Mark V. Williams, MD

RAPID GROWTH AND THE FUTURE OF ACOs

WHAT HOSPITALISTS NEED TO KNOW ABOUT ACOs

HOW HOSPITALISTS CONTRIBUTE VALUE TO ACO SUCCESS

CONCLUSION

RAPID GROWTH AND THE FUTURE OF ACOs

WHAT HOSPITALISTS NEED TO KNOW ABOUT ACOs

HOW HOSPITALISTS CONTRIBUTE VALUE TO ACO SUCCESS

CONCLUSION

RAPID GROWTH AND THE FUTURE OF ACOs

WHAT HOSPITALISTS NEED TO KNOW ABOUT ACOs

HOW HOSPITALISTS CONTRIBUTE VALUE TO ACO SUCCESS

CONCLUSION

METHODS

RESULTS

DISCUSSION

METHODS

RESULTS

DISCUSSION

METHODS

RESULTS

DISCUSSION

Internal medicine (IM) residents and hospitalist physicians commonly conduct bedside thoracenteses for both diagnostic and therapeutic purposes.[1] The American Board of Internal Medicine only requires that certification candidates understand the indications, complications, and management of thoracenteses.[2] A disconnect between clinical practice patterns and board requirements may increase patient risk because poorly trained physicians are more likely to cause complications.[3] National practice patterns show that many thoracenteses are referred to interventional radiology (IR).[4] However, research links performance of bedside procedures to reduced hospital length of stay and lower costs, without increasing risk of complications.[1, 5, 6]

Simulation‐based education offers a controlled environment where trainees improve procedural knowledge and skills without patient harm.[7] Simulation‐based mastery learning (SBML) is a rigorous form of competency‐based education that improves clinical skills and reduces iatrogenic complications and healthcare costs.[5, 6, 8] SBML also is an effective method to boost thoracentesis skills among IM residents.[9] However, there are no data to show that thoracentesis skills acquired in the simulation laboratory transfer to clinical environments and affect referral patterns.

We hypothesized that a thoracentesis SBML intervention would improve skills and increase procedural self‐confidence while reducing procedure referrals. This study aimed to (1) assess the effect of thoracentesis SBML on a cohort of IM residents' simulated skills and (2) compare traditionally trained (nonSBML‐trained) residents, SBML‐trained residents, and hospitalist physicians regarding procedure referral patterns, self‐confidence, procedure experience, and reasons for referral.

METHODS AND MATERIALS

RESULTS

DISCUSSION

This study confirms earlier research showing that thoracentesis SBML improves residents' clinical skills, but is the first to use a randomized study design.[9] Use of the mastery model in health professions education ensures that all learners are competent to provide patient care including performing invasive procedures. Such rigorous education yields downstream translational outcomes including safety profiles comparable to experts.[1, 6]

This study also shows that SBML‐trained residents displayed higher self‐confidence and performed significantly more bedside procedures than traditionally trained residents and more experienced hospitalist physicians. Although the Society of Hospital Medicine considers thoracentesis skills a core competency for hospitalist physicians,[11] we speculate that some hospitalist physicians had not performed a thoracentesis in years. A recent national survey showed that only 44% of hospitalist physicians performed at least 1 thoracentesis within the past year.[10] Research also shows a shift in medical culture to refer procedures to specialty services, such as IR, by over 900% in the past 2 decades.[4] Our results provide novel information about procedure referrals because we show that SBML provides translational outcomes by improving skills and self‐confidence that influence referral patterns. SBML‐trained residents performed almost a quarter of procedures at the bedside. Although this only represents an 8% absolute difference in bedside procedures compared to traditionally trained residents, if a large number of residents are trained using SBML this results in a meaningful number of procedures shifted to the patient bedside. According to University HealthSystem Consortium data, in US teaching hospitals, approximately 35,325 thoracenteses are performed yearly.[1] Shifting even 8% of these procedures to the bedside would result in significant clinical benefit and cost savings. Reduced referrals increase additional bedside procedures that are safe, cost‐effective, and highly satisfying to patients.[1, 12, 13] Further study is required to determine the impact on referral patterns after providing SMBL training to attending physicians.

Our study also provides information about the rationale for procedure referrals. Earlier work speculates that financial incentive, training and time may explain high procedure referral rates.[10] One report on IM residents noted an 87% IR referral rate for thoracentesis, and confirmed that both training and time were major reasons.[14] Hospitalist physicians reported lack of time as the major factor leading to procedural referrals, which is problematic because bedside procedures yield similar clinical outcomes at lower costs.[1, 12] Attending preference also prevented 11 additional bedside procedures in the SBML‐trained group. Schedule adjustments and SBML training of hospitalist physicians should be considered, because bundled payments in the Affordable Care Act may favor shifting to the higher‐value approach of bedside thoracenteses.[15]

Our study has several limitations. First, we only performed surveys at 1 institution and the results may not be generalizable. Second, we relied on an electronic query to alert us to thoracenteses. Our query may have missed procedures that were unsuccessful or did not have EHR orders entered. Third, physicians may have been surveyed more than once for different or the same patient(s), but opinions may have shifted over time. Fourth, some items such as time needed to be written in the survey and were not specifically asked. This could have resulted in under‐reporting. Finally, we did not assess the clinical outcomes of thoracenteses in this study, although earlier work shows that residents who complete SBML have safety outcomes similar to IR.[1, 6]

In summary, IM residents who complete thoracentesis SBML demonstrate improved clinical skills and are more likely to perform bedside procedures. In an era of bundled payments, rethinking current care models to promote cost‐effective care is necessary. We believe providing additional education, training, and support to hospitalist physicians to promote bedside procedures is a promising strategy that warrants further study.

Internal medicine (IM) residents and hospitalist physicians commonly conduct bedside thoracenteses for both diagnostic and therapeutic purposes.[1] The American Board of Internal Medicine only requires that certification candidates understand the indications, complications, and management of thoracenteses.[2] A disconnect between clinical practice patterns and board requirements may increase patient risk because poorly trained physicians are more likely to cause complications.[3] National practice patterns show that many thoracenteses are referred to interventional radiology (IR).[4] However, research links performance of bedside procedures to reduced hospital length of stay and lower costs, without increasing risk of complications.[1, 5, 6]

Simulation‐based education offers a controlled environment where trainees improve procedural knowledge and skills without patient harm.[7] Simulation‐based mastery learning (SBML) is a rigorous form of competency‐based education that improves clinical skills and reduces iatrogenic complications and healthcare costs.[5, 6, 8] SBML also is an effective method to boost thoracentesis skills among IM residents.[9] However, there are no data to show that thoracentesis skills acquired in the simulation laboratory transfer to clinical environments and affect referral patterns.

