy. Ideally, the pleural effusion should be sampled by thoracentesis, with management on an outpatient or inpatient basis.

5 DAYS LATER, THE EFFUSION HAD BECOME MASSIVE

On follow-up 5 days later, the patient’s chest pain was better, but he was significantly more short of breath. His blood pressure was 137/90 mm Hg, heart rate 117 beats/minute, respiratory rate 16 breaths/minute, oxygen saturation 97% on room air, and temperature 36.9°C (98.4°F). Chest auscultation revealed decreased breath sounds over the left hemithorax, with dullness to percussion and decreased fremitus.

RAPIDLY PROGRESSIVE PLEURAL EFFUSIONS

A rapidly progressive pleural effusion in a healthy patient suggests parapneumonic effusion. The most likely organism is streptococcal.²

Explosive pleuritis is defined as a pleural effusion that increases in size in less than 24 hours. It was first described by Braman and Donat³ in 1986 as an effusion that develops within hours of admission. In 2001, Sharma and Marrie⁴ refined the definition as rapid development of pleural effusion involving more than 90% of the hemithorax within 24 hours, causing compression of pulmonary tissue and a mediastinal shift. It is a medical emergency that requires prompt investigation and treatment with drainage and antibiotics. All reported cases of explosive pleuritis have been parapneumonic effusion.

The organisms implicated in explosive pleuritis include gram-positive cocci such as Streptococcus pneumoniae, S pyogenes, other streptococci, staphylococci, and gram-negative cocci such as Neisseria meningitidis and Moraxella catarrhalis. Gram-negative bacilli include Haemophilus influenzae, Klebsiella pneumoniae, Pseudomonas species, Escherichia coli, Proteus species, Enterobacter species, Bacteroides species, and Legionella species.^4,5 However, malignancy is the most common cause of massive pleural effusion, accounting for 54% of cases; 17% of cases are idiopathic, 13% are parapneumonic, and 12% are hydrothorax related to liver cirrhosis.⁶

CASE CONTINUED

Our patient’s massive effusion needed drainage, and he was admitted to the hospital for further management. Samples of blood and sputum were sent for culture. Intravenous piperacillin-tazobactam was started, and an intercostal chest tube was inserted into the pleural cavity under ultrasonographic guidance to drain turbid fluid.

Multiple pleural fluid samples sent for bacterial, fungal, and acid-fast bacilli culture were negative. Blood and sputum cultures also showed no growth. The administration of oral antibiotics for 5 days on an outpatient basis before pleural fluid culture could have led to sterility of all cultures.

Our patient had inadequate pleural fluid output through his chest tube, and radiography showed that the pleural collections failed to clear. In fact, an apical locule did not appear to be connecting with the lower aspect of the pleural collection. In such cases, instillation of intrapleural agents through the chest tube has become common practice in an attempt to lyse adhesions, to connect various locules or pockets of pleural fluid, and to improve drainage.

3. What was the best management strategy for this loculated empyema?

• Continue intravenous antibiotics and existing chest tube drainage for 5 to 7 days, then reassess
• Continue intravenous antibiotics and instill intrapleural fibrinolytics (eg, tissue plasminogen activator [tPA]) through the existing chest tube
• Continue intravenous antibiotics and instill intrapleural fibrinolytics with deoxyribonuclease (DNase) into the existing chest tube
• Continue intravenous antibiotics, insert a second chest tube into the apical pocket under imaging guidance, and instill tPA and DNase
• Surgical decortication

Continuing antibiotics with existing chest tube drainage and the two options of using single-agent intrapleural fibrinolytics have been shown to be less effective than combining tPA and DNase when managing a loculated empyema. As such, surgical decortication, attempting intrapleural instillation of fibrinolytics and DNase (with or without further chest tube insertion into noncommunicating locules), or both were the most appropriate options at this stage.

MANAGEMENT OF PARAPNEUMONIC PLEURAL EFFUSION IN ADULTS

There are several options for managing parapneumonic effusion, and clinicians can use the classification system in Table 1 to assess the risk of a poor outcome and to plan the management. Based on radiographic findings and pleural fluid sampling, a pleural effusion can be either observed or drained.

Options for drainage of the pleural space include repeat thoracentesis, surgical insertion of a chest tube, or image-guided insertion of a small-bore catheter. Although no randomized trial has been done to compare tube sizes, a large retrospective series showed that small-bore tubes (< 14 F) perform similarly to standard large-bore tubes.⁸ However, in another study, Keeling et al⁹ reported higher failure rates when tubes smaller than 12 F were used. Regular flushing of the chest tube (ideally twice a day) is recommended to keep it patent, particularly with small-bore tubes. Multiloculated empyema may require multiple intercostal chest tubes to drain completely, and therefore small-bore tubes are recommended.

In cases that do not improve radiographically and clinically, one must consider whether the antibiotic choice is adequate, review the position of the chest tube, and assess for loculations. As such, repeating chest CT within 24 to 48 hours of tube insertion and drainage is recommended to confirm adequate tube positioning, assess effective drainage, look for different locules and pockets, and determine the degree of communication between them.

The largest well-powered randomized controlled trials of intrapleural agents in the management of pleural infection, the Multicentre Intrapleural Sepsis Trial (MIST1)¹⁰ and MIST2,¹¹ clearly demonstrated that intrapleural fibrinolytics were not beneficial when used alone compared with placebo. However, in MIST2, the combination of tPA and DNase led to clinically significant benefits including radiologic improvement, shorter hospital stay, and less need for surgical decortication.

At our hospital, we follow the MIST2 protocol using a combination of tPA and DNase given intrapleurally twice daily for 3 days. In our patient, we inserted a chest tube into the apical pocket under ultrasonographic guidance, as 2 instillations of intrapleural tPA and DNase did not result in drainage of the apical locule.

Success rates with intrapleural tPA-DNase for complicated pleural effusion and empyema range from 68% to 92%.^12–15 Pleural thickening and necrotizing pneumonia and abscess are important predictors of failure of tPA-DNase therapy and of the need for surgery.^13,14

Early surgical intervention was another reasonable option in this case. The decision to proceed with surgery is based on need to debride multiloculated empyemas or uniloculated empyemas that fail to resolve with antibiotics and tube thoracostomy drainage. Nonetheless, the decision must be individualized and based on factors such as the patient’s risks vs possible benefit from a surgical procedure under general anesthesia, the patient’s ability to tolerate multiple thoracentesis procedures and chest tubes for a potentially lengthy period, the patient’s pain threshold, the patient’s wishes to avoid a surgical procedure balanced against a longer hospital stay, and cultural norms and beliefs.

Surgical options include video-assisted thoracoscopy, thoracotomy, and open drainage. Decortication can be considered early to control pleural sepsis, or late (after 3 to 6 months) if the lung does not expand. Debate continues on the optimal timing for video-assisted thoracoscopy, with data suggesting that when the procedure is performed later in the course of the disease there is a greater chance of complications and of the need to convert to thoracotomy.

A 2017 Cochrane review¹⁶ of surgical vs nonsurgical management of empyema identified 8 randomized trials, 6 in children and 2 in adults, with a total of 391 patients. The authors compared video-assisted thoracoscopy vs tube thoracotomy, with and without intrapleural fibrinolytics. They noted no difference in rates of mortality or procedural complications. However, the mean length of hospital stay was shorter with video-assisted thoracoscopy than with tube thoracotomy (5.9 vs 15.4 days). They could not assess the impact of fibrinolytic therapy on total cost of treatment in the 2 groups.

A randomized trial is planned to compare early video-assisted thoracoscopy vs treatment with chest tube drainage and t-PA-DNase.¹⁷

At our institution, we use a multidisciplinary approach, discussing cases at weekly meetings with thoracic surgeons, pulmonologists, infectious disease specialists, and interventional radiologists. We generally try conservative management first, with chest tube drainage and intrapleural agents for 5 to 7 days, before considering surgery if the response is unsatisfactory.

THE PATIENT RECOVERED

In our patient, the multiloculated empyema was successfully cleared after intrapleural instillation of 4 doses of tPA and DNAse over 3 days and insertion of a second intercostal chest tube into the noncommunicating apical locule. He completed 14 days of intravenous piperacillin-tazobactam treatment and, after discharge home, completed another 4 weeks of oral amoxicillin-clavulanate. He made a full recovery and was back at work 2 weeks after discharge. Chest radiography 10 weeks after discharge showed normal results.

Many hospitalists incorporate point-of-care ultrasound (POCUS) into their daily practice because it adds value to their bedside evaluation of patients. However, standards for training and assessing hospitalists in POCUS have not yet been established. Other acute care specialties, including emergency medicine and critical care medicine, have already incorporated POCUS into their graduate medical education training programs, but most internal medicine residency programs are only beginning to provide POCUS training.¹

Several features distinguish POCUS from comprehensive ultrasound examinations. First, POCUS is designed to answer focused questions, whereas comprehensive ultrasound examinations evaluate all organs in an anatomical region; for example, an abdominal POCUS exam may evaluate only for presence or absence of intraperitoneal free fluid, whereas a comprehensive examination of the right upper quadrant will evaluate the liver, gallbladder, and biliary ducts. Second, POCUS examinations are generally performed by the same clinician who generates the relevant clinical question to answer with POCUS and ultimately integrates the findings into the patient’s care.² By contrast, comprehensive ultrasound examinations involve multiple providers and steps: a clinician generates a relevant clinical question and requests an ultrasound examination that is acquired by a sonographer, interpreted by a radiologist, and reported back to the requesting clinician. Third, POCUS is often used to evaluate multiple body systems. For example, to evaluate a patient with undifferentiated hypotension, a multisystem POCUS examination of the heart, inferior vena cava, lungs, abdomen, and lower extremity veins is typically performed. Finally, POCUS examinations can be performed serially to investigate changes in clinical status or evaluate response to therapy, such as monitoring the heart, lungs, and inferior vena cava during fluid resuscitation.

The purpose of this position statement is to inform a broad audience about how hospitalists are using diagnostic and procedural applications of POCUS. This position statement does not mandate that hospitalists use POCUS. Rather, it is intended to provide guidance on the safe and effective use of POCUS by the hospitalists who use it and the administrators who oversee its use. We discuss POCUS (1) applications, (2) training, (3) assessments, and (4) program management. This position statement was reviewed and approved by the Society of Hospital Medicine (SHM) Executive Committee in March 2018.

Common diagnostic and procedural applications of POCUS used by hospitalists are listed in Table 1. Selected evidence supporting the use of these applications is described in the supplementary online content (Appendices 1–8 available at http://journalofhospitalmedicine.com) and SHM position statements on specific ultrasound-guided bedside procedures.^3,4 Additional applications not listed in Table 1 that may be performed by some hospitalists include assessment of the eyes, stomach, bowels, ovaries, pregnancy, and testicles, as well as performance of regional anesthesia. Moreover, hospitalists caring for pediatric and adolescent patients may use additional applications besides those listed here. Currently, many hospitalists already perform more complex and sophisticated POCUS examinations than those listed in Table 1. The scope of POCUS use by hospitalists continues to expand, and this position statement should not restrict that expansion.

As outlined in our earlier position statements,^3,4 ultrasound guidance lowers complication rates and increases success rates of invasive bedside procedures. Diagnostic POCUS can guide clinical decision making prior to bedside procedures. For instance, hospitalists may use POCUS to assess the size and character of a pleural effusion to help determine the most appropriate management strategy: observation, medical treatment, thoracentesis, chest tube placement, or surgical therapy. Furthermore, diagnostic POCUS can be used to rapidly assess for immediate postprocedural complications, such as pneumothorax, or if the patient develops new symptoms.

Basic Knowledge

Basic knowledge includes fundamentals of ultrasound physics; safety;⁴ anatomy; physiology; and device operation, including maintenance and cleaning. Basic knowledge can be taught by multiple methods, including live or recorded lectures, online modules, or directed readings.

Image Acquisition

Training should occur across multiple types of patients (eg, obese, cachectic, postsurgical) and clinical settings (eg, intensive care unit, general medicine wards, emergency department) when available. Training is largely hands-on because the relevant skills involve integration of 3D anatomy with spatial manipulation, hand-eye coordination, and fine motor movements. Virtual reality ultrasound simulators may accelerate mastery, particularly for cardiac image acquisition, and expose learners to standardized sets of pathologic findings. Real-time bedside feedback on image acquisition is ideal because understanding how ultrasound probe manipulation affects the images acquired is essential to learning.

Image Interpretation

Training in image interpretation relies on visual pattern recognition of normal and abnormal findings. Therefore, the normal to abnormal spectrum should be broad, and learners should maintain a log of what abnormalities have been identified. Giving real-time feedback at the bedside is ideal because of the connection between image acquisition and interpretation. Image interpretation can be taught through didactic sessions, image review sessions, or review of teaching files with annotated images.

Clinical Integration

Learners must interpret and integrate image findings with other clinical data considering the image quality, patient characteristics, and changing physiology. Clinical integration should be taught by instructors that share similar clinical knowledge as learners. Although sonographers are well suited to teach image acquisition, they should not be the sole instructors to teach hospitalists how to integrate ultrasound findings in clinical decision making. Likewise, emphasis should be placed on the appropriate use of POCUS within a provider’s skill set. Learners must appreciate the clinical significance of POCUS findings, including recognition of incidental findings that may require further workup. Supplemental training in clinical integration can occur through didactics that include complex patient scenarios.

Pathways

Clinical competency can be achieved with training adherent to five criteria. First, the training environment should be similar to where the trainee will practice. Second, training and feedback should occur in real time. Third, specific applications should be taught rather than broad training in “hospitalist POCUS.” Each application requires unique skills and knowledge, including image acquisition pitfalls and artifacts. Fourth, clinical competence must be achieved and demonstrated; it is not necessarily gained through experience. Fifth, once competency is achieved, continued education and feedback are necessary to ensure it is maintained.

Residency-based POCUS training pathways can best fulfill these criteria. They may eventually become commonplace, but until then alternative pathways must exist for hospitalist providers who are already in practice. There are three important attributes of such pathways. First, administrators’ expectations about learners’ clinical productivity must be realistically, but only temporarily, relaxed; otherwise, competing demands on time will likely overwhelm learners and subvert training. Second, training should begin through a local or national hands-on training program. The SHM POCUS certificate program consolidates training for common diagnostic POCUS applications for hospitalists.⁶ Other medical societies offer training for their respective clinical specialties.⁷ Third, once basic POCUS training has begun, longitudinal training should continue ideally with a local hospitalist POCUS expert.

In some settings, a subgroup of hospitalists may not desire, or be able to achieve, competency in the manual skills of POCUS image acquisition. Nevertheless, hospitalists may still find value in understanding POCUS nomenclature, image pattern recognition, and the evidence and pitfalls behind clinical integration of specific POCUS findings. This subset of POCUS skills allows hospitalists to communicate effectively with and understand the clinical decisions made by their colleagues who are competent in POCUS use.

The minimal skills a hospitalist should possess to serve as a POCUS trainer include proficiency of basic knowledge, image acquisition, image interpretation, and clinical integration of the POCUS applications being taught; effectiveness as a hands-on instructor to teach image acquisition skills; and an in-depth understanding of common POCUS pitfalls and limitations.

Assessment methods for POCUS can include the following: knowledge-based questions, image acquisition using task-specific checklists on human or simulation models, image interpretation using a series of videos or still images with normal and abnormal findings, clinical integration using “next best step” in a multiple choice format with POCUS images, and simulation-based clinical scenarios. Assessment methods should be aligned with local availability of resources and trainers.

Basic Knowledge

Basic knowledge can be assessed via multiple choice questions assessing knowledge of ultrasound physics, image optimization, relevant anatomy, and limitations of POCUS imaging. Basic knowledge lies primarily in the cognitive domain and does not assess manual skills.

Image Acquisition

Image acquisition can be assessed by observation and rating of image quality. Where resources allow, assessment of image acquisition is likely best done through a combination of developing an image portfolio with a minimum number of high quality images, plus direct observation of image acquisition by an expert. Various programs have utilized minimum numbers of images acquired to help define competence with image acquisition skills.^6–8 Although minimums may be a necessary step to gain competence, using them as a sole means to determine competence does not account for variable learning curves.⁹ As with other manual skills in hospital medicine, such as ultrasound-guided bedside procedures, minimum numbers are best used as a starting point for assessments.^3,10 In this regard, portfolio development with meticulous attention to the gain, depth, and proper tomographic plane of images can monitor a hospitalist’s progress toward competence by providing objective assessments and feedback. Simulation may also be used as it allows assessment of image acquisition skills and an opportunity to provide real-time feedback, similar to direct observation but without actual patients.

Image Interpretation

Image interpretation is best assessed by an expert observing the learner at bedside; however, when bedside assessment is not possible, image interpretation skills may be assessed using multiple choice or free text interpretation of archived ultrasound images with normal and abnormal findings. This is often incorporated into the portfolio development portion of a training program, as learners can submit their image interpretation along with the video clip. Both normal and abnormal images can be used to assess anatomic recognition and interpretation. Emphasis should be placed on determining when an image is suboptimal for diagnosis (eg, incomplete exam or poor-quality images). Quality assurance programs should incorporate structured feedback sessions.

Clinical Integration

Assessment of clinical integration can be completed through case scenarios that assess knowledge, interpretation of images, and integration of findings into clinical decision making, which is often delivered via a computer-based assessment. Assessments should combine specific POCUS applications to evaluate common clinical problems in hospital medicine, such as undifferentiated hypotension and dyspnea. High-fidelity simulators can be used to blend clinical case scenarios with image acquisition, image interpretation, and clinical integration. When feasible, comprehensive feedback on how providers acquire, interpret, and apply ultrasound at the bedside is likely the best mechanism to assess clinical integration. This process can be done with a hospitalist’s own patients.

General Assessment

A general assessment that includes a summative knowledge and hands-on skills assessment using task-specific checklists can be performed upon completion of training. A high-fidelity simulator with dynamic or virtual anatomy can provide reproducible standardized assessments with variation in the type and difficulty of cases. When available, we encourage the use of dynamic assessments on actual patients that have both normal and abnormal ultrasound findings because simulated patient scenarios have limitations, even with the use of high-fidelity simulators. Programs are recommended to use formative and summative assessments for evaluation. Quantitative scoring systems using checklists are likely the best framework.^11,12

A certificate of completion is proof of a provider’s participation in an educational activity; it does not equate with competency, though it may be a step toward it. Most POCUS training workshops and short courses provide certificates of completion. Certification of competency is an attestation of a hospitalist’s basic competence within a defined scope of practice (Table 2).¹³ However, without longitudinal supervision and feedback, skills can decay; therefore, we recommend a longitudinal training program that provides mentored feedback and incorporates periodic competency assessments. At present, no national board certification in POCUS is available to grant external certification of competency for hospitalists.