We hypothesized that a thoracentesis SBML intervention would improve skills and increase procedural self‐confidence while reducing procedure referrals. This study aimed to (1) assess the effect of thoracentesis SBML on a cohort of IM residents' simulated skills and (2) compare traditionally trained (nonSBML‐trained) residents, SBML‐trained residents, and hospitalist physicians regarding procedure referral patterns, self‐confidence, procedure experience, and reasons for referral.

METHODS AND MATERIALS

RESULTS

DISCUSSION

This study confirms earlier research showing that thoracentesis SBML improves residents' clinical skills, but is the first to use a randomized study design.[9] Use of the mastery model in health professions education ensures that all learners are competent to provide patient care including performing invasive procedures. Such rigorous education yields downstream translational outcomes including safety profiles comparable to experts.[1, 6]

This study also shows that SBML‐trained residents displayed higher self‐confidence and performed significantly more bedside procedures than traditionally trained residents and more experienced hospitalist physicians. Although the Society of Hospital Medicine considers thoracentesis skills a core competency for hospitalist physicians,[11] we speculate that some hospitalist physicians had not performed a thoracentesis in years. A recent national survey showed that only 44% of hospitalist physicians performed at least 1 thoracentesis within the past year.[10] Research also shows a shift in medical culture to refer procedures to specialty services, such as IR, by over 900% in the past 2 decades.[4] Our results provide novel information about procedure referrals because we show that SBML provides translational outcomes by improving skills and self‐confidence that influence referral patterns. SBML‐trained residents performed almost a quarter of procedures at the bedside. Although this only represents an 8% absolute difference in bedside procedures compared to traditionally trained residents, if a large number of residents are trained using SBML this results in a meaningful number of procedures shifted to the patient bedside. According to University HealthSystem Consortium data, in US teaching hospitals, approximately 35,325 thoracenteses are performed yearly.[1] Shifting even 8% of these procedures to the bedside would result in significant clinical benefit and cost savings. Reduced referrals increase additional bedside procedures that are safe, cost‐effective, and highly satisfying to patients.[1, 12, 13] Further study is required to determine the impact on referral patterns after providing SMBL training to attending physicians.

Our study also provides information about the rationale for procedure referrals. Earlier work speculates that financial incentive, training and time may explain high procedure referral rates.[10] One report on IM residents noted an 87% IR referral rate for thoracentesis, and confirmed that both training and time were major reasons.[14] Hospitalist physicians reported lack of time as the major factor leading to procedural referrals, which is problematic because bedside procedures yield similar clinical outcomes at lower costs.[1, 12] Attending preference also prevented 11 additional bedside procedures in the SBML‐trained group. Schedule adjustments and SBML training of hospitalist physicians should be considered, because bundled payments in the Affordable Care Act may favor shifting to the higher‐value approach of bedside thoracenteses.[15]

Our study has several limitations. First, we only performed surveys at 1 institution and the results may not be generalizable. Second, we relied on an electronic query to alert us to thoracenteses. Our query may have missed procedures that were unsuccessful or did not have EHR orders entered. Third, physicians may have been surveyed more than once for different or the same patient(s), but opinions may have shifted over time. Fourth, some items such as time needed to be written in the survey and were not specifically asked. This could have resulted in under‐reporting. Finally, we did not assess the clinical outcomes of thoracenteses in this study, although earlier work shows that residents who complete SBML have safety outcomes similar to IR.[1, 6]

In summary, IM residents who complete thoracentesis SBML demonstrate improved clinical skills and are more likely to perform bedside procedures. In an era of bundled payments, rethinking current care models to promote cost‐effective care is necessary. We believe providing additional education, training, and support to hospitalist physicians to promote bedside procedures is a promising strategy that warrants further study.

Internal medicine (IM) residents and hospitalist physicians commonly conduct bedside thoracenteses for both diagnostic and therapeutic purposes.[1] The American Board of Internal Medicine only requires that certification candidates understand the indications, complications, and management of thoracenteses.[2] A disconnect between clinical practice patterns and board requirements may increase patient risk because poorly trained physicians are more likely to cause complications.[3] National practice patterns show that many thoracenteses are referred to interventional radiology (IR).[4] However, research links performance of bedside procedures to reduced hospital length of stay and lower costs, without increasing risk of complications.[1, 5, 6]

Simulation‐based education offers a controlled environment where trainees improve procedural knowledge and skills without patient harm.[7] Simulation‐based mastery learning (SBML) is a rigorous form of competency‐based education that improves clinical skills and reduces iatrogenic complications and healthcare costs.[5, 6, 8] SBML also is an effective method to boost thoracentesis skills among IM residents.[9] However, there are no data to show that thoracentesis skills acquired in the simulation laboratory transfer to clinical environments and affect referral patterns.

We hypothesized that a thoracentesis SBML intervention would improve skills and increase procedural self‐confidence while reducing procedure referrals. This study aimed to (1) assess the effect of thoracentesis SBML on a cohort of IM residents' simulated skills and (2) compare traditionally trained (nonSBML‐trained) residents, SBML‐trained residents, and hospitalist physicians regarding procedure referral patterns, self‐confidence, procedure experience, and reasons for referral.

METHODS AND MATERIALS

RESULTS

DISCUSSION

This study confirms earlier research showing that thoracentesis SBML improves residents' clinical skills, but is the first to use a randomized study design.[9] Use of the mastery model in health professions education ensures that all learners are competent to provide patient care including performing invasive procedures. Such rigorous education yields downstream translational outcomes including safety profiles comparable to experts.[1, 6]

This study also shows that SBML‐trained residents displayed higher self‐confidence and performed significantly more bedside procedures than traditionally trained residents and more experienced hospitalist physicians. Although the Society of Hospital Medicine considers thoracentesis skills a core competency for hospitalist physicians,[11] we speculate that some hospitalist physicians had not performed a thoracentesis in years. A recent national survey showed that only 44% of hospitalist physicians performed at least 1 thoracentesis within the past year.[10] Research also shows a shift in medical culture to refer procedures to specialty services, such as IR, by over 900% in the past 2 decades.[4] Our results provide novel information about procedure referrals because we show that SBML provides translational outcomes by improving skills and self‐confidence that influence referral patterns. SBML‐trained residents performed almost a quarter of procedures at the bedside. Although this only represents an 8% absolute difference in bedside procedures compared to traditionally trained residents, if a large number of residents are trained using SBML this results in a meaningful number of procedures shifted to the patient bedside. According to University HealthSystem Consortium data, in US teaching hospitals, approximately 35,325 thoracenteses are performed yearly.[1] Shifting even 8% of these procedures to the bedside would result in significant clinical benefit and cost savings. Reduced referrals increase additional bedside procedures that are safe, cost‐effective, and highly satisfying to patients.[1, 12, 13] Further study is required to determine the impact on referral patterns after providing SMBL training to attending physicians.