External Certificate

Certificates of completion can be external through a national organization. An external certificate of completion designed for hospitalists includes the POCUS Certificate of Completion offered by SHM in collaboration with CHEST.⁶ This certificate program provides regional training options and longitudinal portfolio development. Other external certificates are also available to hospitalists.^7,14,15

Most hospitalists are boarded by the American Board of Internal Medicine or the American Board of Family Medicine. These boards do not yet include certification of competency in POCUS. Other specialty boards, such as emergency medicine, include competency in POCUS. For emergency medicine, completion of an accredited residency training program and certification by the national board includes POCUS competency.

Internal Certificate

There are a few examples of successful local institutional programs that have provided internal certificates of competency.^12,14 Competency assessments require significant resources including investment by both faculty and learners. Ongoing evaluation of competency should be based on quality assurance processes.

Credentialing and Privileging

The American Medical Association (AMA) House of Delegates in 1999 passed a resolution (AMA HR. 802) recommending hospitals follow specialty-specific guidelines for privileging decisions related to POCUS use.¹⁷ The resolution included a statement that, “ultrasound imaging is within the scope of practice of appropriately trained physicians.”

Some institutions have begun to rely on a combination of internal and external certificate programs to grant privileges to hospitalists.¹⁰ Although specific privileges for POCUS may not be required in some hospitals, some institutions may require certification of training and assessments prior to granting permission to use POCUS.

Hospitalist programs are encouraged to evaluate ongoing POCUS use by their providers after granting initial permission. If privileging is instituted by a hospital, hospitalists must play a significant role in determining the requirements for privileging and ongoing maintenance of skills.

Maintenance of Skills

All medical skills can decay with disuse, including those associated with POCUS.^12,18 Thus, POCUS users should continue using POCUS regularly in clinical practice and participate in POCUS continuing medical education activities, ideally with ongoing assessments. Maintenance of skills may be confirmed through routine participation in a quality assurance program.

Use of POCUS in hospital medicine has unique considerations, and hospitalists should be integrally involved in decision making surrounding institutional POCUS program management. Appointing a dedicated POCUS director can help a program succeed.⁸

Equipment and Image Archiving

Several factors are important to consider when selecting an ultrasound machine: portability, screen size, and ease of use; integration with the electronic medical record and options for image archiving; manufacturer’s service plan, including technical and clinical support; and compliance with local infection control policies. The ability to easily archive and retrieve images is essential for quality assurance, continuing education, institutional quality improvement, documentation, and reimbursement. In certain scenarios, image archiving may not be possible (such as with personal handheld devices or in emergency situations) or necessary (such as with frequent serial examinations during fluid resuscitation). An image archive is ideally linked to reports, orders, and billing software.^10,19 If such linkages are not feasible, parallel external storage that complies with regulatory standards (ie, HIPAA compliance) may be suitable.²⁰

Documentation and Billing

Components of documentation include the indication and type of ultrasound examination performed, date and time of the examination, patient identifying information, name of provider(s) acquiring and interpreting the images, specific scanning protocols used, patient position, probe used, and findings. Documentation can occur through a standalone note or as part of another note, such as a progress note. Whenever possible, documentation should be timely to facilitate communication with other providers.

Billing is supported through the AMA Current Procedural Terminology codes for “focused” or “limited” ultrasound examinations (Appendix 9). The following three criteria must be satisfied for billing. First, images must be permanently stored. Specific requirements vary by insurance policy, though current practice suggests a minimum of one image demonstrating relevant anatomy and pathology for the ultrasound examination coded. For ultrasound-guided procedures that require needle insertion, images should be captured at the point of interest, and a procedure note should reflect that the needle was guided and visualized under ultrasound.²¹ Second, proper documentation must be entered in the medical record. Third, local institutional privileges for POCUS must be considered. Although privileges are not required to bill, some hospitals or payers may require them.

Quality Assurance

Published guidelines on quality assurance in POCUS are available from different specialty organizations, including emergency medicine, pediatric emergency medicine, critical care, anesthesiology, obstetrics, and cardiology.^8,22–28 Quality assurance is aimed at ensuring that physicians maintain basic competency in using POCUS to influence bedside decisions.

Quality assurance should be carried out by an individual or committee with expertise in POCUS. Multidisciplinary QA programs in which hospital medicine providers are working collaboratively with other POCUS providers have been demonstrated to be highly effective.¹⁰ Oversight includes ensuring that providers using POCUS are appropriately trained,^10,22,28 using the equipment correctly,^8,26,28 and documenting properly. Some programs have implemented mechanisms to review and provide feedback on image acquisition, interpretation, and clinical integration.^8,10 Other programs have compared POCUS findings with referral studies, such as comprehensive ultrasound examinations.

Practicing hospitalists must continue to collaborate with their institutions to build POCUS capabilities. In particular, they must work with their local privileging body to determine what credentials are required. The distinction between certificates of completion and certificates of competency, including whether those certificates are internal or external, is important in the credentialing process.

External certificates of competency are currently unavailable for most practicing hospitalists because ABIM certification does not include POCUS-related competencies. As internal medicine residency training programs begin to adopt POCUS training and certification into their educational curricula, we foresee a need to update the ABIM Policies and Procedures for Certification. Until then, we recommend that certificates of competency be defined and granted internally by local hospitalist groups.

Given the many advantages of POCUS over traditional tools, we anticipate its increasing implementation among hospitalists in the future. As with all medical technology, its role in clinical care should be continuously reexamined and redefined through health services research. Such information will be useful in developing practice guidelines, educational curricula, and training standards.

Acknowledgments

The authors would like to thank all members that participated in the discussion and finalization of this position statement during the Point-of-care Ultrasound Faculty Retreat at the 2018 Society of Hospital Medicine Annual Conference: Saaid Abdel-Ghani, Brandon Boesch, Joel Cho, Ria Dancel, Renee Dversdal, Ricardo Franco-Sadud, Benjamin Galen, Trevor P. Jensen, Mohit Jindal, Gordon Johnson, Linda M. Kurian, Gigi Liu, Charles M. LoPresti, Brian P. Lucas, Venkat Kalidindi, Benji Matthews, Anna Maw, Gregory Mints, Kreegan Reierson, Gerard Salame, Richard Schildhouse, Daniel Schnobrich, Nilam Soni, Kirk Spencer, Hiromizu Takahashi, David M. Tierney, Tanping Wong, and Toru Yamada.

Many hospitalists incorporate point-of-care ultrasound (POCUS) into their daily practice because it adds value to their bedside evaluation of patients. However, standards for training and assessing hospitalists in POCUS have not yet been established. Other acute care specialties, including emergency medicine and critical care medicine, have already incorporated POCUS into their graduate medical education training programs, but most internal medicine residency programs are only beginning to provide POCUS training.¹

Several features distinguish POCUS from comprehensive ultrasound examinations. First, POCUS is designed to answer focused questions, whereas comprehensive ultrasound examinations evaluate all organs in an anatomical region; for example, an abdominal POCUS exam may evaluate only for presence or absence of intraperitoneal free fluid, whereas a comprehensive examination of the right upper quadrant will evaluate the liver, gallbladder, and biliary ducts. Second, POCUS examinations are generally performed by the same clinician who generates the relevant clinical question to answer with POCUS and ultimately integrates the findings into the patient’s care.² By contrast, comprehensive ultrasound examinations involve multiple providers and steps: a clinician generates a relevant clinical question and requests an ultrasound examination that is acquired by a sonographer, interpreted by a radiologist, and reported back to the requesting clinician. Third, POCUS is often used to evaluate multiple body systems. For example, to evaluate a patient with undifferentiated hypotension, a multisystem POCUS examination of the heart, inferior vena cava, lungs, abdomen, and lower extremity veins is typically performed. Finally, POCUS examinations can be performed serially to investigate changes in clinical status or evaluate response to therapy, such as monitoring the heart, lungs, and inferior vena cava during fluid resuscitation.

The purpose of this position statement is to inform a broad audience about how hospitalists are using diagnostic and procedural applications of POCUS. This position statement does not mandate that hospitalists use POCUS. Rather, it is intended to provide guidance on the safe and effective use of POCUS by the hospitalists who use it and the administrators who oversee its use. We discuss POCUS (1) applications, (2) training, (3) assessments, and (4) program management. This position statement was reviewed and approved by the Society of Hospital Medicine (SHM) Executive Committee in March 2018.

Common diagnostic and procedural applications of POCUS used by hospitalists are listed in Table 1. Selected evidence supporting the use of these applications is described in the supplementary online content (Appendices 1–8 available at http://journalofhospitalmedicine.com) and SHM position statements on specific ultrasound-guided bedside procedures.^3,4 Additional applications not listed in Table 1 that may be performed by some hospitalists include assessment of the eyes, stomach, bowels, ovaries, pregnancy, and testicles, as well as performance of regional anesthesia. Moreover, hospitalists caring for pediatric and adolescent patients may use additional applications besides those listed here. Currently, many hospitalists already perform more complex and sophisticated POCUS examinations than those listed in Table 1. The scope of POCUS use by hospitalists continues to expand, and this position statement should not restrict that expansion.

As outlined in our earlier position statements,^3,4 ultrasound guidance lowers complication rates and increases success rates of invasive bedside procedures. Diagnostic POCUS can guide clinical decision making prior to bedside procedures. For instance, hospitalists may use POCUS to assess the size and character of a pleural effusion to help determine the most appropriate management strategy: observation, medical treatment, thoracentesis, chest tube placement, or surgical therapy. Furthermore, diagnostic POCUS can be used to rapidly assess for immediate postprocedural complications, such as pneumothorax, or if the patient develops new symptoms.

Basic Knowledge

Basic knowledge includes fundamentals of ultrasound physics; safety;⁴ anatomy; physiology; and device operation, including maintenance and cleaning. Basic knowledge can be taught by multiple methods, including live or recorded lectures, online modules, or directed readings.

Image Acquisition

Training should occur across multiple types of patients (eg, obese, cachectic, postsurgical) and clinical settings (eg, intensive care unit, general medicine wards, emergency department) when available. Training is largely hands-on because the relevant skills involve integration of 3D anatomy with spatial manipulation, hand-eye coordination, and fine motor movements. Virtual reality ultrasound simulators may accelerate mastery, particularly for cardiac image acquisition, and expose learners to standardized sets of pathologic findings. Real-time bedside feedback on image acquisition is ideal because understanding how ultrasound probe manipulation affects the images acquired is essential to learning.

Image Interpretation

Training in image interpretation relies on visual pattern recognition of normal and abnormal findings. Therefore, the normal to abnormal spectrum should be broad, and learners should maintain a log of what abnormalities have been identified. Giving real-time feedback at the bedside is ideal because of the connection between image acquisition and interpretation. Image interpretation can be taught through didactic sessions, image review sessions, or review of teaching files with annotated images.

Clinical Integration

Learners must interpret and integrate image findings with other clinical data considering the image quality, patient characteristics, and changing physiology. Clinical integration should be taught by instructors that share similar clinical knowledge as learners. Although sonographers are well suited to teach image acquisition, they should not be the sole instructors to teach hospitalists how to integrate ultrasound findings in clinical decision making. Likewise, emphasis should be placed on the appropriate use of POCUS within a provider’s skill set. Learners must appreciate the clinical significance of POCUS findings, including recognition of incidental findings that may require further workup. Supplemental training in clinical integration can occur through didactics that include complex patient scenarios.

Pathways

Clinical competency can be achieved with training adherent to five criteria. First, the training environment should be similar to where the trainee will practice. Second, training and feedback should occur in real time. Third, specific applications should be taught rather than broad training in “hospitalist POCUS.” Each application requires unique skills and knowledge, including image acquisition pitfalls and artifacts. Fourth, clinical competence must be achieved and demonstrated; it is not necessarily gained through experience. Fifth, once competency is achieved, continued education and feedback are necessary to ensure it is maintained.

Residency-based POCUS training pathways can best fulfill these criteria. They may eventually become commonplace, but until then alternative pathways must exist for hospitalist providers who are already in practice. There are three important attributes of such pathways. First, administrators’ expectations about learners’ clinical productivity must be realistically, but only temporarily, relaxed; otherwise, competing demands on time will likely overwhelm learners and subvert training. Second, training should begin through a local or national hands-on training program. The SHM POCUS certificate program consolidates training for common diagnostic POCUS applications for hospitalists.⁶ Other medical societies offer training for their respective clinical specialties.⁷ Third, once basic POCUS training has begun, longitudinal training should continue ideally with a local hospitalist POCUS expert.

In some settings, a subgroup of hospitalists may not desire, or be able to achieve, competency in the manual skills of POCUS image acquisition. Nevertheless, hospitalists may still find value in understanding POCUS nomenclature, image pattern recognition, and the evidence and pitfalls behind clinical integration of specific POCUS findings. This subset of POCUS skills allows hospitalists to communicate effectively with and understand the clinical decisions made by their colleagues who are competent in POCUS use.

The minimal skills a hospitalist should possess to serve as a POCUS trainer include proficiency of basic knowledge, image acquisition, image interpretation, and clinical integration of the POCUS applications being taught; effectiveness as a hands-on instructor to teach image acquisition skills; and an in-depth understanding of common POCUS pitfalls and limitations.

Assessment methods for POCUS can include the following: knowledge-based questions, image acquisition using task-specific checklists on human or simulation models, image interpretation using a series of videos or still images with normal and abnormal findings, clinical integration using “next best step” in a multiple choice format with POCUS images, and simulation-based clinical scenarios. Assessment methods should be aligned with local availability of resources and trainers.

Basic Knowledge

Basic knowledge can be assessed via multiple choice questions assessing knowledge of ultrasound physics, image optimization, relevant anatomy, and limitations of POCUS imaging. Basic knowledge lies primarily in the cognitive domain and does not assess manual skills.

Image Acquisition

Image acquisition can be assessed by observation and rating of image quality. Where resources allow, assessment of image acquisition is likely best done through a combination of developing an image portfolio with a minimum number of high quality images, plus direct observation of image acquisition by an expert. Various programs have utilized minimum numbers of images acquired to help define competence with image acquisition skills.^6–8 Although minimums may be a necessary step to gain competence, using them as a sole means to determine competence does not account for variable learning curves.⁹ As with other manual skills in hospital medicine, such as ultrasound-guided bedside procedures, minimum numbers are best used as a starting point for assessments.^3,10 In this regard, portfolio development with meticulous attention to the gain, depth, and proper tomographic plane of images can monitor a hospitalist’s progress toward competence by providing objective assessments and feedback. Simulation may also be used as it allows assessment of image acquisition skills and an opportunity to provide real-time feedback, similar to direct observation but without actual patients.

Image Interpretation

Image interpretation is best assessed by an expert observing the learner at bedside; however, when bedside assessment is not possible, image interpretation skills may be assessed using multiple choice or free text interpretation of archived ultrasound images with normal and abnormal findings. This is often incorporated into the portfolio development portion of a training program, as learners can submit their image interpretation along with the video clip. Both normal and abnormal images can be used to assess anatomic recognition and interpretation. Emphasis should be placed on determining when an image is suboptimal for diagnosis (eg, incomplete exam or poor-quality images). Quality assurance programs should incorporate structured feedback sessions.

Clinical Integration

Assessment of clinical integration can be completed through case scenarios that assess knowledge, interpretation of images, and integration of findings into clinical decision making, which is often delivered via a computer-based assessment. Assessments should combine specific POCUS applications to evaluate common clinical problems in hospital medicine, such as undifferentiated hypotension and dyspnea. High-fidelity simulators can be used to blend clinical case scenarios with image acquisition, image interpretation, and clinical integration. When feasible, comprehensive feedback on how providers acquire, interpret, and apply ultrasound at the bedside is likely the best mechanism to assess clinical integration. This process can be done with a hospitalist’s own patients.

General Assessment

A general assessment that includes a summative knowledge and hands-on skills assessment using task-specific checklists can be performed upon completion of training. A high-fidelity simulator with dynamic or virtual anatomy can provide reproducible standardized assessments with variation in the type and difficulty of cases. When available, we encourage the use of dynamic assessments on actual patients that have both normal and abnormal ultrasound findings because simulated patient scenarios have limitations, even with the use of high-fidelity simulators. Programs are recommended to use formative and summative assessments for evaluation. Quantitative scoring systems using checklists are likely the best framework.^11,12

A certificate of completion is proof of a provider’s participation in an educational activity; it does not equate with competency, though it may be a step toward it. Most POCUS training workshops and short courses provide certificates of completion. Certification of competency is an attestation of a hospitalist’s basic competence within a defined scope of practice (Table 2).¹³ However, without longitudinal supervision and feedback, skills can decay; therefore, we recommend a longitudinal training program that provides mentored feedback and incorporates periodic competency assessments. At present, no national board certification in POCUS is available to grant external certification of competency for hospitalists.

External Certificate

Certificates of completion can be external through a national organization. An external certificate of completion designed for hospitalists includes the POCUS Certificate of Completion offered by SHM in collaboration with CHEST.⁶ This certificate program provides regional training options and longitudinal portfolio development. Other external certificates are also available to hospitalists.^7,14,15

Most hospitalists are boarded by the American Board of Internal Medicine or the American Board of Family Medicine. These boards do not yet include certification of competency in POCUS. Other specialty boards, such as emergency medicine, include competency in POCUS. For emergency medicine, completion of an accredited residency training program and certification by the national board includes POCUS competency.

Internal Certificate

There are a few examples of successful local institutional programs that have provided internal certificates of competency.^12,14 Competency assessments require significant resources including investment by both faculty and learners. Ongoing evaluation of competency should be based on quality assurance processes.

Credentialing and Privileging

The American Medical Association (AMA) House of Delegates in 1999 passed a resolution (AMA HR. 802) recommending hospitals follow specialty-specific guidelines for privileging decisions related to POCUS use.¹⁷ The resolution included a statement that, “ultrasound imaging is within the scope of practice of appropriately trained physicians.”