Our study also provides information about the rationale for procedure referrals. Earlier work speculates that financial incentive, training and time may explain high procedure referral rates.[10] One report on IM residents noted an 87% IR referral rate for thoracentesis, and confirmed that both training and time were major reasons.[14] Hospitalist physicians reported lack of time as the major factor leading to procedural referrals, which is problematic because bedside procedures yield similar clinical outcomes at lower costs.[1, 12] Attending preference also prevented 11 additional bedside procedures in the SBML‐trained group. Schedule adjustments and SBML training of hospitalist physicians should be considered, because bundled payments in the Affordable Care Act may favor shifting to the higher‐value approach of bedside thoracenteses.[15]

Our study has several limitations. First, we only performed surveys at 1 institution and the results may not be generalizable. Second, we relied on an electronic query to alert us to thoracenteses. Our query may have missed procedures that were unsuccessful or did not have EHR orders entered. Third, physicians may have been surveyed more than once for different or the same patient(s), but opinions may have shifted over time. Fourth, some items such as time needed to be written in the survey and were not specifically asked. This could have resulted in under‐reporting. Finally, we did not assess the clinical outcomes of thoracenteses in this study, although earlier work shows that residents who complete SBML have safety outcomes similar to IR.[1, 6]

In summary, IM residents who complete thoracentesis SBML demonstrate improved clinical skills and are more likely to perform bedside procedures. In an era of bundled payments, rethinking current care models to promote cost‐effective care is necessary. We believe providing additional education, training, and support to hospitalist physicians to promote bedside procedures is a promising strategy that warrants further study.

Hospital readmissions have a significant impact on the healthcare system. Medicare data suggest a 19% all‐cause 30‐day readmission rate, of which 47% may be preventable.[1, 2] The Centers for Medicare & Medicaid Services continue to expand their criteria of disease states that will be penalized for readmissions, now reducing hospital reimbursement rates up to 3%. Pharmacists, by optimizing patient utilization of medications, can play a valuable role in contributing to preventing readmissions.[3]

Lack of acceptable transitional care is a serious problem that is consistently identified in the literature.[4] Transitional care involves 3 domains of transfer: information, education, and destination. A breakdown in any of these components can negatively impact patients and their caregivers.

Prior studies consistently demonstrated a high likelihood of adverse drug events (ADEs) and patients' lack of knowledge regarding medications postdischarge, both of which can lead to readmission. Forster and colleagues found that 19% to 23% of patients experienced an ADE within 5 weeks of discharge from an inpatient visit, 66% to 72% of which were drug related, and approximately one‐third were deemed preventable.[5, 6] One survey found that less than 60% of patients knew the indication for a new medication prescribed at discharge, whereas only 12% reported knowledge of an anticipated ADE.[7]

Pharmacists can play a large role in the information and education aspect of transitional care. Previous studies demonstrate that pharmacist involvement in the discharge process can reduce the incidence of ADEs and have a positive impact on patient satisfaction. There are conflicting data regarding the effect of comprehensive medication education and follow‐up calls by pharmacy team members on ADEs and medication errors (MEs).[3, 8, 9] Although overall pharmacist participation has shown positive patient‐related outcomes, the impact of pharmacists' involvement on readmissions has not been consistently demonstrated.[10, 11, 12, 13, 14]

Our study evaluated the impact of the pharmacy team in the transitions‐of‐care settings in a unique combination utilizing the pharmacist during medication reconciliation, discharge, and with 3 follow‐up phone call interactions postdischarge. Our study was designed to evaluate the impact of intensive pharmacist involvement during the acute care admission as well as for a 30‐day time period postdischarge on both ADEs and readmissions.

METHODS

All patients were admitted to hospitalist‐based internal medicine units at Northwestern Memorial Hospital, an 894‐bed academic medical center located in Chicago, Illinois. Patients were randomized by study investigators using a random number generator to either the usual care or intervention arms and then evaluated each day for eligibility to participate in the study. Patients remained blinded throughout the study. Patients met inclusion criteria if they were discharged to home and either discharged on greater than 3 scheduled prescription medications or discharged with at least 1 high‐risk medication. High‐risk medications were classified as anticoagulants, antiplatelets (eg, aspirin and clopidogrel), hypoglycemic agents (eg, insulin), immunosuppressants, or anti‐infectives. Patients also needed to participate in a minimum of 1 postdischarge phone call or experience an emergency department (ED) visit or readmission within 30 days of discharge to meet inclusion criteria. Exclusion criteria included: impaired cognition based on Mini‐Cog screening assessment scale, unable or unwilling to provide informed consent, lack of a personal phone number, nonEnglish speaking, subsequent elective readmission within 30 days of initial visit, more than 3 previous hospital admissions in the past 2 months, palliative care or home/skilled nursing hospice, anticipated length of survival less than 3 months, discharged within 24 hours of admission, discharged against medical advice, or discharged before medication education was conducted (Figure 1). Patients who met inclusion criteria provided informed consent, received a Mini‐Cog screening assessment, and were given the Rapid Estimate of Adult Literacy in Medicine revised (REALM‐R) assessment to evaluate health literacy. The REALM‐R is a word recognition test designed to identify patients at risk for poor health literacy skills. Patients with REALM‐R scores of 6 or less are considered to have low health literacy.[15] Patients were randomized to receive either the usual care or pharmacist‐directed medication evaluation and management as described in Table 1. Patients included in the study were contacted by phone postdischarge, with 3 attempts on consecutive days. Patients who were readmitted as an inpatient or had an ED visit were not contacted for the study after that point.