Some institutions have begun to rely on a combination of internal and external certificate programs to grant privileges to hospitalists.¹⁰ Although specific privileges for POCUS may not be required in some hospitals, some institutions may require certification of training and assessments prior to granting permission to use POCUS.

Hospitalist programs are encouraged to evaluate ongoing POCUS use by their providers after granting initial permission. If privileging is instituted by a hospital, hospitalists must play a significant role in determining the requirements for privileging and ongoing maintenance of skills.

Maintenance of Skills

All medical skills can decay with disuse, including those associated with POCUS.^12,18 Thus, POCUS users should continue using POCUS regularly in clinical practice and participate in POCUS continuing medical education activities, ideally with ongoing assessments. Maintenance of skills may be confirmed through routine participation in a quality assurance program.

Use of POCUS in hospital medicine has unique considerations, and hospitalists should be integrally involved in decision making surrounding institutional POCUS program management. Appointing a dedicated POCUS director can help a program succeed.⁸

Equipment and Image Archiving

Several factors are important to consider when selecting an ultrasound machine: portability, screen size, and ease of use; integration with the electronic medical record and options for image archiving; manufacturer’s service plan, including technical and clinical support; and compliance with local infection control policies. The ability to easily archive and retrieve images is essential for quality assurance, continuing education, institutional quality improvement, documentation, and reimbursement. In certain scenarios, image archiving may not be possible (such as with personal handheld devices or in emergency situations) or necessary (such as with frequent serial examinations during fluid resuscitation). An image archive is ideally linked to reports, orders, and billing software.^10,19 If such linkages are not feasible, parallel external storage that complies with regulatory standards (ie, HIPAA compliance) may be suitable.²⁰

Documentation and Billing

Components of documentation include the indication and type of ultrasound examination performed, date and time of the examination, patient identifying information, name of provider(s) acquiring and interpreting the images, specific scanning protocols used, patient position, probe used, and findings. Documentation can occur through a standalone note or as part of another note, such as a progress note. Whenever possible, documentation should be timely to facilitate communication with other providers.

Billing is supported through the AMA Current Procedural Terminology codes for “focused” or “limited” ultrasound examinations (Appendix 9). The following three criteria must be satisfied for billing. First, images must be permanently stored. Specific requirements vary by insurance policy, though current practice suggests a minimum of one image demonstrating relevant anatomy and pathology for the ultrasound examination coded. For ultrasound-guided procedures that require needle insertion, images should be captured at the point of interest, and a procedure note should reflect that the needle was guided and visualized under ultrasound.²¹ Second, proper documentation must be entered in the medical record. Third, local institutional privileges for POCUS must be considered. Although privileges are not required to bill, some hospitals or payers may require them.

Quality Assurance

Published guidelines on quality assurance in POCUS are available from different specialty organizations, including emergency medicine, pediatric emergency medicine, critical care, anesthesiology, obstetrics, and cardiology.^8,22–28 Quality assurance is aimed at ensuring that physicians maintain basic competency in using POCUS to influence bedside decisions.

Quality assurance should be carried out by an individual or committee with expertise in POCUS. Multidisciplinary QA programs in which hospital medicine providers are working collaboratively with other POCUS providers have been demonstrated to be highly effective.¹⁰ Oversight includes ensuring that providers using POCUS are appropriately trained,^10,22,28 using the equipment correctly,^8,26,28 and documenting properly. Some programs have implemented mechanisms to review and provide feedback on image acquisition, interpretation, and clinical integration.^8,10 Other programs have compared POCUS findings with referral studies, such as comprehensive ultrasound examinations.

Practicing hospitalists must continue to collaborate with their institutions to build POCUS capabilities. In particular, they must work with their local privileging body to determine what credentials are required. The distinction between certificates of completion and certificates of competency, including whether those certificates are internal or external, is important in the credentialing process.

External certificates of competency are currently unavailable for most practicing hospitalists because ABIM certification does not include POCUS-related competencies. As internal medicine residency training programs begin to adopt POCUS training and certification into their educational curricula, we foresee a need to update the ABIM Policies and Procedures for Certification. Until then, we recommend that certificates of competency be defined and granted internally by local hospitalist groups.

Given the many advantages of POCUS over traditional tools, we anticipate its increasing implementation among hospitalists in the future. As with all medical technology, its role in clinical care should be continuously reexamined and redefined through health services research. Such information will be useful in developing practice guidelines, educational curricula, and training standards.

Acknowledgments

The authors would like to thank all members that participated in the discussion and finalization of this position statement during the Point-of-care Ultrasound Faculty Retreat at the 2018 Society of Hospital Medicine Annual Conference: Saaid Abdel-Ghani, Brandon Boesch, Joel Cho, Ria Dancel, Renee Dversdal, Ricardo Franco-Sadud, Benjamin Galen, Trevor P. Jensen, Mohit Jindal, Gordon Johnson, Linda M. Kurian, Gigi Liu, Charles M. LoPresti, Brian P. Lucas, Venkat Kalidindi, Benji Matthews, Anna Maw, Gregory Mints, Kreegan Reierson, Gerard Salame, Richard Schildhouse, Daniel Schnobrich, Nilam Soni, Kirk Spencer, Hiromizu Takahashi, David M. Tierney, Tanping Wong, and Toru Yamada.

Many hospitalists incorporate point-of-care ultrasound (POCUS) into their daily practice because it adds value to their bedside evaluation of patients. However, standards for training and assessing hospitalists in POCUS have not yet been established. Other acute care specialties, including emergency medicine and critical care medicine, have already incorporated POCUS into their graduate medical education training programs, but most internal medicine residency programs are only beginning to provide POCUS training.¹

Several features distinguish POCUS from comprehensive ultrasound examinations. First, POCUS is designed to answer focused questions, whereas comprehensive ultrasound examinations evaluate all organs in an anatomical region; for example, an abdominal POCUS exam may evaluate only for presence or absence of intraperitoneal free fluid, whereas a comprehensive examination of the right upper quadrant will evaluate the liver, gallbladder, and biliary ducts. Second, POCUS examinations are generally performed by the same clinician who generates the relevant clinical question to answer with POCUS and ultimately integrates the findings into the patient’s care.² By contrast, comprehensive ultrasound examinations involve multiple providers and steps: a clinician generates a relevant clinical question and requests an ultrasound examination that is acquired by a sonographer, interpreted by a radiologist, and reported back to the requesting clinician. Third, POCUS is often used to evaluate multiple body systems. For example, to evaluate a patient with undifferentiated hypotension, a multisystem POCUS examination of the heart, inferior vena cava, lungs, abdomen, and lower extremity veins is typically performed. Finally, POCUS examinations can be performed serially to investigate changes in clinical status or evaluate response to therapy, such as monitoring the heart, lungs, and inferior vena cava during fluid resuscitation.

The purpose of this position statement is to inform a broad audience about how hospitalists are using diagnostic and procedural applications of POCUS. This position statement does not mandate that hospitalists use POCUS. Rather, it is intended to provide guidance on the safe and effective use of POCUS by the hospitalists who use it and the administrators who oversee its use. We discuss POCUS (1) applications, (2) training, (3) assessments, and (4) program management. This position statement was reviewed and approved by the Society of Hospital Medicine (SHM) Executive Committee in March 2018.

Common diagnostic and procedural applications of POCUS used by hospitalists are listed in Table 1. Selected evidence supporting the use of these applications is described in the supplementary online content (Appendices 1–8 available at http://journalofhospitalmedicine.com) and SHM position statements on specific ultrasound-guided bedside procedures.^3,4 Additional applications not listed in Table 1 that may be performed by some hospitalists include assessment of the eyes, stomach, bowels, ovaries, pregnancy, and testicles, as well as performance of regional anesthesia. Moreover, hospitalists caring for pediatric and adolescent patients may use additional applications besides those listed here. Currently, many hospitalists already perform more complex and sophisticated POCUS examinations than those listed in Table 1. The scope of POCUS use by hospitalists continues to expand, and this position statement should not restrict that expansion.

As outlined in our earlier position statements,^3,4 ultrasound guidance lowers complication rates and increases success rates of invasive bedside procedures. Diagnostic POCUS can guide clinical decision making prior to bedside procedures. For instance, hospitalists may use POCUS to assess the size and character of a pleural effusion to help determine the most appropriate management strategy: observation, medical treatment, thoracentesis, chest tube placement, or surgical therapy. Furthermore, diagnostic POCUS can be used to rapidly assess for immediate postprocedural complications, such as pneumothorax, or if the patient develops new symptoms.

Basic Knowledge

Basic knowledge includes fundamentals of ultrasound physics; safety;⁴ anatomy; physiology; and device operation, including maintenance and cleaning. Basic knowledge can be taught by multiple methods, including live or recorded lectures, online modules, or directed readings.

Image Acquisition

Training should occur across multiple types of patients (eg, obese, cachectic, postsurgical) and clinical settings (eg, intensive care unit, general medicine wards, emergency department) when available. Training is largely hands-on because the relevant skills involve integration of 3D anatomy with spatial manipulation, hand-eye coordination, and fine motor movements. Virtual reality ultrasound simulators may accelerate mastery, particularly for cardiac image acquisition, and expose learners to standardized sets of pathologic findings. Real-time bedside feedback on image acquisition is ideal because understanding how ultrasound probe manipulation affects the images acquired is essential to learning.

Image Interpretation

Training in image interpretation relies on visual pattern recognition of normal and abnormal findings. Therefore, the normal to abnormal spectrum should be broad, and learners should maintain a log of what abnormalities have been identified. Giving real-time feedback at the bedside is ideal because of the connection between image acquisition and interpretation. Image interpretation can be taught through didactic sessions, image review sessions, or review of teaching files with annotated images.

Clinical Integration

Learners must interpret and integrate image findings with other clinical data considering the image quality, patient characteristics, and changing physiology. Clinical integration should be taught by instructors that share similar clinical knowledge as learners. Although sonographers are well suited to teach image acquisition, they should not be the sole instructors to teach hospitalists how to integrate ultrasound findings in clinical decision making. Likewise, emphasis should be placed on the appropriate use of POCUS within a provider’s skill set. Learners must appreciate the clinical significance of POCUS findings, including recognition of incidental findings that may require further workup. Supplemental training in clinical integration can occur through didactics that include complex patient scenarios.

Pathways

Clinical competency can be achieved with training adherent to five criteria. First, the training environment should be similar to where the trainee will practice. Second, training and feedback should occur in real time. Third, specific applications should be taught rather than broad training in “hospitalist POCUS.” Each application requires unique skills and knowledge, including image acquisition pitfalls and artifacts. Fourth, clinical competence must be achieved and demonstrated; it is not necessarily gained through experience. Fifth, once competency is achieved, continued education and feedback are necessary to ensure it is maintained.

Residency-based POCUS training pathways can best fulfill these criteria. They may eventually become commonplace, but until then alternative pathways must exist for hospitalist providers who are already in practice. There are three important attributes of such pathways. First, administrators’ expectations about learners’ clinical productivity must be realistically, but only temporarily, relaxed; otherwise, competing demands on time will likely overwhelm learners and subvert training. Second, training should begin through a local or national hands-on training program. The SHM POCUS certificate program consolidates training for common diagnostic POCUS applications for hospitalists.⁶ Other medical societies offer training for their respective clinical specialties.⁷ Third, once basic POCUS training has begun, longitudinal training should continue ideally with a local hospitalist POCUS expert.

In some settings, a subgroup of hospitalists may not desire, or be able to achieve, competency in the manual skills of POCUS image acquisition. Nevertheless, hospitalists may still find value in understanding POCUS nomenclature, image pattern recognition, and the evidence and pitfalls behind clinical integration of specific POCUS findings. This subset of POCUS skills allows hospitalists to communicate effectively with and understand the clinical decisions made by their colleagues who are competent in POCUS use.

The minimal skills a hospitalist should possess to serve as a POCUS trainer include proficiency of basic knowledge, image acquisition, image interpretation, and clinical integration of the POCUS applications being taught; effectiveness as a hands-on instructor to teach image acquisition skills; and an in-depth understanding of common POCUS pitfalls and limitations.

Assessment methods for POCUS can include the following: knowledge-based questions, image acquisition using task-specific checklists on human or simulation models, image interpretation using a series of videos or still images with normal and abnormal findings, clinical integration using “next best step” in a multiple choice format with POCUS images, and simulation-based clinical scenarios. Assessment methods should be aligned with local availability of resources and trainers.

Basic Knowledge

Basic knowledge can be assessed via multiple choice questions assessing knowledge of ultrasound physics, image optimization, relevant anatomy, and limitations of POCUS imaging. Basic knowledge lies primarily in the cognitive domain and does not assess manual skills.

Image Acquisition

Image acquisition can be assessed by observation and rating of image quality. Where resources allow, assessment of image acquisition is likely best done through a combination of developing an image portfolio with a minimum number of high quality images, plus direct observation of image acquisition by an expert. Various programs have utilized minimum numbers of images acquired to help define competence with image acquisition skills.^6–8 Although minimums may be a necessary step to gain competence, using them as a sole means to determine competence does not account for variable learning curves.⁹ As with other manual skills in hospital medicine, such as ultrasound-guided bedside procedures, minimum numbers are best used as a starting point for assessments.^3,10 In this regard, portfolio development with meticulous attention to the gain, depth, and proper tomographic plane of images can monitor a hospitalist’s progress toward competence by providing objective assessments and feedback. Simulation may also be used as it allows assessment of image acquisition skills and an opportunity to provide real-time feedback, similar to direct observation but without actual patients.

Image Interpretation

Image interpretation is best assessed by an expert observing the learner at bedside; however, when bedside assessment is not possible, image interpretation skills may be assessed using multiple choice or free text interpretation of archived ultrasound images with normal and abnormal findings. This is often incorporated into the portfolio development portion of a training program, as learners can submit their image interpretation along with the video clip. Both normal and abnormal images can be used to assess anatomic recognition and interpretation. Emphasis should be placed on determining when an image is suboptimal for diagnosis (eg, incomplete exam or poor-quality images). Quality assurance programs should incorporate structured feedback sessions.

Clinical Integration

Assessment of clinical integration can be completed through case scenarios that assess knowledge, interpretation of images, and integration of findings into clinical decision making, which is often delivered via a computer-based assessment. Assessments should combine specific POCUS applications to evaluate common clinical problems in hospital medicine, such as undifferentiated hypotension and dyspnea. High-fidelity simulators can be used to blend clinical case scenarios with image acquisition, image interpretation, and clinical integration. When feasible, comprehensive feedback on how providers acquire, interpret, and apply ultrasound at the bedside is likely the best mechanism to assess clinical integration. This process can be done with a hospitalist’s own patients.

General Assessment

A general assessment that includes a summative knowledge and hands-on skills assessment using task-specific checklists can be performed upon completion of training. A high-fidelity simulator with dynamic or virtual anatomy can provide reproducible standardized assessments with variation in the type and difficulty of cases. When available, we encourage the use of dynamic assessments on actual patients that have both normal and abnormal ultrasound findings because simulated patient scenarios have limitations, even with the use of high-fidelity simulators. Programs are recommended to use formative and summative assessments for evaluation. Quantitative scoring systems using checklists are likely the best framework.^11,12

A certificate of completion is proof of a provider’s participation in an educational activity; it does not equate with competency, though it may be a step toward it. Most POCUS training workshops and short courses provide certificates of completion. Certification of competency is an attestation of a hospitalist’s basic competence within a defined scope of practice (Table 2).¹³ However, without longitudinal supervision and feedback, skills can decay; therefore, we recommend a longitudinal training program that provides mentored feedback and incorporates periodic competency assessments. At present, no national board certification in POCUS is available to grant external certification of competency for hospitalists.

External Certificate

Certificates of completion can be external through a national organization. An external certificate of completion designed for hospitalists includes the POCUS Certificate of Completion offered by SHM in collaboration with CHEST.⁶ This certificate program provides regional training options and longitudinal portfolio development. Other external certificates are also available to hospitalists.^7,14,15

Most hospitalists are boarded by the American Board of Internal Medicine or the American Board of Family Medicine. These boards do not yet include certification of competency in POCUS. Other specialty boards, such as emergency medicine, include competency in POCUS. For emergency medicine, completion of an accredited residency training program and certification by the national board includes POCUS competency.

Internal Certificate

There are a few examples of successful local institutional programs that have provided internal certificates of competency.^12,14 Competency assessments require significant resources including investment by both faculty and learners. Ongoing evaluation of competency should be based on quality assurance processes.

Credentialing and Privileging

The American Medical Association (AMA) House of Delegates in 1999 passed a resolution (AMA HR. 802) recommending hospitals follow specialty-specific guidelines for privileging decisions related to POCUS use.¹⁷ The resolution included a statement that, “ultrasound imaging is within the scope of practice of appropriately trained physicians.”

Some institutions have begun to rely on a combination of internal and external certificate programs to grant privileges to hospitalists.¹⁰ Although specific privileges for POCUS may not be required in some hospitals, some institutions may require certification of training and assessments prior to granting permission to use POCUS.

Hospitalist programs are encouraged to evaluate ongoing POCUS use by their providers after granting initial permission. If privileging is instituted by a hospital, hospitalists must play a significant role in determining the requirements for privileging and ongoing maintenance of skills.

Maintenance of Skills

All medical skills can decay with disuse, including those associated with POCUS.^12,18 Thus, POCUS users should continue using POCUS regularly in clinical practice and participate in POCUS continuing medical education activities, ideally with ongoing assessments. Maintenance of skills may be confirmed through routine participation in a quality assurance program.