Figure 1

Patients enrolled in the control group received the usual standard of care by a clinical pharmacist. This included a medication reconciliation completed from the admitting physician's patient history and physical and medication counseling provided by the physician or nursing staff at discharge. Patients were not interviewed face‐to‐face on admission and did not receive discharge counseling by a pharmacy team member. Patients were assessed daily by the pharmacist for evaluation of the pharmacotherapy plans and presence of MEs or safety‐related concerns. The control group received 1 postdischarge phone call from a pharmacist at day 30 to assess for study endpoints of ADEs, MEs, ED visit, and readmission only. The endpoints of ADEs and MEs were determined by professional judgment by the clinical pharmacist based on an algorithm similar to National Coordinating Council for Medication Error Reporting and Prevention, although a specific tool was not utilized.

The study group received face‐to‐face medication reconciliation on admission by a pharmacist or a pharmacy student. Prior to discharge, a personalized medication plan was created by the pharmacist and discussed with the physician. Medication discrepancies were addressed prior to the discharge instructions being given and discussed with the patient. Medication counseling was performed at discharge by the pharmacist or pharmacy student. Patients received 3 phone calls at 3, 14, and 30 days postdischarge. The presence of ADEs and MEs were evaluated during each phone call. The patients were asked to confirm their medication regimens including drug, indication, dose, route, and frequency. They were also asked questions regarding possible side effects, new symptoms, and any changes to their current therapy. The calls focused on clarifying the pharmacy discharge plan, resolving any unanswered questions or medication‐related issues, identifying and overcoming any barriers to adherence, and assistance with providing patients access to medications by contacting pharmacies and physicians to resolve and troubleshoot further prescription claims and clarifications. Pharmacists performed all postdischarge phone calls. Pharmacy students were able to provide face‐to‐face medication reconciliation upon admission and discharge counseling under the supervision of the pharmacist for the intervention arm.

The patient Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) responses to the medication domain question, Did you clearly understand the purpose for taking each of your medications at the time of discharge? were collected for the 2 designated hospitalist units for both the control and study groups. HCAHPS scores were collected at the 6 months point prior to the study initiation and throughout the 6‐month study period for the control and intervention groups. A physician and 2 pharmacists, who were blinded to the study randomization and results, assessed all Northwestern Memorial Hospital readmissions to determine if the readmissions were medication‐related or not.

This study obtained institutional review board approval from Northwestern University.

RESULTS

Over the course of 7 months, 341 patients were enrolled in the study, 189 in the control arm and 152 in the study arm. Forty‐eight patients in the control group and 15 patients in the study group were lost to follow‐up. The final analysis included 278 patients, 141 in the control group and 137 in the study group. Patients were eligible for study inclusion if they received at least 1 phone call, which resulted in more patients being lost to follow‐up in the control arm due to fewer total phone call attempts. Demographic and disposition data for the control and study groups are shown in Table 2. Baseline characteristics between the 2 groups were similar with the exception of total medications at time of discharge. The control group had more total medications at discharge compared to the study group (7.2 vs 6.4, P=0.04). The number of high‐risk medications and the number of scheduled medications were similar between both groups. During medication reconciliation, 380 discrepancies (46.2%) were found in the study group compared to 205 (19.9%) in the control group (P<0.0001). The higher number of identified discrepancies in the study group was expected due to the fact that the pharmacist did not complete a face‐to‐face medication history in the control patients. The average length of stay, REALM‐R scores, and reason for admissions were similar between the 2 groups (Table 2).

A total of 55 patients (39%) in the control arm were readmitted to an inpatient hospital or had an ED visit within 30‐days postdischarge compared to 34 patients (24.8%) in the study group (P=0.001) (Table 3). Of the patients readmitted to the ED, 21 were enrolled in the control arm (14.8%) compared to only 6 patients in the study arm (4.4%) (P=0.005). Reviewers concluded that 24% of the control group readmissions were medication‐related versus 23% of the study group (P=1.0). In total, 78 out of 89 readmissions were to Northwestern Memorial Hospital. Medication‐related causes to outside institutions were not evaluated. The causes for all readmissions were not evaluated.

A sensitivity analysis was undertaken to understand the impact of the lost to follow‐up rate in both the control and study groups. Undertaking an assumption that all 15 patients lost to follow‐up in the study group were readmitted and that 15 of 48 patients lost to follow‐up in the control group were readmitted, the intervention continued to show a significant benefit in reduction of composite ED and inpatient readmissions (35.7% study group vs 49.6% control group, P=0.022)

Multivariate logistic regression analysis that controlled for Charlson Comorbidity Index score, length of stay, total number of medications on discharge, and payer type showed an adjusted odds ratio of 0.55 (95% confidence interval [CI]: 0.32‐0.94) in the intervention cohort compared to controls for the combined endpoint of readmission and ED visit within 30‐days postdischarge. The adjusted odds ratio for 30‐day readmission alone was 0.88 (95% CI: 0.49‐1.61).

Eighteen of the 86 control patients who received a 30‐day postdischarge phone call experienced an ADE or ME compared to 11 of the 83 study patients (P=0.22). Patient satisfaction scores of both designated units as represented by the HCAHPS score in the medication knowledge domain increased from the prestudy period. Patients selected agree or strongly agree only 47% of the time at the 6‐month prestudy point compared to 56% of the time during the 6‐month study period.

DISCUSSION

Although previous studies show conflicting results regarding the impact of pharmacist interventions on readmissions, our study demonstrated a decrease in the composite measure of inpatient readmissions and ED visits. Its success stresses the need for a comprehensive approach that contains continuity of care by healthcare providers to reconcile and manage medications throughout the hospital stay, extending up to a full month postdischarge with multiple phone calls. This included (1) face‐to‐face medication reconciliation on admission, (2) development of a personalized medication plan discussed with the patient's physician, (3) addressing any medication discrepancies to the discharge instructions being given to the patient, (4) medication counseling performed at discharge, and (5) 3 postdischarge phone calls at 3, 14, and 30 days.