Use of POCUS in hospital medicine has unique considerations, and hospitalists should be integrally involved in decision making surrounding institutional POCUS program management. Appointing a dedicated POCUS director can help a program succeed.⁸

Equipment and Image Archiving

Several factors are important to consider when selecting an ultrasound machine: portability, screen size, and ease of use; integration with the electronic medical record and options for image archiving; manufacturer’s service plan, including technical and clinical support; and compliance with local infection control policies. The ability to easily archive and retrieve images is essential for quality assurance, continuing education, institutional quality improvement, documentation, and reimbursement. In certain scenarios, image archiving may not be possible (such as with personal handheld devices or in emergency situations) or necessary (such as with frequent serial examinations during fluid resuscitation). An image archive is ideally linked to reports, orders, and billing software.^10,19 If such linkages are not feasible, parallel external storage that complies with regulatory standards (ie, HIPAA compliance) may be suitable.²⁰

Documentation and Billing

Components of documentation include the indication and type of ultrasound examination performed, date and time of the examination, patient identifying information, name of provider(s) acquiring and interpreting the images, specific scanning protocols used, patient position, probe used, and findings. Documentation can occur through a standalone note or as part of another note, such as a progress note. Whenever possible, documentation should be timely to facilitate communication with other providers.

Billing is supported through the AMA Current Procedural Terminology codes for “focused” or “limited” ultrasound examinations (Appendix 9). The following three criteria must be satisfied for billing. First, images must be permanently stored. Specific requirements vary by insurance policy, though current practice suggests a minimum of one image demonstrating relevant anatomy and pathology for the ultrasound examination coded. For ultrasound-guided procedures that require needle insertion, images should be captured at the point of interest, and a procedure note should reflect that the needle was guided and visualized under ultrasound.²¹ Second, proper documentation must be entered in the medical record. Third, local institutional privileges for POCUS must be considered. Although privileges are not required to bill, some hospitals or payers may require them.

Quality Assurance

Published guidelines on quality assurance in POCUS are available from different specialty organizations, including emergency medicine, pediatric emergency medicine, critical care, anesthesiology, obstetrics, and cardiology.^8,22–28 Quality assurance is aimed at ensuring that physicians maintain basic competency in using POCUS to influence bedside decisions.

Quality assurance should be carried out by an individual or committee with expertise in POCUS. Multidisciplinary QA programs in which hospital medicine providers are working collaboratively with other POCUS providers have been demonstrated to be highly effective.¹⁰ Oversight includes ensuring that providers using POCUS are appropriately trained,^10,22,28 using the equipment correctly,^8,26,28 and documenting properly. Some programs have implemented mechanisms to review and provide feedback on image acquisition, interpretation, and clinical integration.^8,10 Other programs have compared POCUS findings with referral studies, such as comprehensive ultrasound examinations.

Practicing hospitalists must continue to collaborate with their institutions to build POCUS capabilities. In particular, they must work with their local privileging body to determine what credentials are required. The distinction between certificates of completion and certificates of competency, including whether those certificates are internal or external, is important in the credentialing process.

External certificates of competency are currently unavailable for most practicing hospitalists because ABIM certification does not include POCUS-related competencies. As internal medicine residency training programs begin to adopt POCUS training and certification into their educational curricula, we foresee a need to update the ABIM Policies and Procedures for Certification. Until then, we recommend that certificates of competency be defined and granted internally by local hospitalist groups.

Given the many advantages of POCUS over traditional tools, we anticipate its increasing implementation among hospitalists in the future. As with all medical technology, its role in clinical care should be continuously reexamined and redefined through health services research. Such information will be useful in developing practice guidelines, educational curricula, and training standards.

Acknowledgments

The authors would like to thank all members that participated in the discussion and finalization of this position statement during the Point-of-care Ultrasound Faculty Retreat at the 2018 Society of Hospital Medicine Annual Conference: Saaid Abdel-Ghani, Brandon Boesch, Joel Cho, Ria Dancel, Renee Dversdal, Ricardo Franco-Sadud, Benjamin Galen, Trevor P. Jensen, Mohit Jindal, Gordon Johnson, Linda M. Kurian, Gigi Liu, Charles M. LoPresti, Brian P. Lucas, Venkat Kalidindi, Benji Matthews, Anna Maw, Gregory Mints, Kreegan Reierson, Gerard Salame, Richard Schildhouse, Daniel Schnobrich, Nilam Soni, Kirk Spencer, Hiromizu Takahashi, David M. Tierney, Tanping Wong, and Toru Yamada.

Abdominal paracentesis is a common and increasingly performed procedure in the United States. According to Medicare Physician Supplier Procedure Summary Master Files, an estimated 150,000 paracenteses were performed on Medicare fee-for-service beneficiaries in 2008 alone; such a number represents more than a two-fold increase from the same service population in 1993.¹ This increasing trend was again noted by the Nationwide Inpatient Sample data, which identified a 10% increase in hospitalized patients with a diagnosis of cirrhosis receiving paracentesis from 2004 (50%) to 2012 (61%; P < .0001).²

Although these data demonstrate that paracentesis is being performed frequently, paracentesis may be underutilized in hospitalized cirrhotics with ascites. In addition, in-hospital mortality of cirrhotics with ascites is higher among those who do not undergo paracentesis than among those who do (9% vs 6%; P = .03).^3,4

While complications associated with paracentesis are rare, serious complications, including death, have been documented.^5-10 The most common serious complication of paracentesis is bleeding, although puncture of the bowel and other abdominal organs has also been observed. Over the past few decades, ultrasound has been increasingly used with paracentesis due to the ability of ultrasound to improve detection of ascites^11,12 and to avoid blood vessels^10,13-15 and bowels.¹⁶

Three-quarters of all paracenteses are currently performed by interventional radiologists.¹ However, paracenteses are often required off-hours,¹⁷ when interventional radiologists are less readily available. Weekend admissions have less frequent performance of early paracentesis than weekday admissions, and delaying paracentesis may increase mortality.^3,18 High proficiency in ultrasound-guided paracentesis is achievable by nonradiologists^19-28 with equal or better patient outcomes after appropriate training.²⁹

The purpose of this guideline is to review the literature and present evidence-based recommendations on the performance of ultrasound-guided paracentesis at the bedside by practicing hospitalists.

Detailed methods are described in Appendix 1. The Society of Hospital Medicine (SHM) Point-of-care Ultrasound (POCUS) Task Force was assembled to carry out this guideline development project under the direction of the SHM Board of Directors, Director of Education, and Education Committee. All expert panel members were physicians or advanced-practice providers with expertise in POCUS. Expert panel members were divided into working group members, external peer reviewers, and a methodologist, and all Task Force members were required to disclose any potential conflicts of interests (Appendix 2). The literature search was conducted in two independent phases. The first phase included literature searches conducted by the five working group members themselves. Key clinical questions and draft recommendations were then prepared, and a systematic literature search was conducted by a medical librarian based on the findings of the initial literature search and draft recommendations. The Medline, Embase, CINAHL, and Cochrane medical databases were initially searched from 1975 to October 2015. Google Scholar was also searched without limiters. An updated search was conducted from November 2015 to November 2017, search strings for which are included in Appendix 3. All article abstracts were first screened for relevance by at least two members of the working group. Full-text versions of screened articles were reviewed and articles on ultrasound guidance for paracentesis were selected. The following article types were excluded: non-English language, nonhuman, age <18 years, meeting abstracts, meeting posters, letters, and editorials. All relevant systematic reviews, meta-analyses, randomized controlled trials, and observational studies of ultrasound-guided paracentesis were screened and selected. Final article selection was based on working group consensus. The selected literature was incorporated into the draft recommendations.

We used the RAND Appropriateness Method that required panel judgment and consensus to establish recommendations.³⁰ The voting members of the SHM POCUS Task Force reviewed and voted on the draft recommendations considering five transforming factors: (1) problem priority and importance; (2) level of quality of evidence; (3) benefit/harm balance; (4) benefit/burden balance; and (5) certainty/concerns about preferences/equity acceptability/feasibility. Panel members participated in two rounds of electronic voting using an internet-based electronic data collection tool (Redcap™) during February 2018 and April 2018 (Appendix 4) and voting on appropriateness was conducted using a 9-point Likert scale. The three zones based on the 9-point Likert scale were inappropriate (1-3 points), uncertain (4-6 points), and appropriate (7-9 points), and the degree of consensus was assessed using the RAND algorithm (Appendix 1, Figure 1, and Table 1). Establishing a recommendation required at least 70% agreement that a recommendation was “appropriate.” A strong recommendation required 80% of the votes within one integer of the median, following RAND rules, and disagreement was defined as >30% of panelists voting outside of the zone of the median.

Literature search

A total of 794 references were pooled and screened from literature searches conducted by a certified medical librarian in October 2015 (604 citations) and updated in November 2017 (118 citations), and working group members’ personal bibliographies and searches (72 citations; Appendix 3, Figure 2). Final selection included 91 articles that were abstracted into a data table and incorporated into the draft recommendations.

Four domains (terminology, clinical outcomes, technique, and training) with 13 draft recommendations were generated based on the literature review by the paracentesis working group. After two rounds of panel voting, one recommendation did not achieve consensus based on the RAND rules, and 12 statements received final approval. The degree of consensus based on the median score and dispersion of voting around the median are shown in Appendix 5. All 12 statements achieved consensus as strong recommendations. The strength of each recommendation and degree of consensus are summarized in Table 3.

Terminology

Abdominal paracentesis is a procedure in which fluid is aspirated from the intraperitoneal space by percutaneous insertion of a needle with or without a catheter through the abdominal wall. Throughout this document, the term “paracentesis” refers to “abdominal paracentesis.”

In this document, ultrasound-guided paracentesis refers to the use of static ultrasound guidance to mark a needle insertion site immediately prior to performing the procedure. Real-time (dynamic) ultrasound guidance refers to tracking of the needle tip with ultrasound as it traverses the abdominal wall to enter the peritoneal cavity. Landmark-based paracentesis refers to paracentesis based on physical examination alone.

RECOMMENDATIONS

Clinical outcomes

1. We recommend that ultrasound guidance should be used for paracentesis to reduce the risk of serious complications, the most common being bleeding.

Rationale. The occurrence of both minor and serious life-threatening complications from paracentesis has been well described.^5-10,31,32 A recent retrospective study that evaluated 515 landmark-guided paracenteses noted that the most common minor complication was persistent ascites leakage (5%) and that the most common serious complication was postprocedural bleeding (1%).⁸ Studies have shown that abdominal wall hematoma and hemoperitoneum are common hemorrhagic complications of paracentesis, although inferior epigastric artery pseudoaneurysm has also been described.^9,33,34

Current literature suggests that ultrasound-guided paracentesis is a safe procedure, even with reduced platelet counts or elevated international normalized ratio.^35-42 Most comparative studies have shown that ultrasound guidance reduces the risk of bleeding complications compared with the use of landmarks alone,^{7,31,32,43-45} although a few studies did not find a significant difference between techniques.^20,36,46 One large retrospective observational study that analyzed the administrative data of 69,859 paracenteses from more than 600 hospitals demonstrated that ultrasound guidance reduced the odds of bleeding complications by 68% (OR, 0.32; 95% CI, 0.25–0.41). Bleeding complication rates with and without the use of ultrasound guidance were 0.27% (CI 0.26-0.29) versus 1.25% (CI 1.21-1.29; P < .0001), respectively. More importantly, in this study, paracentesis complicated by bleeding was associated with a higher in-hospital mortality rate compared to paracentesis that were not complicated by bleeding (12.9% vs 3.7%; P < .0001).⁴³

2. We recommend that ultrasound guidance should be used to avoid attempting paracentesis in patients with an insufficient volume of intraperitoneal free fluid to drain.

Rationale. Abdominal physical examination is not a reliable method for determining the presence or volume of intraperitoneal free fluid, as no specific physical examination finding has consistently shown both high sensitivity and specificity for detecting intraperitoneal free fluid.^{11,12,20,31,47-51} Patient factors limiting the diagnostic accuracy of physical examination include body habitus, abdominal wall edema, and gaseous bowel distention.

In comparative studies, ultrasound has been found to be significantly more sensitive and specific than physical examination in detecting peritoneal free fluid.^11,12 Ultrasound can detect as little as 100 mL of peritoneal free fluid,^52,53 and larger volumes of fluid have higher diagnostic accuracy.^53-55 In one randomized trial of 100 patients suspected of having ascites, patients were randomized to landmark-based and ultrasound-guided paracentesis groups. Of the 56 patients in the ultrasound-guided group, 14 patients suspected of having ascites on physical examination were found to have no or an insufficient volume of ascites to attempt paracentesis.²⁰ Another study with 41 ultrasound examinations on cancer patients suspected of having intraperitoneal free fluid by history and physical examination demonstrated that only 19 (46%) were considered to have a sufficient volume of ascites by ultrasound to attempt paracentesis.³⁸

3. We recommend that ultrasound guidance should be used for paracentesis to improve the success rates of the overall procedure.

Rationale. In addition to avoiding drainage attempts in patients with an insufficient volume of intraperitoneal free fluid, ultrasound can increase the success rate of attempted procedures by localizing the largest fluid collection and guiding selection of an optimal needle insertion site. The success rates of landmark-based paracentesis in patients suspected of having intraperitoneal free fluid by physical examination are not well described in the literature, but multiple studies report success rates of 95%-100% for paracentesis when using ultrasound guidance to select a needle insertion site.^20,38,56,57 In one randomized trial comparing ultrasound-guided versus landmark-based paracentesis, ultrasound-guided paracentesis revealed a significantly higher success rate (95% of procedures performed) compared with landmark-based parancentesis (61% of procedures performed). Moreover, 87% of the initial failures in the landmark-based group underwent subsequent successful paracentesis when ultrasound guidance was used. Ultrasound revealed that the rest of the patients (13%) did not have enough fluid to attempt ultrasound-guided paracentesis.²⁰

Technique

4. We recommend that ultrasound should be used to assess the characteristics of intraperitoneal free fluid to guide clinical decision making of where paracentesis can be safely performed.

Rationale. The presence and characteristics of intraperitoneal fluid collections are important determinants of whether paracentesis, another procedure, or no procedure should be performed in a given clinical scenario. One study reported that the overall diagnostic accuracy of physical examination for detecting ascites was only 58%,⁵⁰ and many providers are unable to detect ascites by physical examination until 1L of fluid has accumulated. One small study showed that at least 500 ml of fluid must accumulate before shifting dullness could be detected.⁵⁸ By contrast, ultrasound has been shown to reliably detect as little as 100 mL of peritoneal free fluid ^52,53 and has been proven to be superior to physical examination in several studies.^11,12 Therefore, ultrasound can be used to qualitatively determine whether a sufficient volume of intraperitoneal free fluid is present to safely perform paracentesis.

Studies have shown that ultrasound can also be used to differentiate ascites from other pathologies (eg, matted bowel loops, metastases, abscesses) in patients with suspected ascites on history and physical examination.¹⁶ In addition, ultrasound can help to better understand the etiology and distribution of the ascites.^59-61 Sonographic measurements allow semiquantitative assessment of the volume of intraperitoneal free fluid, which may correlate with the amount of fluid removed in therapeutic paracentesis procedures.^62,63 Furthermore, depth of a fluid collection by ultrasound may be an independent risk factor for the presence of spontaneous bacterial peritonitis (SBP), with one small study showing a higher risk of SBP with larger fluid collections than with small ones.⁶⁴

5. We recommend that ultrasound should be used to identify a needle insertion site based on size of the fluid collection, thickness of the abdominal wall, and proximity to abdominal organs.

Rationale. When providers perform paracentesis using ultrasound guidance, any fluid collection that is directly visualized and accessible may be considered for drainage. The presence of ascites using ultrasound is best detected using a low-frequency transducer, such as phased array or curvilinear transducer, which provides deep penetration into the abdomen and pelvis to assess peritoneal free fluid.^{13,14,45,51,65} An optimal needle insertion site should be determined based on a combination of visualization of largest fluid collection, avoidance of underlying abdominal organs, and thickness of abdominal wall.^13,31,66,67

6. We recommend the needle insertion site should be evaluated using color flow Doppler ultrasound to identify and avoid abdominal wall blood vessels along the anticipated needle trajectory.

Rationale. The anatomy of the superficial blood vessels of the abdominal wall, especially the lateral branches, varies greatly.^68-70 Although uncommon, inadvertent laceration of an inferior epigastric artery or one of its large branches is associated with significant morbidity and mortality.^{10,15,69,71-73} A review of 126 cases of rectus sheath hematomas, which most likely occur due to laceration of the inferior or superior epigastric artery, at a single institution from 1992 to 2002 showed a mortality rate of 1.6%, even with aggressive intervention.⁷⁴ Besides the inferior epigastric arteries, several other blood vessels are at risk of injury during paracentesis, including the inferior epigastric veins, thoracoepigastric veins, subcostal artery and vein branches, deep circumflex iliac artery and vein, and recanalized subumbilical vasculature.^75-77 Laceration of any of the abdominal wall blood vessels could result in catastrophic bleeding.

Identification of abdominal wall blood vessels is most commonly performed with a high-frequency transducer using color flow Doppler ultrasound.^10,13-15 A low-frequency transducer capable of color flow Doppler ultrasound may be utilized in patients with a thick abdominal wall.

Studies suggest that detection of abdominal wall blood vessels with ultrasound may reduce the risk of bleeding complications. One study showed that 43% of patients had a vascular structure present at one or more of the three traditional landmark paracentesis sites.⁷⁸ Another study directly compared bleeding rates between an approach utilizing a low-frequency transducer to identify the largest collection of fluid only versus a two-transducer approach utilizing both low and high-frequency transducers to identify the largest collection of fluid and evaluate for any superficial blood vessels. In this study, which included 5,777 paracenteses, paracentesis-related minor bleeding rates were similar in both groups, but major bleeding rates were less in the group utilizing color flow Doppler to evaluate for superficial vessels (0.3% vs 0.08%); differences found between groups, however, did not reach statistical significance (P = .07).⁷⁹

7. We recommend that a needle insertion site should be evaluated in multiple planes to ensure clearance from underlying abdominal organs and detect any abdominal wall blood vessels along the anticipated needle trajectory.

Rationale. Most ultrasound machines have a slice thickness of <4 mm at the focal zone.⁸⁰ Considering that an ultrasound beam represents a very thin 2-dimentional cross-section of the underlying tissues, visualization in only one plane could lead to inadvertent puncture of nearby critical structures such as loops of bowel or edges of solid organs. Therefore, it is important to evaluate the needle insertion site and surrounding areas in multiple planes by tilting the transducer and rotating the transducer to orthogonal planes.⁶¹ Additionally, evaluation with color flow Doppler could be performed in a similar fashion to ensure that no large blood vessels are along the anticipated needle trajectory.