A study conducted in 2001 analyzed the Medicare Current Beneficiary Survey (MCBS) and found that living alone, having limited education, and lack of self‐management skills have significant associations with early readmission.[16] Approximately 80 million Americans have limited health literacy and are associated with poor health outcomes and healthcare utilization as seen in a review completed by Berkman and colleagues.[17] Because no difference was found between both groups, it would suggest health literacy did not influence or bias the study group. Additionally, no statistically different medication issues, such as total number of medications or rates of ADEs and MEs, were identified in the patients of this study. This may be explained by the small, final population size at the 30‐day period or that the impact of the pharmacist intervention did not reach the threshold that this study was powered to detect. Also, a lack of statistical significance may be due to the subjective nature of ADEs/MEs and the prevention of ADEs/MEs throughout all patients' hospitalizations from the clinical pharmacist's involvement in care, which was not collected. Although a combined endpoint collecting readmission to either the ED or rehospitalization was lower in the intervention cohort, the isolated rehospitalization endpoint was not significantly different between the 2 groups. ED utilization was markedly decreased, but we may have lacked the power to show a statistically significant decrease in rehospitalization. These results mirror those of the Project RED (Re‐Engineered Discharge) intervention.[17]

HCAHPS surveys are sent to only a small percent of randomly selected patients who are discharged from the hospital. Thus, respondents may or may not have been included in the study, indicating a possible greater impact of the intervention on individual patients than collected. Importantly, the described interventions appeared to improve patients' perception of understanding the purpose of their medications. We found that HCAHPS scores across the 2 units improved, though the intervention only impacted 16.8% of all patients discharged from these units due to the nature of the survey distribution.

The pharmacists' abilities to educate all eligible patients prior to discharge from 7:30 _am to 4:00 _pm each day of the week was a limitation of this study, as some patients were discharged outside of the duty hours. This may have allowed for a differential exclusion and could have led to selection bias. Another limitation is that a large number of patients were lost to follow‐up in the control group, likely because the first postdischarge contact with patients was not until the day 30 phone calls. The extensive exclusion criteria caused many patients not to be enrolled. Though the intervention arm received postdischarge phone calls at days 3 and 14, only postdischarge call‐backs at day 30 of the intervention arm were compared to the control arm, which could have led to bias in the 30‐day analysis of the intervention arm, as patients may have not reported previous issues that were resolved in earlier phone calls. Medication‐related readmissions were not statistically different between the groups, which could suggest that the difference in readmissions were not solely due to the intervention, and a decrease in healthcare utilization may be due to chance. The subjective nature of how ADEs and MEs were collected also serves as a limitation, as they were only screened for presence or absence and not classified by severity or category. This study was at a single‐center academic institution, which may limit the ability to apply the results to other institutions. Last, outcome assessments relied on participant report, including ADE and ME occurrence and presentation at outside hospitals. Future study evaluation conducted as a multicenter design while continuing to strengthen the continuity of the healthcare provider and patient relationship at each intervention would be ideal. Also, having an objective measure of ADEs and MEs with severity categorization would be beneficial.

Compared to previous literature, our study design was unique in the number of phone calls made to patients postdischarge and its prospective, randomized design. In the previously mentioned study by Walker et al., phone calls were made only at days 3 and 30.[13] Although the majority of readmissions occurred within the first 14 days of discharge, additional visits to the ED and readmissions may have been avoided by contacting patients twice within the critical 14‐day period. Another distinction of this study design was the expansion of a rather limited and peripheral pharmacist role in transitions of care to a much more integrated participation. We believe the relationship developed between patients and their pharmacy care team provided coordination and the continuity of communication regarding their care. Additionally, our study was unique through the use of pharmacy extenders via fourth‐year pharmacy students who were completing their advanced pharmacy practice rotations. Pharmacy extenders can also be certified and trained pharmacy technicians, which many hospitals utilize to perform medication reconciliations at a lower cost than pharmacists. As hospitals face increased demands to shrink budgets due to decreasing reimbursements, healthcare systems will be forced to find creative new ways to use existing resources.

In conclusion, transition of care is a high‐risk situation for many patients. A comprehensive approach by healthcare providers, including pharmacists and pharmacy extenders, may have a positive impact in reducing or preventing ADEs/MEs, inpatient admissions, and ED visits. Although our study focused directly on the impact of a pharmacy care team on transitions‐of‐care, we cannot conclude this applies strictly to pharmacists. Across the nation, the role of various disciplines of healthcare providers in admission, hospitalization, discharge, and postdischarge is not standardized and varies significantly by institution. Importantly, no mechanism currently exists to directly reimburse for such efforts, but demonstration of cost effectiveness through reduced posthospital utilization may justify this investment for accountable care organizations.[18]

Hospital readmissions have a significant impact on the healthcare system. Medicare data suggest a 19% all‐cause 30‐day readmission rate, of which 47% may be preventable.[1, 2] The Centers for Medicare & Medicaid Services continue to expand their criteria of disease states that will be penalized for readmissions, now reducing hospital reimbursement rates up to 3%. Pharmacists, by optimizing patient utilization of medications, can play a valuable role in contributing to preventing readmissions.[3]

Lack of acceptable transitional care is a serious problem that is consistently identified in the literature.[4] Transitional care involves 3 domains of transfer: information, education, and destination. A breakdown in any of these components can negatively impact patients and their caregivers.

Prior studies consistently demonstrated a high likelihood of adverse drug events (ADEs) and patients' lack of knowledge regarding medications postdischarge, both of which can lead to readmission. Forster and colleagues found that 19% to 23% of patients experienced an ADE within 5 weeks of discharge from an inpatient visit, 66% to 72% of which were drug related, and approximately one‐third were deemed preventable.[5, 6] One survey found that less than 60% of patients knew the indication for a new medication prescribed at discharge, whereas only 12% reported knowledge of an anticipated ADE.[7]

Pharmacists can play a large role in the information and education aspect of transitional care. Previous studies demonstrate that pharmacist involvement in the discharge process can reduce the incidence of ADEs and have a positive impact on patient satisfaction. There are conflicting data regarding the effect of comprehensive medication education and follow‐up calls by pharmacy team members on ADEs and medication errors (MEs).[3, 8, 9] Although overall pharmacist participation has shown positive patient‐related outcomes, the impact of pharmacists' involvement on readmissions has not been consistently demonstrated.[10, 11, 12, 13, 14]

Our study evaluated the impact of the pharmacy team in the transitions‐of‐care settings in a unique combination utilizing the pharmacist during medication reconciliation, discharge, and with 3 follow‐up phone call interactions postdischarge. Our study was designed to evaluate the impact of intensive pharmacist involvement during the acute care admission as well as for a 30‐day time period postdischarge on both ADEs and readmissions.

METHODS

All patients were admitted to hospitalist‐based internal medicine units at Northwestern Memorial Hospital, an 894‐bed academic medical center located in Chicago, Illinois. Patients were randomized by study investigators using a random number generator to either the usual care or intervention arms and then evaluated each day for eligibility to participate in the study. Patients remained blinded throughout the study. Patients met inclusion criteria if they were discharged to home and either discharged on greater than 3 scheduled prescription medications or discharged with at least 1 high‐risk medication. High‐risk medications were classified as anticoagulants, antiplatelets (eg, aspirin and clopidogrel), hypoglycemic agents (eg, insulin), immunosuppressants, or anti‐infectives. Patients also needed to participate in a minimum of 1 postdischarge phone call or experience an emergency department (ED) visit or readmission within 30 days of discharge to meet inclusion criteria. Exclusion criteria included: impaired cognition based on Mini‐Cog screening assessment scale, unable or unwilling to provide informed consent, lack of a personal phone number, nonEnglish speaking, subsequent elective readmission within 30 days of initial visit, more than 3 previous hospital admissions in the past 2 months, palliative care or home/skilled nursing hospice, anticipated length of survival less than 3 months, discharged within 24 hours of admission, discharged against medical advice, or discharged before medication education was conducted (Figure 1). Patients who met inclusion criteria provided informed consent, received a Mini‐Cog screening assessment, and were given the Rapid Estimate of Adult Literacy in Medicine revised (REALM‐R) assessment to evaluate health literacy. The REALM‐R is a word recognition test designed to identify patients at risk for poor health literacy skills. Patients with REALM‐R scores of 6 or less are considered to have low health literacy.[15] Patients were randomized to receive either the usual care or pharmacist‐directed medication evaluation and management as described in Table 1. Patients included in the study were contacted by phone postdischarge, with 3 attempts on consecutive days. Patients who were readmitted as an inpatient or had an ED visit were not contacted for the study after that point.

Figure 1

Patients enrolled in the control group received the usual standard of care by a clinical pharmacist. This included a medication reconciliation completed from the admitting physician's patient history and physical and medication counseling provided by the physician or nursing staff at discharge. Patients were not interviewed face‐to‐face on admission and did not receive discharge counseling by a pharmacy team member. Patients were assessed daily by the pharmacist for evaluation of the pharmacotherapy plans and presence of MEs or safety‐related concerns. The control group received 1 postdischarge phone call from a pharmacist at day 30 to assess for study endpoints of ADEs, MEs, ED visit, and readmission only. The endpoints of ADEs and MEs were determined by professional judgment by the clinical pharmacist based on an algorithm similar to National Coordinating Council for Medication Error Reporting and Prevention, although a specific tool was not utilized.

The study group received face‐to‐face medication reconciliation on admission by a pharmacist or a pharmacy student. Prior to discharge, a personalized medication plan was created by the pharmacist and discussed with the physician. Medication discrepancies were addressed prior to the discharge instructions being given and discussed with the patient. Medication counseling was performed at discharge by the pharmacist or pharmacy student. Patients received 3 phone calls at 3, 14, and 30 days postdischarge. The presence of ADEs and MEs were evaluated during each phone call. The patients were asked to confirm their medication regimens including drug, indication, dose, route, and frequency. They were also asked questions regarding possible side effects, new symptoms, and any changes to their current therapy. The calls focused on clarifying the pharmacy discharge plan, resolving any unanswered questions or medication‐related issues, identifying and overcoming any barriers to adherence, and assistance with providing patients access to medications by contacting pharmacies and physicians to resolve and troubleshoot further prescription claims and clarifications. Pharmacists performed all postdischarge phone calls. Pharmacy students were able to provide face‐to‐face medication reconciliation upon admission and discharge counseling under the supervision of the pharmacist for the intervention arm.

The patient Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) responses to the medication domain question, Did you clearly understand the purpose for taking each of your medications at the time of discharge? were collected for the 2 designated hospitalist units for both the control and study groups. HCAHPS scores were collected at the 6 months point prior to the study initiation and throughout the 6‐month study period for the control and intervention groups. A physician and 2 pharmacists, who were blinded to the study randomization and results, assessed all Northwestern Memorial Hospital readmissions to determine if the readmissions were medication‐related or not.

This study obtained institutional review board approval from Northwestern University.

RESULTS

Over the course of 7 months, 341 patients were enrolled in the study, 189 in the control arm and 152 in the study arm. Forty‐eight patients in the control group and 15 patients in the study group were lost to follow‐up. The final analysis included 278 patients, 141 in the control group and 137 in the study group. Patients were eligible for study inclusion if they received at least 1 phone call, which resulted in more patients being lost to follow‐up in the control arm due to fewer total phone call attempts. Demographic and disposition data for the control and study groups are shown in Table 2. Baseline characteristics between the 2 groups were similar with the exception of total medications at time of discharge. The control group had more total medications at discharge compared to the study group (7.2 vs 6.4, P=0.04). The number of high‐risk medications and the number of scheduled medications were similar between both groups. During medication reconciliation, 380 discrepancies (46.2%) were found in the study group compared to 205 (19.9%) in the control group (P<0.0001). The higher number of identified discrepancies in the study group was expected due to the fact that the pharmacist did not complete a face‐to‐face medication history in the control patients. The average length of stay, REALM‐R scores, and reason for admissions were similar between the 2 groups (Table 2).

A total of 55 patients (39%) in the control arm were readmitted to an inpatient hospital or had an ED visit within 30‐days postdischarge compared to 34 patients (24.8%) in the study group (P=0.001) (Table 3). Of the patients readmitted to the ED, 21 were enrolled in the control arm (14.8%) compared to only 6 patients in the study arm (4.4%) (P=0.005). Reviewers concluded that 24% of the control group readmissions were medication‐related versus 23% of the study group (P=1.0). In total, 78 out of 89 readmissions were to Northwestern Memorial Hospital. Medication‐related causes to outside institutions were not evaluated. The causes for all readmissions were not evaluated.

A sensitivity analysis was undertaken to understand the impact of the lost to follow‐up rate in both the control and study groups. Undertaking an assumption that all 15 patients lost to follow‐up in the study group were readmitted and that 15 of 48 patients lost to follow‐up in the control group were readmitted, the intervention continued to show a significant benefit in reduction of composite ED and inpatient readmissions (35.7% study group vs 49.6% control group, P=0.022)

Multivariate logistic regression analysis that controlled for Charlson Comorbidity Index score, length of stay, total number of medications on discharge, and payer type showed an adjusted odds ratio of 0.55 (95% confidence interval [CI]: 0.32‐0.94) in the intervention cohort compared to controls for the combined endpoint of readmission and ED visit within 30‐days postdischarge. The adjusted odds ratio for 30‐day readmission alone was 0.88 (95% CI: 0.49‐1.61).