8. We recommend that a needle insertion site should be marked with ultrasound immediately before performing the procedure, and the patient should remain in the same position between marking the site and performing the procedure.

Rationale. Free-flowing peritoneal fluid and abdominal organs, especially loops of small bowel, can easily shift when a patient changes position or takes a deep breath.^13,16,53 Therefore, if the patient changes position or there is a delay between marking the needle insertion site and performing the procedure, the patient should be reevaluated with ultrasound to ensure that the marked needle insertion site is still safe for paracentesis.⁷⁸ After marking the needle insertion site, the skin surface should be wiped completely clean of gel, and the probe should be removed from the area before sterilizing the skin surface.

9. We recommend that using real-time ultrasound guidance for paracentesis should be considered when the fluid collection is small or difficult to access.

Rationale. Use of real-time ultrasound guidance for paracentesis has been described to drain abdominal fluid collections.^13,20,62 Several studies have commented that real-time ultrasound guidance for paracentesis may be necessary in obese patients, in patients with small fluid collections, or when performing the procedure near critical structures, such as loops of small bowel, liver, or spleen.^57,81 Real-time ultrasound guidance for paracentesis requires additional training in needle tracking techniques and specialized equipment to maintain sterility.

Training

10. We recommend that dedicated training sessions, including didactics, supervised practice on patients, and simulation-based practice, should be used to teach novices how to perform ultrasound-guided paracentesis.

Rationale. Healthcare providers must gain multiple skills to safely perform ultrasound-guided paracentesis. Trainees must learn how to operate the ultrasound machine to identify the most appropriate needle insertion site based on the abdominal wall thickness, fluid collection size, proximity to nearby abdominal organs, and presence of blood vessels. Education regarding the use of ultrasound guidance for paracentesis is both desired ^82,83 and being increasingly taught to health care providers who perform paracentesis.^20,84-86

Several approaches have shown high uptake of essential skills to perform ultrasound-guided paracentesis after short training sessions. One study showed that first-year medical students can be taught to use POCUS to accurately diagnose ascites after three 30-minute teaching sessions.¹⁹ Another study showed that emergency medicine residents can achieve high levels of proficiency in the preprocedural ultrasound evaluation for paracentesis with only one hour of didactic training.²⁰ Other studies also support the concept that adequate proficiency is achievable within brief, focused training sessions.^21-28 However, these skills can decay significantly over time without ongoing education.⁸⁷

11. We recommend that simulation-based practice should be used, when available, to facilitate acquisition of the required knowledge and skills to perform ultrasound-guided paracentesis.

Rationale. Simulation-based practice should be used when available, as it has been shown to increase competence in bedside diagnostic ultrasonography and procedural techniques for ultrasound-guided procedures, including paracentesis.^{22,25,29,88,89} One study showed that internal medicine residents were able to achieve a high level of proficiency to perform ultrasound-guided paracentesis after a three-hour simulation-based mastery learning session.⁸⁸ A follow-up study suggested that, after sufficient simulation-based training, a nonradiologist can perform ultrasound-guided paracentesis as safely as an interventional radiologist.²⁹

12. We recommend that competence in performing ultrasound-guided paracentesis should be demonstrated prior to independently performing the procedure on patients.

Rationale. Competence in ultrasound-guided paracentesis requires acquisition of clinical knowledge of paracentesis, skills in basic abdominal ultrasonography, and manual techniques to perform the procedure. Competence in ultrasound-guided paracentesis cannot be assumed for those graduating from internal medicine residency in the United States. While clinical knowledge of paracentesis remains a core competency of graduating internal medicine residents per the American Board of Internal Medicine, demonstration of competence in performing ultrasound-guided or landmark-based paracentesis is not currently mandated.⁹⁰ A recent national survey of internal medicine residency program directors revealed that the curricula and resources available to train residents in bedside diagnostic ultrasound and ultrasound-guided procedures, including paracentesis, remain quite variable. ⁸³

While it has not been well studied, competence in ultrasound for paracentesis, as with all other skills involved in bedside procedures, is likely best evaluated through direct observation on actual patients.⁹¹ As such, individualized systems to evaluate competency in ultrasound-guided paracentesis should be established for each site where it is performed. A list of consensus-derived ultrasound competencies for ultrasound-guided paracentesis has been proposed, and this list may serve as a guide for both training curriculum development and practitioner evaluation.^86,91,92

In the process of developing these recommendations, we identified several important gaps in the literature regarding the use of ultrasound guidance for paracentesis.

First, while some data suggest that the use of ultrasound guidance for paracentesis may reduce the inpatient length of stay and overall costs, this suggestion has not been studied rigorously. In a retrospective review of 1,297 abdominal paracenteses by Patel et al., ultrasound-guided paracentesis was associated with a lower incidence of adverse events compared with landmark-based paracentesis (1.4% vs 4.7%; P = .01). The adjusted analysis from this study showed significant reductions in adverse events (OR 0.35; 95%CI 0.165-0.739; P = .006) and hospitalization costs ($8,761 ± $5,956 vs $9,848 ± $6,581; P < .001) for paracentesis with ultrasound guidance versus without such guidance. Additionally, the adjusted average length of stay was 0.2 days shorter for paracentesis with ultrasound guidance versus that without guidance (5.6 days vs 5.8 days; P < .0001).⁴⁴ Similar conclusions were reached by Mercaldi et al., who conducted a retrospective study of 69,859 patients who underwent paracentesis. Fewer bleeding complications occurred when paracentesis was performed with ultrasound guidance (0.27%) versus without ultrasound guidance (1.27%). Hospitalization costs increased by $19,066 (P < .0001) and length of stay increased by 4.3 days (P < .0001) for patients when paracentesis was complicated by bleeding.⁴³  Because both of these studies were retrospective reviews of administrative databases, associations between procedures, complications, and use of ultrasound may be limited by erroneous coding and documentation.

Second, regarding technique, it is unknown whether the use of real-time ultrasound guidance confers additional benefits compared with use of static ultrasound to mark a suitable needle insertion site. In clinical practice, real-time ultrasound guidance is used to sample small fluid collections, particularly when loops of bowel or a solid organ are nearby. It is possible that higher procedural success rates and lower complication rates may be demonstrated in these scenarios in future studies.

Third, the optimal approach to train providers to perform ultrasound-guided paracentesis is unknown. While short training sessions have shown high uptake of essential skills to perform ultrasound-guided paracentesis, data regarding the effectiveness of training using a comprehensive competency assessment are limited. Simulation-based mastery learning as a means to obtain competency for paracentesis has been described in one study,⁸⁸ but the translation of competency demonstrated by simulation to actual patient outcomes has not been studied. Furthermore, the most effective method to train providers who are proficient in landmark-based paracentesis to achieve competency in ultrasound-guided paracentesis has not been well studied.

Fourth, the optimal technique for identifying blood vessels in the abdominal wall is unknown. We have proposed that color flow Doppler should be used to identify and avoid puncture of superficial vessels, but power Doppler is three times more sensitive at detecting blood vessels, especially at low velocities, such as in veins independent of direction or flow.⁹³ Hence using power Doppler instead of color flow Doppler may further improve the ability to identify and avoid superficial vessels along the needle trajectory.⁹²

Finally, the impact of ultrasound use on patient experience has yet to be studied. Some studies in the literature show high patient satisfaction with use of ultrasound at the bedside,^94,95 but patient satisfaction with ultrasound-guided paracentesis has not been compared directly with the landmark-based technique.

The use of ultrasound guidance for paracentesis has been associated with higher success rates and lower complication rates. Ultrasound is superior to physical examination in assessing the presence and volume of ascites, and determining the optimal needle insertion site to avoid inadvertent injury to abdominal wall blood vessels. Hospitalists can attain competence in ultrasound-guided paracentesis through the use of various training methods, including lectures, simulation-based practice, and hands-on training. Ongoing use and training over time is necessary to maintain competence.

Acknowledgments

The authors thank all the members of the Society of Hospital Medicine Point-of-care Ultrasound Task Force and the Education Committee members for their time and dedication to develop these guidelines.

SHM Point-of-care Ultrasound Task Force: CHAIRS: Nilam Soni, Ricardo Franco Sadud, Jeff Bates. WORKING GROUPS: Thoracentesis Working Group: Ria Dancel (chair), Daniel Schnobrich, Nitin Puri. Vascular Access Working Group: Ricardo Franco (chair), Benji Matthews, Saaid Abdel-Ghani, Sophia Rodgers, Martin Perez, Daniel Schnobrich. Paracentesis Working Group: Joel Cho (chair), Benji Mathews, Kreegan Reierson, Anjali Bhagra, Trevor P. Jensen. Lumbar Puncture Working Group: Nilam J. Soni (chair), Ricardo Franco, Gerard Salame, Josh Lenchus, Venkat Kalidindi, Ketino Kobaidze. Credentialing Working Group: Brian P Lucas (chair), David Tierney, Trevor P. Jensen PEER REVIEWERS: Robert Arntfield, Michael Blaivas, Richard Hoppmann, Paul Mayo, Vicki Noble, Aliaksei Pustavoitau, Kirk Spencer, Vivek Tayal. METHODOLOGIST: Mahmoud El Barbary. LIBRARIAN: Loretta Grikis. SOCIETY OF HOSPITAL MEDICINE EDUCATION COMMITTEE: Daniel Brotman (past chair), Satyen Nichani (current chair), Susan Hunt. SOCIETY OF HOSPITAL MEDICINE STAFF: Nick Marzano.

Collaborators of the Society of Hospital Medicine Point-of-care Ultrasound Task Force

Saaid Abdel-Ghani, Robert Arntfield, Jeffrey Bates, Michael Blaivas, Dan Brotman, Carolina Candotti, Richard Hoppmann, Susan Hunt, Venkat Kalidindi, Ketino Kobaidze, Josh Lenchus, Paul Mayo, Satyen Nichani, Vicki Noble, Martin Perez, Nitin Puri, Aliaksei Pustavoitau, Sophia Rodgers, Gerard Salame, Daniel Schnobrich, Kirk Spencer, Vivek Tayal, David M. Tierney

Disclaimer

The contents of this publication do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

All 5 appendices are viewable online at https://www.journalofhospitalmedicine.com.

Abdominal paracentesis is a common and increasingly performed procedure in the United States. According to Medicare Physician Supplier Procedure Summary Master Files, an estimated 150,000 paracenteses were performed on Medicare fee-for-service beneficiaries in 2008 alone; such a number represents more than a two-fold increase from the same service population in 1993.¹ This increasing trend was again noted by the Nationwide Inpatient Sample data, which identified a 10% increase in hospitalized patients with a diagnosis of cirrhosis receiving paracentesis from 2004 (50%) to 2012 (61%; P < .0001).²

Although these data demonstrate that paracentesis is being performed frequently, paracentesis may be underutilized in hospitalized cirrhotics with ascites. In addition, in-hospital mortality of cirrhotics with ascites is higher among those who do not undergo paracentesis than among those who do (9% vs 6%; P = .03).^3,4

While complications associated with paracentesis are rare, serious complications, including death, have been documented.^5-10 The most common serious complication of paracentesis is bleeding, although puncture of the bowel and other abdominal organs has also been observed. Over the past few decades, ultrasound has been increasingly used with paracentesis due to the ability of ultrasound to improve detection of ascites^11,12 and to avoid blood vessels^10,13-15 and bowels.¹⁶

Three-quarters of all paracenteses are currently performed by interventional radiologists.¹ However, paracenteses are often required off-hours,¹⁷ when interventional radiologists are less readily available. Weekend admissions have less frequent performance of early paracentesis than weekday admissions, and delaying paracentesis may increase mortality.^3,18 High proficiency in ultrasound-guided paracentesis is achievable by nonradiologists^19-28 with equal or better patient outcomes after appropriate training.²⁹

The purpose of this guideline is to review the literature and present evidence-based recommendations on the performance of ultrasound-guided paracentesis at the bedside by practicing hospitalists.

Detailed methods are described in Appendix 1. The Society of Hospital Medicine (SHM) Point-of-care Ultrasound (POCUS) Task Force was assembled to carry out this guideline development project under the direction of the SHM Board of Directors, Director of Education, and Education Committee. All expert panel members were physicians or advanced-practice providers with expertise in POCUS. Expert panel members were divided into working group members, external peer reviewers, and a methodologist, and all Task Force members were required to disclose any potential conflicts of interests (Appendix 2). The literature search was conducted in two independent phases. The first phase included literature searches conducted by the five working group members themselves. Key clinical questions and draft recommendations were then prepared, and a systematic literature search was conducted by a medical librarian based on the findings of the initial literature search and draft recommendations. The Medline, Embase, CINAHL, and Cochrane medical databases were initially searched from 1975 to October 2015. Google Scholar was also searched without limiters. An updated search was conducted from November 2015 to November 2017, search strings for which are included in Appendix 3. All article abstracts were first screened for relevance by at least two members of the working group. Full-text versions of screened articles were reviewed and articles on ultrasound guidance for paracentesis were selected. The following article types were excluded: non-English language, nonhuman, age <18 years, meeting abstracts, meeting posters, letters, and editorials. All relevant systematic reviews, meta-analyses, randomized controlled trials, and observational studies of ultrasound-guided paracentesis were screened and selected. Final article selection was based on working group consensus. The selected literature was incorporated into the draft recommendations.

We used the RAND Appropriateness Method that required panel judgment and consensus to establish recommendations.³⁰ The voting members of the SHM POCUS Task Force reviewed and voted on the draft recommendations considering five transforming factors: (1) problem priority and importance; (2) level of quality of evidence; (3) benefit/harm balance; (4) benefit/burden balance; and (5) certainty/concerns about preferences/equity acceptability/feasibility. Panel members participated in two rounds of electronic voting using an internet-based electronic data collection tool (Redcap™) during February 2018 and April 2018 (Appendix 4) and voting on appropriateness was conducted using a 9-point Likert scale. The three zones based on the 9-point Likert scale were inappropriate (1-3 points), uncertain (4-6 points), and appropriate (7-9 points), and the degree of consensus was assessed using the RAND algorithm (Appendix 1, Figure 1, and Table 1). Establishing a recommendation required at least 70% agreement that a recommendation was “appropriate.” A strong recommendation required 80% of the votes within one integer of the median, following RAND rules, and disagreement was defined as >30% of panelists voting outside of the zone of the median.

Literature search

A total of 794 references were pooled and screened from literature searches conducted by a certified medical librarian in October 2015 (604 citations) and updated in November 2017 (118 citations), and working group members’ personal bibliographies and searches (72 citations; Appendix 3, Figure 2). Final selection included 91 articles that were abstracted into a data table and incorporated into the draft recommendations.

Four domains (terminology, clinical outcomes, technique, and training) with 13 draft recommendations were generated based on the literature review by the paracentesis working group. After two rounds of panel voting, one recommendation did not achieve consensus based on the RAND rules, and 12 statements received final approval. The degree of consensus based on the median score and dispersion of voting around the median are shown in Appendix 5. All 12 statements achieved consensus as strong recommendations. The strength of each recommendation and degree of consensus are summarized in Table 3.

Terminology

Abdominal paracentesis is a procedure in which fluid is aspirated from the intraperitoneal space by percutaneous insertion of a needle with or without a catheter through the abdominal wall. Throughout this document, the term “paracentesis” refers to “abdominal paracentesis.”

In this document, ultrasound-guided paracentesis refers to the use of static ultrasound guidance to mark a needle insertion site immediately prior to performing the procedure. Real-time (dynamic) ultrasound guidance refers to tracking of the needle tip with ultrasound as it traverses the abdominal wall to enter the peritoneal cavity. Landmark-based paracentesis refers to paracentesis based on physical examination alone.

RECOMMENDATIONS

Clinical outcomes

1. We recommend that ultrasound guidance should be used for paracentesis to reduce the risk of serious complications, the most common being bleeding.

Rationale. The occurrence of both minor and serious life-threatening complications from paracentesis has been well described.^5-10,31,32 A recent retrospective study that evaluated 515 landmark-guided paracenteses noted that the most common minor complication was persistent ascites leakage (5%) and that the most common serious complication was postprocedural bleeding (1%).⁸ Studies have shown that abdominal wall hematoma and hemoperitoneum are common hemorrhagic complications of paracentesis, although inferior epigastric artery pseudoaneurysm has also been described.^9,33,34

Current literature suggests that ultrasound-guided paracentesis is a safe procedure, even with reduced platelet counts or elevated international normalized ratio.^35-42 Most comparative studies have shown that ultrasound guidance reduces the risk of bleeding complications compared with the use of landmarks alone,^{7,31,32,43-45} although a few studies did not find a significant difference between techniques.^20,36,46 One large retrospective observational study that analyzed the administrative data of 69,859 paracenteses from more than 600 hospitals demonstrated that ultrasound guidance reduced the odds of bleeding complications by 68% (OR, 0.32; 95% CI, 0.25–0.41). Bleeding complication rates with and without the use of ultrasound guidance were 0.27% (CI 0.26-0.29) versus 1.25% (CI 1.21-1.29; P < .0001), respectively. More importantly, in this study, paracentesis complicated by bleeding was associated with a higher in-hospital mortality rate compared to paracentesis that were not complicated by bleeding (12.9% vs 3.7%; P < .0001).⁴³

2. We recommend that ultrasound guidance should be used to avoid attempting paracentesis in patients with an insufficient volume of intraperitoneal free fluid to drain.

Rationale. Abdominal physical examination is not a reliable method for determining the presence or volume of intraperitoneal free fluid, as no specific physical examination finding has consistently shown both high sensitivity and specificity for detecting intraperitoneal free fluid.^{11,12,20,31,47-51} Patient factors limiting the diagnostic accuracy of physical examination include body habitus, abdominal wall edema, and gaseous bowel distention.

In comparative studies, ultrasound has been found to be significantly more sensitive and specific than physical examination in detecting peritoneal free fluid.^11,12 Ultrasound can detect as little as 100 mL of peritoneal free fluid,^52,53 and larger volumes of fluid have higher diagnostic accuracy.^53-55 In one randomized trial of 100 patients suspected of having ascites, patients were randomized to landmark-based and ultrasound-guided paracentesis groups. Of the 56 patients in the ultrasound-guided group, 14 patients suspected of having ascites on physical examination were found to have no or an insufficient volume of ascites to attempt paracentesis.²⁰ Another study with 41 ultrasound examinations on cancer patients suspected of having intraperitoneal free fluid by history and physical examination demonstrated that only 19 (46%) were considered to have a sufficient volume of ascites by ultrasound to attempt paracentesis.³⁸

3. We recommend that ultrasound guidance should be used for paracentesis to improve the success rates of the overall procedure.