Eighteen of the 86 control patients who received a 30‐day postdischarge phone call experienced an ADE or ME compared to 11 of the 83 study patients (P=0.22). Patient satisfaction scores of both designated units as represented by the HCAHPS score in the medication knowledge domain increased from the prestudy period. Patients selected agree or strongly agree only 47% of the time at the 6‐month prestudy point compared to 56% of the time during the 6‐month study period.

DISCUSSION

Although previous studies show conflicting results regarding the impact of pharmacist interventions on readmissions, our study demonstrated a decrease in the composite measure of inpatient readmissions and ED visits. Its success stresses the need for a comprehensive approach that contains continuity of care by healthcare providers to reconcile and manage medications throughout the hospital stay, extending up to a full month postdischarge with multiple phone calls. This included (1) face‐to‐face medication reconciliation on admission, (2) development of a personalized medication plan discussed with the patient's physician, (3) addressing any medication discrepancies to the discharge instructions being given to the patient, (4) medication counseling performed at discharge, and (5) 3 postdischarge phone calls at 3, 14, and 30 days.

A study conducted in 2001 analyzed the Medicare Current Beneficiary Survey (MCBS) and found that living alone, having limited education, and lack of self‐management skills have significant associations with early readmission.[16] Approximately 80 million Americans have limited health literacy and are associated with poor health outcomes and healthcare utilization as seen in a review completed by Berkman and colleagues.[17] Because no difference was found between both groups, it would suggest health literacy did not influence or bias the study group. Additionally, no statistically different medication issues, such as total number of medications or rates of ADEs and MEs, were identified in the patients of this study. This may be explained by the small, final population size at the 30‐day period or that the impact of the pharmacist intervention did not reach the threshold that this study was powered to detect. Also, a lack of statistical significance may be due to the subjective nature of ADEs/MEs and the prevention of ADEs/MEs throughout all patients' hospitalizations from the clinical pharmacist's involvement in care, which was not collected. Although a combined endpoint collecting readmission to either the ED or rehospitalization was lower in the intervention cohort, the isolated rehospitalization endpoint was not significantly different between the 2 groups. ED utilization was markedly decreased, but we may have lacked the power to show a statistically significant decrease in rehospitalization. These results mirror those of the Project RED (Re‐Engineered Discharge) intervention.[17]

HCAHPS surveys are sent to only a small percent of randomly selected patients who are discharged from the hospital. Thus, respondents may or may not have been included in the study, indicating a possible greater impact of the intervention on individual patients than collected. Importantly, the described interventions appeared to improve patients' perception of understanding the purpose of their medications. We found that HCAHPS scores across the 2 units improved, though the intervention only impacted 16.8% of all patients discharged from these units due to the nature of the survey distribution.

The pharmacists' abilities to educate all eligible patients prior to discharge from 7:30 _am to 4:00 _pm each day of the week was a limitation of this study, as some patients were discharged outside of the duty hours. This may have allowed for a differential exclusion and could have led to selection bias. Another limitation is that a large number of patients were lost to follow‐up in the control group, likely because the first postdischarge contact with patients was not until the day 30 phone calls. The extensive exclusion criteria caused many patients not to be enrolled. Though the intervention arm received postdischarge phone calls at days 3 and 14, only postdischarge call‐backs at day 30 of the intervention arm were compared to the control arm, which could have led to bias in the 30‐day analysis of the intervention arm, as patients may have not reported previous issues that were resolved in earlier phone calls. Medication‐related readmissions were not statistically different between the groups, which could suggest that the difference in readmissions were not solely due to the intervention, and a decrease in healthcare utilization may be due to chance. The subjective nature of how ADEs and MEs were collected also serves as a limitation, as they were only screened for presence or absence and not classified by severity or category. This study was at a single‐center academic institution, which may limit the ability to apply the results to other institutions. Last, outcome assessments relied on participant report, including ADE and ME occurrence and presentation at outside hospitals. Future study evaluation conducted as a multicenter design while continuing to strengthen the continuity of the healthcare provider and patient relationship at each intervention would be ideal. Also, having an objective measure of ADEs and MEs with severity categorization would be beneficial.

Compared to previous literature, our study design was unique in the number of phone calls made to patients postdischarge and its prospective, randomized design. In the previously mentioned study by Walker et al., phone calls were made only at days 3 and 30.[13] Although the majority of readmissions occurred within the first 14 days of discharge, additional visits to the ED and readmissions may have been avoided by contacting patients twice within the critical 14‐day period. Another distinction of this study design was the expansion of a rather limited and peripheral pharmacist role in transitions of care to a much more integrated participation. We believe the relationship developed between patients and their pharmacy care team provided coordination and the continuity of communication regarding their care. Additionally, our study was unique through the use of pharmacy extenders via fourth‐year pharmacy students who were completing their advanced pharmacy practice rotations. Pharmacy extenders can also be certified and trained pharmacy technicians, which many hospitals utilize to perform medication reconciliations at a lower cost than pharmacists. As hospitals face increased demands to shrink budgets due to decreasing reimbursements, healthcare systems will be forced to find creative new ways to use existing resources.

In conclusion, transition of care is a high‐risk situation for many patients. A comprehensive approach by healthcare providers, including pharmacists and pharmacy extenders, may have a positive impact in reducing or preventing ADEs/MEs, inpatient admissions, and ED visits. Although our study focused directly on the impact of a pharmacy care team on transitions‐of‐care, we cannot conclude this applies strictly to pharmacists. Across the nation, the role of various disciplines of healthcare providers in admission, hospitalization, discharge, and postdischarge is not standardized and varies significantly by institution. Importantly, no mechanism currently exists to directly reimburse for such efforts, but demonstration of cost effectiveness through reduced posthospital utilization may justify this investment for accountable care organizations.[18]

Hospital readmissions have a significant impact on the healthcare system. Medicare data suggest a 19% all‐cause 30‐day readmission rate, of which 47% may be preventable.[1, 2] The Centers for Medicare & Medicaid Services continue to expand their criteria of disease states that will be penalized for readmissions, now reducing hospital reimbursement rates up to 3%. Pharmacists, by optimizing patient utilization of medications, can play a valuable role in contributing to preventing readmissions.[3]

Lack of acceptable transitional care is a serious problem that is consistently identified in the literature.[4] Transitional care involves 3 domains of transfer: information, education, and destination. A breakdown in any of these components can negatively impact patients and their caregivers.