Rationale. In addition to avoiding drainage attempts in patients with an insufficient volume of intraperitoneal free fluid, ultrasound can increase the success rate of attempted procedures by localizing the largest fluid collection and guiding selection of an optimal needle insertion site. The success rates of landmark-based paracentesis in patients suspected of having intraperitoneal free fluid by physical examination are not well described in the literature, but multiple studies report success rates of 95%-100% for paracentesis when using ultrasound guidance to select a needle insertion site.^20,38,56,57 In one randomized trial comparing ultrasound-guided versus landmark-based paracentesis, ultrasound-guided paracentesis revealed a significantly higher success rate (95% of procedures performed) compared with landmark-based parancentesis (61% of procedures performed). Moreover, 87% of the initial failures in the landmark-based group underwent subsequent successful paracentesis when ultrasound guidance was used. Ultrasound revealed that the rest of the patients (13%) did not have enough fluid to attempt ultrasound-guided paracentesis.²⁰

Technique

4. We recommend that ultrasound should be used to assess the characteristics of intraperitoneal free fluid to guide clinical decision making of where paracentesis can be safely performed.

Rationale. The presence and characteristics of intraperitoneal fluid collections are important determinants of whether paracentesis, another procedure, or no procedure should be performed in a given clinical scenario. One study reported that the overall diagnostic accuracy of physical examination for detecting ascites was only 58%,⁵⁰ and many providers are unable to detect ascites by physical examination until 1L of fluid has accumulated. One small study showed that at least 500 ml of fluid must accumulate before shifting dullness could be detected.⁵⁸ By contrast, ultrasound has been shown to reliably detect as little as 100 mL of peritoneal free fluid ^52,53 and has been proven to be superior to physical examination in several studies.^11,12 Therefore, ultrasound can be used to qualitatively determine whether a sufficient volume of intraperitoneal free fluid is present to safely perform paracentesis.

Studies have shown that ultrasound can also be used to differentiate ascites from other pathologies (eg, matted bowel loops, metastases, abscesses) in patients with suspected ascites on history and physical examination.¹⁶ In addition, ultrasound can help to better understand the etiology and distribution of the ascites.^59-61 Sonographic measurements allow semiquantitative assessment of the volume of intraperitoneal free fluid, which may correlate with the amount of fluid removed in therapeutic paracentesis procedures.^62,63 Furthermore, depth of a fluid collection by ultrasound may be an independent risk factor for the presence of spontaneous bacterial peritonitis (SBP), with one small study showing a higher risk of SBP with larger fluid collections than with small ones.⁶⁴

5. We recommend that ultrasound should be used to identify a needle insertion site based on size of the fluid collection, thickness of the abdominal wall, and proximity to abdominal organs.

Rationale. When providers perform paracentesis using ultrasound guidance, any fluid collection that is directly visualized and accessible may be considered for drainage. The presence of ascites using ultrasound is best detected using a low-frequency transducer, such as phased array or curvilinear transducer, which provides deep penetration into the abdomen and pelvis to assess peritoneal free fluid.^{13,14,45,51,65} An optimal needle insertion site should be determined based on a combination of visualization of largest fluid collection, avoidance of underlying abdominal organs, and thickness of abdominal wall.^13,31,66,67

6. We recommend the needle insertion site should be evaluated using color flow Doppler ultrasound to identify and avoid abdominal wall blood vessels along the anticipated needle trajectory.

Rationale. The anatomy of the superficial blood vessels of the abdominal wall, especially the lateral branches, varies greatly.^68-70 Although uncommon, inadvertent laceration of an inferior epigastric artery or one of its large branches is associated with significant morbidity and mortality.^{10,15,69,71-73} A review of 126 cases of rectus sheath hematomas, which most likely occur due to laceration of the inferior or superior epigastric artery, at a single institution from 1992 to 2002 showed a mortality rate of 1.6%, even with aggressive intervention.⁷⁴ Besides the inferior epigastric arteries, several other blood vessels are at risk of injury during paracentesis, including the inferior epigastric veins, thoracoepigastric veins, subcostal artery and vein branches, deep circumflex iliac artery and vein, and recanalized subumbilical vasculature.^75-77 Laceration of any of the abdominal wall blood vessels could result in catastrophic bleeding.

Identification of abdominal wall blood vessels is most commonly performed with a high-frequency transducer using color flow Doppler ultrasound.^10,13-15 A low-frequency transducer capable of color flow Doppler ultrasound may be utilized in patients with a thick abdominal wall.

Studies suggest that detection of abdominal wall blood vessels with ultrasound may reduce the risk of bleeding complications. One study showed that 43% of patients had a vascular structure present at one or more of the three traditional landmark paracentesis sites.⁷⁸ Another study directly compared bleeding rates between an approach utilizing a low-frequency transducer to identify the largest collection of fluid only versus a two-transducer approach utilizing both low and high-frequency transducers to identify the largest collection of fluid and evaluate for any superficial blood vessels. In this study, which included 5,777 paracenteses, paracentesis-related minor bleeding rates were similar in both groups, but major bleeding rates were less in the group utilizing color flow Doppler to evaluate for superficial vessels (0.3% vs 0.08%); differences found between groups, however, did not reach statistical significance (P = .07).⁷⁹

7. We recommend that a needle insertion site should be evaluated in multiple planes to ensure clearance from underlying abdominal organs and detect any abdominal wall blood vessels along the anticipated needle trajectory.

Rationale. Most ultrasound machines have a slice thickness of <4 mm at the focal zone.⁸⁰ Considering that an ultrasound beam represents a very thin 2-dimentional cross-section of the underlying tissues, visualization in only one plane could lead to inadvertent puncture of nearby critical structures such as loops of bowel or edges of solid organs. Therefore, it is important to evaluate the needle insertion site and surrounding areas in multiple planes by tilting the transducer and rotating the transducer to orthogonal planes.⁶¹ Additionally, evaluation with color flow Doppler could be performed in a similar fashion to ensure that no large blood vessels are along the anticipated needle trajectory.

8. We recommend that a needle insertion site should be marked with ultrasound immediately before performing the procedure, and the patient should remain in the same position between marking the site and performing the procedure.

Rationale. Free-flowing peritoneal fluid and abdominal organs, especially loops of small bowel, can easily shift when a patient changes position or takes a deep breath.^13,16,53 Therefore, if the patient changes position or there is a delay between marking the needle insertion site and performing the procedure, the patient should be reevaluated with ultrasound to ensure that the marked needle insertion site is still safe for paracentesis.⁷⁸ After marking the needle insertion site, the skin surface should be wiped completely clean of gel, and the probe should be removed from the area before sterilizing the skin surface.

9. We recommend that using real-time ultrasound guidance for paracentesis should be considered when the fluid collection is small or difficult to access.

Rationale. Use of real-time ultrasound guidance for paracentesis has been described to drain abdominal fluid collections.^13,20,62 Several studies have commented that real-time ultrasound guidance for paracentesis may be necessary in obese patients, in patients with small fluid collections, or when performing the procedure near critical structures, such as loops of small bowel, liver, or spleen.^57,81 Real-time ultrasound guidance for paracentesis requires additional training in needle tracking techniques and specialized equipment to maintain sterility.

Training

10. We recommend that dedicated training sessions, including didactics, supervised practice on patients, and simulation-based practice, should be used to teach novices how to perform ultrasound-guided paracentesis.

Rationale. Healthcare providers must gain multiple skills to safely perform ultrasound-guided paracentesis. Trainees must learn how to operate the ultrasound machine to identify the most appropriate needle insertion site based on the abdominal wall thickness, fluid collection size, proximity to nearby abdominal organs, and presence of blood vessels. Education regarding the use of ultrasound guidance for paracentesis is both desired ^82,83 and being increasingly taught to health care providers who perform paracentesis.^20,84-86

Several approaches have shown high uptake of essential skills to perform ultrasound-guided paracentesis after short training sessions. One study showed that first-year medical students can be taught to use POCUS to accurately diagnose ascites after three 30-minute teaching sessions.¹⁹ Another study showed that emergency medicine residents can achieve high levels of proficiency in the preprocedural ultrasound evaluation for paracentesis with only one hour of didactic training.²⁰ Other studies also support the concept that adequate proficiency is achievable within brief, focused training sessions.^21-28 However, these skills can decay significantly over time without ongoing education.⁸⁷

11. We recommend that simulation-based practice should be used, when available, to facilitate acquisition of the required knowledge and skills to perform ultrasound-guided paracentesis.

Rationale. Simulation-based practice should be used when available, as it has been shown to increase competence in bedside diagnostic ultrasonography and procedural techniques for ultrasound-guided procedures, including paracentesis.^{22,25,29,88,89} One study showed that internal medicine residents were able to achieve a high level of proficiency to perform ultrasound-guided paracentesis after a three-hour simulation-based mastery learning session.⁸⁸ A follow-up study suggested that, after sufficient simulation-based training, a nonradiologist can perform ultrasound-guided paracentesis as safely as an interventional radiologist.²⁹

12. We recommend that competence in performing ultrasound-guided paracentesis should be demonstrated prior to independently performing the procedure on patients.

Rationale. Competence in ultrasound-guided paracentesis requires acquisition of clinical knowledge of paracentesis, skills in basic abdominal ultrasonography, and manual techniques to perform the procedure. Competence in ultrasound-guided paracentesis cannot be assumed for those graduating from internal medicine residency in the United States. While clinical knowledge of paracentesis remains a core competency of graduating internal medicine residents per the American Board of Internal Medicine, demonstration of competence in performing ultrasound-guided or landmark-based paracentesis is not currently mandated.⁹⁰ A recent national survey of internal medicine residency program directors revealed that the curricula and resources available to train residents in bedside diagnostic ultrasound and ultrasound-guided procedures, including paracentesis, remain quite variable. ⁸³

While it has not been well studied, competence in ultrasound for paracentesis, as with all other skills involved in bedside procedures, is likely best evaluated through direct observation on actual patients.⁹¹ As such, individualized systems to evaluate competency in ultrasound-guided paracentesis should be established for each site where it is performed. A list of consensus-derived ultrasound competencies for ultrasound-guided paracentesis has been proposed, and this list may serve as a guide for both training curriculum development and practitioner evaluation.^86,91,92

In the process of developing these recommendations, we identified several important gaps in the literature regarding the use of ultrasound guidance for paracentesis.

First, while some data suggest that the use of ultrasound guidance for paracentesis may reduce the inpatient length of stay and overall costs, this suggestion has not been studied rigorously. In a retrospective review of 1,297 abdominal paracenteses by Patel et al., ultrasound-guided paracentesis was associated with a lower incidence of adverse events compared with landmark-based paracentesis (1.4% vs 4.7%; P = .01). The adjusted analysis from this study showed significant reductions in adverse events (OR 0.35; 95%CI 0.165-0.739; P = .006) and hospitalization costs ($8,761 ± $5,956 vs $9,848 ± $6,581; P < .001) for paracentesis with ultrasound guidance versus without such guidance. Additionally, the adjusted average length of stay was 0.2 days shorter for paracentesis with ultrasound guidance versus that without guidance (5.6 days vs 5.8 days; P < .0001).⁴⁴ Similar conclusions were reached by Mercaldi et al., who conducted a retrospective study of 69,859 patients who underwent paracentesis. Fewer bleeding complications occurred when paracentesis was performed with ultrasound guidance (0.27%) versus without ultrasound guidance (1.27%). Hospitalization costs increased by $19,066 (P < .0001) and length of stay increased by 4.3 days (P < .0001) for patients when paracentesis was complicated by bleeding.⁴³  Because both of these studies were retrospective reviews of administrative databases, associations between procedures, complications, and use of ultrasound may be limited by erroneous coding and documentation.

Second, regarding technique, it is unknown whether the use of real-time ultrasound guidance confers additional benefits compared with use of static ultrasound to mark a suitable needle insertion site. In clinical practice, real-time ultrasound guidance is used to sample small fluid collections, particularly when loops of bowel or a solid organ are nearby. It is possible that higher procedural success rates and lower complication rates may be demonstrated in these scenarios in future studies.

Third, the optimal approach to train providers to perform ultrasound-guided paracentesis is unknown. While short training sessions have shown high uptake of essential skills to perform ultrasound-guided paracentesis, data regarding the effectiveness of training using a comprehensive competency assessment are limited. Simulation-based mastery learning as a means to obtain competency for paracentesis has been described in one study,⁸⁸ but the translation of competency demonstrated by simulation to actual patient outcomes has not been studied. Furthermore, the most effective method to train providers who are proficient in landmark-based paracentesis to achieve competency in ultrasound-guided paracentesis has not been well studied.

Fourth, the optimal technique for identifying blood vessels in the abdominal wall is unknown. We have proposed that color flow Doppler should be used to identify and avoid puncture of superficial vessels, but power Doppler is three times more sensitive at detecting blood vessels, especially at low velocities, such as in veins independent of direction or flow.⁹³ Hence using power Doppler instead of color flow Doppler may further improve the ability to identify and avoid superficial vessels along the needle trajectory.⁹²

Finally, the impact of ultrasound use on patient experience has yet to be studied. Some studies in the literature show high patient satisfaction with use of ultrasound at the bedside,^94,95 but patient satisfaction with ultrasound-guided paracentesis has not been compared directly with the landmark-based technique.

The use of ultrasound guidance for paracentesis has been associated with higher success rates and lower complication rates. Ultrasound is superior to physical examination in assessing the presence and volume of ascites, and determining the optimal needle insertion site to avoid inadvertent injury to abdominal wall blood vessels. Hospitalists can attain competence in ultrasound-guided paracentesis through the use of various training methods, including lectures, simulation-based practice, and hands-on training. Ongoing use and training over time is necessary to maintain competence.

Acknowledgments

The authors thank all the members of the Society of Hospital Medicine Point-of-care Ultrasound Task Force and the Education Committee members for their time and dedication to develop these guidelines.

SHM Point-of-care Ultrasound Task Force: CHAIRS: Nilam Soni, Ricardo Franco Sadud, Jeff Bates. WORKING GROUPS: Thoracentesis Working Group: Ria Dancel (chair), Daniel Schnobrich, Nitin Puri. Vascular Access Working Group: Ricardo Franco (chair), Benji Matthews, Saaid Abdel-Ghani, Sophia Rodgers, Martin Perez, Daniel Schnobrich. Paracentesis Working Group: Joel Cho (chair), Benji Mathews, Kreegan Reierson, Anjali Bhagra, Trevor P. Jensen. Lumbar Puncture Working Group: Nilam J. Soni (chair), Ricardo Franco, Gerard Salame, Josh Lenchus, Venkat Kalidindi, Ketino Kobaidze. Credentialing Working Group: Brian P Lucas (chair), David Tierney, Trevor P. Jensen PEER REVIEWERS: Robert Arntfield, Michael Blaivas, Richard Hoppmann, Paul Mayo, Vicki Noble, Aliaksei Pustavoitau, Kirk Spencer, Vivek Tayal. METHODOLOGIST: Mahmoud El Barbary. LIBRARIAN: Loretta Grikis. SOCIETY OF HOSPITAL MEDICINE EDUCATION COMMITTEE: Daniel Brotman (past chair), Satyen Nichani (current chair), Susan Hunt. SOCIETY OF HOSPITAL MEDICINE STAFF: Nick Marzano.

Collaborators of the Society of Hospital Medicine Point-of-care Ultrasound Task Force

Saaid Abdel-Ghani, Robert Arntfield, Jeffrey Bates, Michael Blaivas, Dan Brotman, Carolina Candotti, Richard Hoppmann, Susan Hunt, Venkat Kalidindi, Ketino Kobaidze, Josh Lenchus, Paul Mayo, Satyen Nichani, Vicki Noble, Martin Perez, Nitin Puri, Aliaksei Pustavoitau, Sophia Rodgers, Gerard Salame, Daniel Schnobrich, Kirk Spencer, Vivek Tayal, David M. Tierney

Disclaimer

The contents of this publication do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

All 5 appendices are viewable online at https://www.journalofhospitalmedicine.com.

Abdominal paracentesis is a common and increasingly performed procedure in the United States. According to Medicare Physician Supplier Procedure Summary Master Files, an estimated 150,000 paracenteses were performed on Medicare fee-for-service beneficiaries in 2008 alone; such a number represents more than a two-fold increase from the same service population in 1993.¹ This increasing trend was again noted by the Nationwide Inpatient Sample data, which identified a 10% increase in hospitalized patients with a diagnosis of cirrhosis receiving paracentesis from 2004 (50%) to 2012 (61%; P < .0001).²

Although these data demonstrate that paracentesis is being performed frequently, paracentesis may be underutilized in hospitalized cirrhotics with ascites. In addition, in-hospital mortality of cirrhotics with ascites is higher among those who do not undergo paracentesis than among those who do (9% vs 6%; P = .03).^3,4

While complications associated with paracentesis are rare, serious complications, including death, have been documented.^5-10 The most common serious complication of paracentesis is bleeding, although puncture of the bowel and other abdominal organs has also been observed. Over the past few decades, ultrasound has been increasingly used with paracentesis due to the ability of ultrasound to improve detection of ascites^11,12 and to avoid blood vessels^10,13-15 and bowels.¹⁶

Three-quarters of all paracenteses are currently performed by interventional radiologists.¹ However, paracenteses are often required off-hours,¹⁷ when interventional radiologists are less readily available. Weekend admissions have less frequent performance of early paracentesis than weekday admissions, and delaying paracentesis may increase mortality.^3,18 High proficiency in ultrasound-guided paracentesis is achievable by nonradiologists^19-28 with equal or better patient outcomes after appropriate training.²⁹

The purpose of this guideline is to review the literature and present evidence-based recommendations on the performance of ultrasound-guided paracentesis at the bedside by practicing hospitalists.

Detailed methods are described in Appendix 1. The Society of Hospital Medicine (SHM) Point-of-care Ultrasound (POCUS) Task Force was assembled to carry out this guideline development project under the direction of the SHM Board of Directors, Director of Education, and Education Committee. All expert panel members were physicians or advanced-practice providers with expertise in POCUS. Expert panel members were divided into working group members, external peer reviewers, and a methodologist, and all Task Force members were required to disclose any potential conflicts of interests (Appendix 2). The literature search was conducted in two independent phases. The first phase included literature searches conducted by the five working group members themselves. Key clinical questions and draft recommendations were then prepared, and a systematic literature search was conducted by a medical librarian based on the findings of the initial literature search and draft recommendations. The Medline, Embase, CINAHL, and Cochrane medical databases were initially searched from 1975 to October 2015. Google Scholar was also searched without limiters. An updated search was conducted from November 2015 to November 2017, search strings for which are included in Appendix 3. All article abstracts were first screened for relevance by at least two members of the working group. Full-text versions of screened articles were reviewed and articles on ultrasound guidance for paracentesis were selected. The following article types were excluded: non-English language, nonhuman, age <18 years, meeting abstracts, meeting posters, letters, and editorials. All relevant systematic reviews, meta-analyses, randomized controlled trials, and observational studies of ultrasound-guided paracentesis were screened and selected. Final article selection was based on working group consensus. The selected literature was incorporated into the draft recommendations.

We used the RAND Appropriateness Method that required panel judgment and consensus to establish recommendations.³⁰ The voting members of the SHM POCUS Task Force reviewed and voted on the draft recommendations considering five transforming factors: (1) problem priority and importance; (2) level of quality of evidence; (3) benefit/harm balance; (4) benefit/burden balance; and (5) certainty/concerns about preferences/equity acceptability/feasibility. Panel members participated in two rounds of electronic voting using an internet-based electronic data collection tool (Redcap™) during February 2018 and April 2018 (Appendix 4) and voting on appropriateness was conducted using a 9-point Likert scale. The three zones based on the 9-point Likert scale were inappropriate (1-3 points), uncertain (4-6 points), and appropriate (7-9 points), and the degree of consensus was assessed using the RAND algorithm (Appendix 1, Figure 1, and Table 1). Establishing a recommendation required at least 70% agreement that a recommendation was “appropriate.” A strong recommendation required 80% of the votes within one integer of the median, following RAND rules, and disagreement was defined as >30% of panelists voting outside of the zone of the median.

Literature search

A total of 794 references were pooled and screened from literature searches conducted by a certified medical librarian in October 2015 (604 citations) and updated in November 2017 (118 citations), and working group members’ personal bibliographies and searches (72 citations; Appendix 3, Figure 2). Final selection included 91 articles that were abstracted into a data table and incorporated into the draft recommendations.

Four domains (terminology, clinical outcomes, technique, and training) with 13 draft recommendations were generated based on the literature review by the paracentesis working group. After two rounds of panel voting, one recommendation did not achieve consensus based on the RAND rules, and 12 statements received final approval. The degree of consensus based on the median score and dispersion of voting around the median are shown in Appendix 5. All 12 statements achieved consensus as strong recommendations. The strength of each recommendation and degree of consensus are summarized in Table 3.

Terminology

Abdominal paracentesis is a procedure in which fluid is aspirated from the intraperitoneal space by percutaneous insertion of a needle with or without a catheter through the abdominal wall. Throughout this document, the term “paracentesis” refers to “abdominal paracentesis.”

In this document, ultrasound-guided paracentesis refers to the use of static ultrasound guidance to mark a needle insertion site immediately prior to performing the procedure. Real-time (dynamic) ultrasound guidance refers to tracking of the needle tip with ultrasound as it traverses the abdominal wall to enter the peritoneal cavity. Landmark-based paracentesis refers to paracentesis based on physical examination alone.

RECOMMENDATIONS

Clinical outcomes

1. We recommend that ultrasound guidance should be used for paracentesis to reduce the risk of serious complications, the most common being bleeding.

Rationale. The occurrence of both minor and serious life-threatening complications from paracentesis has been well described.^5-10,31,32 A recent retrospective study that evaluated 515 landmark-guided paracenteses noted that the most common minor complication was persistent ascites leakage (5%) and that the most common serious complication was postprocedural bleeding (1%).⁸ Studies have shown that abdominal wall hematoma and hemoperitoneum are common hemorrhagic complications of paracentesis, although inferior epigastric artery pseudoaneurysm has also been described.^9,33,34

Current literature suggests that ultrasound-guided paracentesis is a safe procedure, even with reduced platelet counts or elevated international normalized ratio.^35-42 Most comparative studies have shown that ultrasound guidance reduces the risk of bleeding complications compared with the use of landmarks alone,^{7,31,32,43-45} although a few studies did not find a significant difference between techniques.^20,36,46 One large retrospective observational study that analyzed the administrative data of 69,859 paracenteses from more than 600 hospitals demonstrated that ultrasound guidance reduced the odds of bleeding complications by 68% (OR, 0.32; 95% CI, 0.25–0.41). Bleeding complication rates with and without the use of ultrasound guidance were 0.27% (CI 0.26-0.29) versus 1.25% (CI 1.21-1.29; P < .0001), respectively. More importantly, in this study, paracentesis complicated by bleeding was associated with a higher in-hospital mortality rate compared to paracentesis that were not complicated by bleeding (12.9% vs 3.7%; P < .0001).⁴³

2. We recommend that ultrasound guidance should be used to avoid attempting paracentesis in patients with an insufficient volume of intraperitoneal free fluid to drain.

Rationale. Abdominal physical examination is not a reliable method for determining the presence or volume of intraperitoneal free fluid, as no specific physical examination finding has consistently shown both high sensitivity and specificity for detecting intraperitoneal free fluid.^{11,12,20,31,47-51} Patient factors limiting the diagnostic accuracy of physical examination include body habitus, abdominal wall edema, and gaseous bowel distention.

In comparative studies, ultrasound has been found to be significantly more sensitive and specific than physical examination in detecting peritoneal free fluid.^11,12 Ultrasound can detect as little as 100 mL of peritoneal free fluid,^52,53 and larger volumes of fluid have higher diagnostic accuracy.^53-55 In one randomized trial of 100 patients suspected of having ascites, patients were randomized to landmark-based and ultrasound-guided paracentesis groups. Of the 56 patients in the ultrasound-guided group, 14 patients suspected of having ascites on physical examination were found to have no or an insufficient volume of ascites to attempt paracentesis.²⁰ Another study with 41 ultrasound examinations on cancer patients suspected of having intraperitoneal free fluid by history and physical examination demonstrated that only 19 (46%) were considered to have a sufficient volume of ascites by ultrasound to attempt paracentesis.³⁸

3. We recommend that ultrasound guidance should be used for paracentesis to improve the success rates of the overall procedure.

Rationale. In addition to avoiding drainage attempts in patients with an insufficient volume of intraperitoneal free fluid, ultrasound can increase the success rate of attempted procedures by localizing the largest fluid collection and guiding selection of an optimal needle insertion site. The success rates of landmark-based paracentesis in patients suspected of having intraperitoneal free fluid by physical examination are not well described in the literature, but multiple studies report success rates of 95%-100% for paracentesis when using ultrasound guidance to select a needle insertion site.^20,38,56,57 In one randomized trial comparing ultrasound-guided versus landmark-based paracentesis, ultrasound-guided paracentesis revealed a significantly higher success rate (95% of procedures performed) compared with landmark-based parancentesis (61% of procedures performed). Moreover, 87% of the initial failures in the landmark-based group underwent subsequent successful paracentesis when ultrasound guidance was used. Ultrasound revealed that the rest of the patients (13%) did not have enough fluid to attempt ultrasound-guided paracentesis.²⁰

Technique

4. We recommend that ultrasound should be used to assess the characteristics of intraperitoneal free fluid to guide clinical decision making of where paracentesis can be safely performed.

Rationale. The presence and characteristics of intraperitoneal fluid collections are important determinants of whether paracentesis, another procedure, or no procedure should be performed in a given clinical scenario. One study reported that the overall diagnostic accuracy of physical examination for detecting ascites was only 58%,⁵⁰ and many providers are unable to detect ascites by physical examination until 1L of fluid has accumulated. One small study showed that at least 500 ml of fluid must accumulate before shifting dullness could be detected.⁵⁸ By contrast, ultrasound has been shown to reliably detect as little as 100 mL of peritoneal free fluid ^52,53 and has been proven to be superior to physical examination in several studies.^11,12 Therefore, ultrasound can be used to qualitatively determine whether a sufficient volume of intraperitoneal free fluid is present to safely perform paracentesis.

Studies have shown that ultrasound can also be used to differentiate ascites from other pathologies (eg, matted bowel loops, metastases, abscesses) in patients with suspected ascites on history and physical examination.¹⁶ In addition, ultrasound can help to better understand the etiology and distribution of the ascites.^59-61 Sonographic measurements allow semiquantitative assessment of the volume of intraperitoneal free fluid, which may correlate with the amount of fluid removed in therapeutic paracentesis procedures.^62,63 Furthermore, depth of a fluid collection by ultrasound may be an independent risk factor for the presence of spontaneous bacterial peritonitis (SBP), with one small study showing a higher risk of SBP with larger fluid collections than with small ones.⁶⁴

5. We recommend that ultrasound should be used to identify a needle insertion site based on size of the fluid collection, thickness of the abdominal wall, and proximity to abdominal organs.

Rationale. When providers perform paracentesis using ultrasound guidance, any fluid collection that is directly visualized and accessible may be considered for drainage. The presence of ascites using ultrasound is best detected using a low-frequency transducer, such as phased array or curvilinear transducer, which provides deep penetration into the abdomen and pelvis to assess peritoneal free fluid.^{13,14,45,51,65} An optimal needle insertion site should be determined based on a combination of visualization of largest fluid collection, avoidance of underlying abdominal organs, and thickness of abdominal wall.^13,31,66,67

6. We recommend the needle insertion site should be evaluated using color flow Doppler ultrasound to identify and avoid abdominal wall blood vessels along the anticipated needle trajectory.

Rationale. The anatomy of the superficial blood vessels of the abdominal wall, especially the lateral branches, varies greatly.^68-70 Although uncommon, inadvertent laceration of an inferior epigastric artery or one of its large branches is associated with significant morbidity and mortality.^{10,15,69,71-73} A review of 126 cases of rectus sheath hematomas, which most likely occur due to laceration of the inferior or superior epigastric artery, at a single institution from 1992 to 2002 showed a mortality rate of 1.6%, even with aggressive intervention.⁷⁴ Besides the inferior epigastric arteries, several other blood vessels are at risk of injury during paracentesis, including the inferior epigastric veins, thoracoepigastric veins, subcostal artery and vein branches, deep circumflex iliac artery and vein, and recanalized subumbilical vasculature.^75-77 Laceration of any of the abdominal wall blood vessels could result in catastrophic bleeding.

Identification of abdominal wall blood vessels is most commonly performed with a high-frequency transducer using color flow Doppler ultrasound.^10,13-15 A low-frequency transducer capable of color flow Doppler ultrasound may be utilized in patients with a thick abdominal wall.

Studies suggest that detection of abdominal wall blood vessels with ultrasound may reduce the risk of bleeding complications. One study showed that 43% of patients had a vascular structure present at one or more of the three traditional landmark paracentesis sites.⁷⁸ Another study directly compared bleeding rates between an approach utilizing a low-frequency transducer to identify the largest collection of fluid only versus a two-transducer approach utilizing both low and high-frequency transducers to identify the largest collection of fluid and evaluate for any superficial blood vessels. In this study, which included 5,777 paracenteses, paracentesis-related minor bleeding rates were similar in both groups, but major bleeding rates were less in the group utilizing color flow Doppler to evaluate for superficial vessels (0.3% vs 0.08%); differences found between groups, however, did not reach statistical significance (P = .07).⁷⁹

7. We recommend that a needle insertion site should be evaluated in multiple planes to ensure clearance from underlying abdominal organs and detect any abdominal wall blood vessels along the anticipated needle trajectory.

Rationale. Most ultrasound machines have a slice thickness of <4 mm at the focal zone.⁸⁰ Considering that an ultrasound beam represents a very thin 2-dimentional cross-section of the underlying tissues, visualization in only one plane could lead to inadvertent puncture of nearby critical structures such as loops of bowel or edges of solid organs. Therefore, it is important to evaluate the needle insertion site and surrounding areas in multiple planes by tilting the transducer and rotating the transducer to orthogonal planes.⁶¹ Additionally, evaluation with color flow Doppler could be performed in a similar fashion to ensure that no large blood vessels are along the anticipated needle trajectory.

8. We recommend that a needle insertion site should be marked with ultrasound immediately before performing the procedure, and the patient should remain in the same position between marking the site and performing the procedure.

Rationale. Free-flowing peritoneal fluid and abdominal organs, especially loops of small bowel, can easily shift when a patient changes position or takes a deep breath.^13,16,53 Therefore, if the patient changes position or there is a delay between marking the needle insertion site and performing the procedure, the patient should be reevaluated with ultrasound to ensure that the marked needle insertion site is still safe for paracentesis.⁷⁸ After marking the needle insertion site, the skin surface should be wiped completely clean of gel, and the probe should be removed from the area before sterilizing the skin surface.

9. We recommend that using real-time ultrasound guidance for paracentesis should be considered when the fluid collection is small or difficult to access.

Rationale. Use of real-time ultrasound guidance for paracentesis has been described to drain abdominal fluid collections.^13,20,62 Several studies have commented that real-time ultrasound guidance for paracentesis may be necessary in obese patients, in patients with small fluid collections, or when performing the procedure near critical structures, such as loops of small bowel, liver, or spleen.^57,81 Real-time ultrasound guidance for paracentesis requires additional training in needle tracking techniques and specialized equipment to maintain sterility.

Training

10. We recommend that dedicated training sessions, including didactics, supervised practice on patients, and simulation-based practice, should be used to teach novices how to perform ultrasound-guided paracentesis.

Rationale. Healthcare providers must gain multiple skills to safely perform ultrasound-guided paracentesis. Trainees must learn how to operate the ultrasound machine to identify the most appropriate needle insertion site based on the abdominal wall thickness, fluid collection size, proximity to nearby abdominal organs, and presence of blood vessels. Education regarding the use of ultrasound guidance for paracentesis is both desired ^82,83 and being increasingly taught to health care providers who perform paracentesis.^20,84-86

Several approaches have shown high uptake of essential skills to perform ultrasound-guided paracentesis after short training sessions. One study showed that first-year medical students can be taught to use POCUS to accurately diagnose ascites after three 30-minute teaching sessions.¹⁹ Another study showed that emergency medicine residents can achieve high levels of proficiency in the preprocedural ultrasound evaluation for paracentesis with only one hour of didactic training.²⁰ Other studies also support the concept that adequate proficiency is achievable within brief, focused training sessions.^21-28 However, these skills can decay significantly over time without ongoing education.⁸⁷

11. We recommend that simulation-based practice should be used, when available, to facilitate acquisition of the required knowledge and skills to perform ultrasound-guided paracentesis.

Rationale. Simulation-based practice should be used when available, as it has been shown to increase competence in bedside diagnostic ultrasonography and procedural techniques for ultrasound-guided procedures, including paracentesis.^{22,25,29,88,89} One study showed that internal medicine residents were able to achieve a high level of proficiency to perform ultrasound-guided paracentesis after a three-hour simulation-based mastery learning session.⁸⁸ A follow-up study suggested that, after sufficient simulation-based training, a nonradiologist can perform ultrasound-guided paracentesis as safely as an interventional radiologist.²⁹

12. We recommend that competence in performing ultrasound-guided paracentesis should be demonstrated prior to independently performing the procedure on patients.

Rationale. Competence in ultrasound-guided paracentesis requires acquisition of clinical knowledge of paracentesis, skills in basic abdominal ultrasonography, and manual techniques to perform the procedure. Competence in ultrasound-guided paracentesis cannot be assumed for those graduating from internal medicine residency in the United States. While clinical knowledge of paracentesis remains a core competency of graduating internal medicine residents per the American Board of Internal Medicine, demonstration of competence in performing ultrasound-guided or landmark-based paracentesis is not currently mandated.⁹⁰ A recent national survey of internal medicine residency program directors revealed that the curricula and resources available to train residents in bedside diagnostic ultrasound and ultrasound-guided procedures, including paracentesis, remain quite variable. ⁸³

While it has not been well studied, competence in ultrasound for paracentesis, as with all other skills involved in bedside procedures, is likely best evaluated through direct observation on actual patients.⁹¹ As such, individualized systems to evaluate competency in ultrasound-guided paracentesis should be established for each site where it is performed. A list of consensus-derived ultrasound competencies for ultrasound-guided paracentesis has been proposed, and this list may serve as a guide for both training curriculum development and practitioner evaluation.^86,91,92

In the process of developing these recommendations, we identified several important gaps in the literature regarding the use of ultrasound guidance for paracentesis.

First, while some data suggest that the use of ultrasound guidance for paracentesis may reduce the inpatient length of stay and overall costs, this suggestion has not been studied rigorously. In a retrospective review of 1,297 abdominal paracenteses by Patel et al., ultrasound-guided paracentesis was associated with a lower incidence of adverse events compared with landmark-based paracentesis (1.4% vs 4.7%; P = .01). The adjusted analysis from this study showed significant reductions in adverse events (OR 0.35; 95%CI 0.165-0.739; P = .006) and hospitalization costs ($8,761 ± $5,956 vs $9,848 ± $6,581; P < .001) for paracentesis with ultrasound guidance versus without such guidance. Additionally, the adjusted average length of stay was 0.2 days shorter for paracentesis with ultrasound guidance versus that without guidance (5.6 days vs 5.8 days; P < .0001).⁴⁴ Similar conclusions were reached by Mercaldi et al., who conducted a retrospective study of 69,859 patients who underwent paracentesis. Fewer bleeding complications occurred when paracentesis was performed with ultrasound guidance (0.27%) versus without ultrasound guidance (1.27%). Hospitalization costs increased by $19,066 (P < .0001) and length of stay increased by 4.3 days (P < .0001) for patients when paracentesis was complicated by bleeding.⁴³  Because both of these studies were retrospective reviews of administrative databases, associations between procedures, complications, and use of ultrasound may be limited by erroneous coding and documentation.

Second, regarding technique, it is unknown whether the use of real-time ultrasound guidance confers additional benefits compared with use of static ultrasound to mark a suitable needle insertion site. In clinical practice, real-time ultrasound guidance is used to sample small fluid collections, particularly when loops of bowel or a solid organ are nearby. It is possible that higher procedural success rates and lower complication rates may be demonstrated in these scenarios in future studies.

Third, the optimal approach to train providers to perform ultrasound-guided paracentesis is unknown. While short training sessions have shown high uptake of essential skills to perform ultrasound-guided paracentesis, data regarding the effectiveness of training using a comprehensive competency assessment are limited. Simulation-based mastery learning as a means to obtain competency for paracentesis has been described in one study,⁸⁸ but the translation of competency demonstrated by simulation to actual patient outcomes has not been studied. Furthermore, the most effective method to train providers who are proficient in landmark-based paracentesis to achieve competency in ultrasound-guided paracentesis has not been well studied.

Fourth, the optimal technique for identifying blood vessels in the abdominal wall is unknown. We have proposed that color flow Doppler should be used to identify and avoid puncture of superficial vessels, but power Doppler is three times more sensitive at detecting blood vessels, especially at low velocities, such as in veins independent of direction or flow.⁹³ Hence using power Doppler instead of color flow Doppler may further improve the ability to identify and avoid superficial vessels along the needle trajectory.⁹²

Finally, the impact of ultrasound use on patient experience has yet to be studied. Some studies in the literature show high patient satisfaction with use of ultrasound at the bedside,^94,95 but patient satisfaction with ultrasound-guided paracentesis has not been compared directly with the landmark-based technique.

The use of ultrasound guidance for paracentesis has been associated with higher success rates and lower complication rates. Ultrasound is superior to physical examination in assessing the presence and volume of ascites, and determining the optimal needle insertion site to avoid inadvertent injury to abdominal wall blood vessels. Hospitalists can attain competence in ultrasound-guided paracentesis through the use of various training methods, including lectures, simulation-based practice, and hands-on training. Ongoing use and training over time is necessary to maintain competence.

Acknowledgments

The authors thank all the members of the Society of Hospital Medicine Point-of-care Ultrasound Task Force and the Education Committee members for their time and dedication to develop these guidelines.

SHM Point-of-care Ultrasound Task Force: CHAIRS: Nilam Soni, Ricardo Franco Sadud, Jeff Bates. WORKING GROUPS: Thoracentesis Working Group: Ria Dancel (chair), Daniel Schnobrich, Nitin Puri. Vascular Access Working Group: Ricardo Franco (chair), Benji Matthews, Saaid Abdel-Ghani, Sophia Rodgers, Martin Perez, Daniel Schnobrich. Paracentesis Working Group: Joel Cho (chair), Benji Mathews, Kreegan Reierson, Anjali Bhagra, Trevor P. Jensen. Lumbar Puncture Working Group: Nilam J. Soni (chair), Ricardo Franco, Gerard Salame, Josh Lenchus, Venkat Kalidindi, Ketino Kobaidze. Credentialing Working Group: Brian P Lucas (chair), David Tierney, Trevor P. Jensen PEER REVIEWERS: Robert Arntfield, Michael Blaivas, Richard Hoppmann, Paul Mayo, Vicki Noble, Aliaksei Pustavoitau, Kirk Spencer, Vivek Tayal. METHODOLOGIST: Mahmoud El Barbary. LIBRARIAN: Loretta Grikis. SOCIETY OF HOSPITAL MEDICINE EDUCATION COMMITTEE: Daniel Brotman (past chair), Satyen Nichani (current chair), Susan Hunt. SOCIETY OF HOSPITAL MEDICINE STAFF: Nick Marzano.

Collaborators of the Society of Hospital Medicine Point-of-care Ultrasound Task Force

Saaid Abdel-Ghani, Robert Arntfield, Jeffrey Bates, Michael Blaivas, Dan Brotman, Carolina Candotti, Richard Hoppmann, Susan Hunt, Venkat Kalidindi, Ketino Kobaidze, Josh Lenchus, Paul Mayo, Satyen Nichani, Vicki Noble, Martin Perez, Nitin Puri, Aliaksei Pustavoitau, Sophia Rodgers, Gerard Salame, Daniel Schnobrich, Kirk Spencer, Vivek Tayal, David M. Tierney

Disclaimer

The contents of this publication do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

All 5 appendices are viewable online at https://www.journalofhospitalmedicine.com.

Whenever the principles of effective medical consultation are discussed, a classic article published in 1983 by Lee Goldman et al. is invariably referenced. In the “Ten Commandments for Effective Consultation,” Goldman argued that internists should “determine the question, establish urgency, look for yourself, be as brief as appropriate, be specific, provide contingency plans, honor thy turf, teach with tact, provide direct personal contact, and follow up.”¹ If these Ten Commandments were followed, then the consultation would be more effective and satisfactory for both the consultant and the referring provider. However, with the advent of comanagement in 1994 where internists and surgeons have a “shared responsibility and accountability,”² there has been a shift, and the once-concrete definitions of a specific reason for consult and the nature of “turf” have become blurred. Since 1994, the use of medical consultation and comanagement has skyrocketed, and today, more than 50% of surgical patients have a medical consultation or comanagement.³ This may be due to increased time pressures on surgeons and better outcomes of comanaged patients (eg, fewer postoperative complications, fewer transfers to an intensive care unit for acute medical deterioration, and increased likelihood to discharge to home).⁴

Medical management of surgical patients in the hospital involves a different skill set than that required to manage general medical patients. Accordingly, in 2012, the Accreditation Council for Graduate Medical Education (ACGME) made medical consultation and perioperative care an End of Training Entrustable Professional Activities and ACGME subcompetency. Earlier this year, a nationwide perioperative curriculum for graduate medical education was consisting of eight objective and core topic modules and pretest/posttest questions selected from SHMConsults.com, including assessment and management of perioperative cardiac and pulmonary risk and management of diabetes, perioperative fever, and anticoagulants. Trainees were assessed using the multiple-choice questions, observed mini-cex, and written evaluation of a consultation report. Despite this encouraging development of curricula and competencies for trainees, there are still important gaps in our knowledge of basic patterns for consultation practices. For example, the type of patients and medical conditions currently encountered on our medical consultation and comanagement services had been previously unknown.

In the December issue of the Journal of Hospital Medicine, Wang et al. answer this question through the first cross-sectional multicenter prospective survey to examine medical consultation/comanagement practices since observational studies in the 1970-1990s.⁶ In a sample of 1,264 consultation requests from 11 academic medical centers over four two-week periods from July 2014 through July 2015, they found that the most common requests for consultation were medical management/comanagement, preoperative evaluation, blood pressure management, and other common postoperative complications, including postoperative atrial fibrillation, heart failure, renal failure, hyponatremia, anemia, hypoxia, and altered mental status.⁹ The majority of referrals were from orthopedic surgery and neurosurgery. They also found that medical consultants and comanagers provided comprehensive evaluations where more than a third of encounters addressed issues that were not stated in the initial reason for consult (RFC) and that consultants addressed more than two RFCs per encounter.⁹

These findings illustrate the paradigm shift of medical consultation focusing on a single specific question to addressing and optimizing the entire patient. This shift toward a broader, more open-ended reason for consultation may present some challenges such as “dumping” where referring surgeons and other specialists signoff their patients after surgery is completed, with internists processing the surgeons’ patients through the hospitalization. These challenges can be mitigated with predefined comanagement agreements with clearly defined roles and collaborative professional relationships.

Nonetheless, given the recent developments in curricula and training competencies mentioned above, internists are better equipped than ever before to put everything together and take care of the medical conditions of the increasingly complex and older surgical patient. For example, if one is consulted to see a patient for postoperative hypertension, it is difficult to not address the patient’s blood sugars in the 300s, lack of venous thromboembolism prophylaxis, delirium, acute renal failure, and acute blood loss anemia. The authors are correct to assert it is critically important to ensure that this input is desired by the referring physician either via verbal communication or comanagement agreements.

The findings of Wang et al. suggest some important future steps in medical consultation to ensure that our trainees and colleagues are prepared to take care of the entire patient regardless of whether the patient is on a consultant or comanagement agreement. This study shows that trainees are exposed to a diverse clinical experience on our medical consultation and comanagement services, which is in accordance with the objectives, assessment tools, and modules of the nationwide curriculum. It is likely that comanagement services will continue to expand as more of our medically complex patients will need either elective or emergency surgeries and surgeons have become less comfortable managing these patients on their own. We also may be asked to participate in quality improvement initiatives in the management of surgical patients, including the “perioperative surgical home programs,” where physicians work on a patient-centered approach to the surgical patient using evidence-based standard clinical care pathways and transitions from before surgery to postdischarge.⁷ We should share our experiences in quality improvement and the patient-centered medical home to ensure that our patients are optimized for surgery and beyond. As Lee Goldman et al. stated in the “Ten Commandments for Effective Consultations,¹” consultative medicine is an important part of an internal medicine practice. Today, more than ever, the consultant or comanagement role or roles need to be carefully defined and clear communication and follow-up are important.

Whenever the principles of effective medical consultation are discussed, a classic article published in 1983 by Lee Goldman et al. is invariably referenced. In the “Ten Commandments for Effective Consultation,” Goldman argued that internists should “determine the question, establish urgency, look for yourself, be as brief as appropriate, be specific, provide contingency plans, honor thy turf, teach with tact, provide direct personal contact, and follow up.”¹ If these Ten Commandments were followed, then the consultation would be more effective and satisfactory for both the consultant and the referring provider. However, with the advent of comanagement in 1994 where internists and surgeons have a “shared responsibility and accountability,”² there has been a shift, and the once-concrete definitions of a specific reason for consult and the nature of “turf” have become blurred. Since 1994, the use of medical consultation and comanagement has skyrocketed, and today, more than 50% of surgical patients have a medical consultation or comanagement.³ This may be due to increased time pressures on surgeons and better outcomes of comanaged patients (eg, fewer postoperative complications, fewer transfers to an intensive care unit for acute medical deterioration, and increased likelihood to discharge to home).⁴

Medical management of surgical patients in the hospital involves a different skill set than that required to manage general medical patients. Accordingly, in 2012, the Accreditation Council for Graduate Medical Education (ACGME) made medical consultation and perioperative care an End of Training Entrustable Professional Activities and ACGME subcompetency. Earlier this year, a nationwide perioperative curriculum for graduate medical education was consisting of eight objective and core topic modules and pretest/posttest questions selected from SHMConsults.com, including assessment and management of perioperative cardiac and pulmonary risk and management of diabetes, perioperative fever, and anticoagulants. Trainees were assessed using the multiple-choice questions, observed mini-cex, and written evaluation of a consultation report. Despite this encouraging development of curricula and competencies for trainees, there are still important gaps in our knowledge of basic patterns for consultation practices. For example, the type of patients and medical conditions currently encountered on our medical consultation and comanagement services had been previously unknown.

In the December issue of the Journal of Hospital Medicine, Wang et al. answer this question through the first cross-sectional multicenter prospective survey to examine medical consultation/comanagement practices since observational studies in the 1970-1990s.⁶ In a sample of 1,264 consultation requests from 11 academic medical centers over four two-week periods from July 2014 through July 2015, they found that the most common requests for consultation were medical management/comanagement, preoperative evaluation, blood pressure management, and other common postoperative complications, including postoperative atrial fibrillation, heart failure, renal failure, hyponatremia, anemia, hypoxia, and altered mental status.⁹ The majority of referrals were from orthopedic surgery and neurosurgery. They also found that medical consultants and comanagers provided comprehensive evaluations where more than a third of encounters addressed issues that were not stated in the initial reason for consult (RFC) and that consultants addressed more than two RFCs per encounter.⁹

These findings illustrate the paradigm shift of medical consultation focusing on a single specific question to addressing and optimizing the entire patient. This shift toward a broader, more open-ended reason for consultation may present some challenges such as “dumping” where referring surgeons and other specialists signoff their patients after surgery is completed, with internists processing the surgeons’ patients through the hospitalization. These challenges can be mitigated with predefined comanagement agreements with clearly defined roles and collaborative professional relationships.

Nonetheless, given the recent developments in curricula and training competencies mentioned above, internists are better equipped than ever before to put everything together and take care of the medical conditions of the increasingly complex and older surgical patient. For example, if one is consulted to see a patient for postoperative hypertension, it is difficult to not address the patient’s blood sugars in the 300s, lack of venous thromboembolism prophylaxis, delirium, acute renal failure, and acute blood loss anemia. The authors are correct to assert it is critically important to ensure that this input is desired by the referring physician either via verbal communication or comanagement agreements.

The findings of Wang et al. suggest some important future steps in medical consultation to ensure that our trainees and colleagues are prepared to take care of the entire patient regardless of whether the patient is on a consultant or comanagement agreement. This study shows that trainees are exposed to a diverse clinical experience on our medical consultation and comanagement services, which is in accordance with the objectives, assessment tools, and modules of the nationwide curriculum. It is likely that comanagement services will continue to expand as more of our medically complex patients will need either elective or emergency surgeries and surgeons have become less comfortable managing these patients on their own. We also may be asked to participate in quality improvement initiatives in the management of surgical patients, including the “perioperative surgical home programs,” where physicians work on a patient-centered approach to the surgical patient using evidence-based standard clinical care pathways and transitions from before surgery to postdischarge.⁷ We should share our experiences in quality improvement and the patient-centered medical home to ensure that our patients are optimized for surgery and beyond. As Lee Goldman et al. stated in the “Ten Commandments for Effective Consultations,¹” consultative medicine is an important part of an internal medicine practice. Today, more than ever, the consultant or comanagement role or roles need to be carefully defined and clear communication and follow-up are important.

Whenever the principles of effective medical consultation are discussed, a classic article published in 1983 by Lee Goldman et al. is invariably referenced. In the “Ten Commandments for Effective Consultation,” Goldman argued that internists should “determine the question, establish urgency, look for yourself, be as brief as appropriate, be specific, provide contingency plans, honor thy turf, teach with tact, provide direct personal contact, and follow up.”¹ If these Ten Commandments were followed, then the consultation would be more effective and satisfactory for both the consultant and the referring provider. However, with the advent of comanagement in 1994 where internists and surgeons have a “shared responsibility and accountability,”² there has been a shift, and the once-concrete definitions of a specific reason for consult and the nature of “turf” have become blurred. Since 1994, the use of medical consultation and comanagement has skyrocketed, and today, more than 50% of surgical patients have a medical consultation or comanagement.³ This may be due to increased time pressures on surgeons and better outcomes of comanaged patients (eg, fewer postoperative complications, fewer transfers to an intensive care unit for acute medical deterioration, and increased likelihood to discharge to home).⁴

Medical management of surgical patients in the hospital involves a different skill set than that required to manage general medical patients. Accordingly, in 2012, the Accreditation Council for Graduate Medical Education (ACGME) made medical consultation and perioperative care an End of Training Entrustable Professional Activities and ACGME subcompetency. Earlier this year, a nationwide perioperative curriculum for graduate medical education was consisting of eight objective and core topic modules and pretest/posttest questions selected from SHMConsults.com, including assessment and management of perioperative cardiac and pulmonary risk and management of diabetes, perioperative fever, and anticoagulants. Trainees were assessed using the multiple-choice questions, observed mini-cex, and written evaluation of a consultation report. Despite this encouraging development of curricula and competencies for trainees, there are still important gaps in our knowledge of basic patterns for consultation practices. For example, the type of patients and medical conditions currently encountered on our medical consultation and comanagement services had been previously unknown.

In the December issue of the Journal of Hospital Medicine, Wang et al. answer this question through the first cross-sectional multicenter prospective survey to examine medical consultation/comanagement practices since observational studies in the 1970-1990s.⁶ In a sample of 1,264 consultation requests from 11 academic medical centers over four two-week periods from July 2014 through July 2015, they found that the most common requests for consultation were medical management/comanagement, preoperative evaluation, blood pressure management, and other common postoperative complications, including postoperative atrial fibrillation, heart failure, renal failure, hyponatremia, anemia, hypoxia, and altered mental status.⁹ The majority of referrals were from orthopedic surgery and neurosurgery. They also found that medical consultants and comanagers provided comprehensive evaluations where more than a third of encounters addressed issues that were not stated in the initial reason for consult (RFC) and that consultants addressed more than two RFCs per encounter.⁹

These findings illustrate the paradigm shift of medical consultation focusing on a single specific question to addressing and optimizing the entire patient. This shift toward a broader, more open-ended reason for consultation may present some challenges such as “dumping” where referring surgeons and other specialists signoff their patients after surgery is completed, with internists processing the surgeons’ patients through the hospitalization. These challenges can be mitigated with predefined comanagement agreements with clearly defined roles and collaborative professional relationships.

Nonetheless, given the recent developments in curricula and training competencies mentioned above, internists are better equipped than ever before to put everything together and take care of the medical conditions of the increasingly complex and older surgical patient. For example, if one is consulted to see a patient for postoperative hypertension, it is difficult to not address the patient’s blood sugars in the 300s, lack of venous thromboembolism prophylaxis, delirium, acute renal failure, and acute blood loss anemia. The authors are correct to assert it is critically important to ensure that this input is desired by the referring physician either via verbal communication or comanagement agreements.

The findings of Wang et al. suggest some important future steps in medical consultation to ensure that our trainees and colleagues are prepared to take care of the entire patient regardless of whether the patient is on a consultant or comanagement agreement. This study shows that trainees are exposed to a diverse clinical experience on our medical consultation and comanagement services, which is in accordance with the objectives, assessment tools, and modules of the nationwide curriculum. It is likely that comanagement services will continue to expand as more of our medically complex patients will need either elective or emergency surgeries and surgeons have become less comfortable managing these patients on their own. We also may be asked to participate in quality improvement initiatives in the management of surgical patients, including the “perioperative surgical home programs,” where physicians work on a patient-centered approach to the surgical patient using evidence-based standard clinical care pathways and transitions from before surgery to postdischarge.⁷ We should share our experiences in quality improvement and the patient-centered medical home to ensure that our patients are optimized for surgery and beyond. As Lee Goldman et al. stated in the “Ten Commandments for Effective Consultations,¹” consultative medicine is an important part of an internal medicine practice. Today, more than ever, the consultant or comanagement role or roles need to be carefully defined and clear communication and follow-up are important.