Prior studies consistently demonstrated a high likelihood of adverse drug events (ADEs) and patients' lack of knowledge regarding medications postdischarge, both of which can lead to readmission. Forster and colleagues found that 19% to 23% of patients experienced an ADE within 5 weeks of discharge from an inpatient visit, 66% to 72% of which were drug related, and approximately one‐third were deemed preventable.[5, 6] One survey found that less than 60% of patients knew the indication for a new medication prescribed at discharge, whereas only 12% reported knowledge of an anticipated ADE.[7]

Pharmacists can play a large role in the information and education aspect of transitional care. Previous studies demonstrate that pharmacist involvement in the discharge process can reduce the incidence of ADEs and have a positive impact on patient satisfaction. There are conflicting data regarding the effect of comprehensive medication education and follow‐up calls by pharmacy team members on ADEs and medication errors (MEs).[3, 8, 9] Although overall pharmacist participation has shown positive patient‐related outcomes, the impact of pharmacists' involvement on readmissions has not been consistently demonstrated.[10, 11, 12, 13, 14]

Our study evaluated the impact of the pharmacy team in the transitions‐of‐care settings in a unique combination utilizing the pharmacist during medication reconciliation, discharge, and with 3 follow‐up phone call interactions postdischarge. Our study was designed to evaluate the impact of intensive pharmacist involvement during the acute care admission as well as for a 30‐day time period postdischarge on both ADEs and readmissions.

METHODS

All patients were admitted to hospitalist‐based internal medicine units at Northwestern Memorial Hospital, an 894‐bed academic medical center located in Chicago, Illinois. Patients were randomized by study investigators using a random number generator to either the usual care or intervention arms and then evaluated each day for eligibility to participate in the study. Patients remained blinded throughout the study. Patients met inclusion criteria if they were discharged to home and either discharged on greater than 3 scheduled prescription medications or discharged with at least 1 high‐risk medication. High‐risk medications were classified as anticoagulants, antiplatelets (eg, aspirin and clopidogrel), hypoglycemic agents (eg, insulin), immunosuppressants, or anti‐infectives. Patients also needed to participate in a minimum of 1 postdischarge phone call or experience an emergency department (ED) visit or readmission within 30 days of discharge to meet inclusion criteria. Exclusion criteria included: impaired cognition based on Mini‐Cog screening assessment scale, unable or unwilling to provide informed consent, lack of a personal phone number, nonEnglish speaking, subsequent elective readmission within 30 days of initial visit, more than 3 previous hospital admissions in the past 2 months, palliative care or home/skilled nursing hospice, anticipated length of survival less than 3 months, discharged within 24 hours of admission, discharged against medical advice, or discharged before medication education was conducted (Figure 1). Patients who met inclusion criteria provided informed consent, received a Mini‐Cog screening assessment, and were given the Rapid Estimate of Adult Literacy in Medicine revised (REALM‐R) assessment to evaluate health literacy. The REALM‐R is a word recognition test designed to identify patients at risk for poor health literacy skills. Patients with REALM‐R scores of 6 or less are considered to have low health literacy.[15] Patients were randomized to receive either the usual care or pharmacist‐directed medication evaluation and management as described in Table 1. Patients included in the study were contacted by phone postdischarge, with 3 attempts on consecutive days. Patients who were readmitted as an inpatient or had an ED visit were not contacted for the study after that point.

Figure 1

Patients enrolled in the control group received the usual standard of care by a clinical pharmacist. This included a medication reconciliation completed from the admitting physician's patient history and physical and medication counseling provided by the physician or nursing staff at discharge. Patients were not interviewed face‐to‐face on admission and did not receive discharge counseling by a pharmacy team member. Patients were assessed daily by the pharmacist for evaluation of the pharmacotherapy plans and presence of MEs or safety‐related concerns. The control group received 1 postdischarge phone call from a pharmacist at day 30 to assess for study endpoints of ADEs, MEs, ED visit, and readmission only. The endpoints of ADEs and MEs were determined by professional judgment by the clinical pharmacist based on an algorithm similar to National Coordinating Council for Medication Error Reporting and Prevention, although a specific tool was not utilized.

The study group received face‐to‐face medication reconciliation on admission by a pharmacist or a pharmacy student. Prior to discharge, a personalized medication plan was created by the pharmacist and discussed with the physician. Medication discrepancies were addressed prior to the discharge instructions being given and discussed with the patient. Medication counseling was performed at discharge by the pharmacist or pharmacy student. Patients received 3 phone calls at 3, 14, and 30 days postdischarge. The presence of ADEs and MEs were evaluated during each phone call. The patients were asked to confirm their medication regimens including drug, indication, dose, route, and frequency. They were also asked questions regarding possible side effects, new symptoms, and any changes to their current therapy. The calls focused on clarifying the pharmacy discharge plan, resolving any unanswered questions or medication‐related issues, identifying and overcoming any barriers to adherence, and assistance with providing patients access to medications by contacting pharmacies and physicians to resolve and troubleshoot further prescription claims and clarifications. Pharmacists performed all postdischarge phone calls. Pharmacy students were able to provide face‐to‐face medication reconciliation upon admission and discharge counseling under the supervision of the pharmacist for the intervention arm.

The patient Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) responses to the medication domain question, Did you clearly understand the purpose for taking each of your medications at the time of discharge? were collected for the 2 designated hospitalist units for both the control and study groups. HCAHPS scores were collected at the 6 months point prior to the study initiation and throughout the 6‐month study period for the control and intervention groups. A physician and 2 pharmacists, who were blinded to the study randomization and results, assessed all Northwestern Memorial Hospital readmissions to determine if the readmissions were medication‐related or not.

This study obtained institutional review board approval from Northwestern University.

RESULTS

Over the course of 7 months, 341 patients were enrolled in the study, 189 in the control arm and 152 in the study arm. Forty‐eight patients in the control group and 15 patients in the study group were lost to follow‐up. The final analysis included 278 patients, 141 in the control group and 137 in the study group. Patients were eligible for study inclusion if they received at least 1 phone call, which resulted in more patients being lost to follow‐up in the control arm due to fewer total phone call attempts. Demographic and disposition data for the control and study groups are shown in Table 2. Baseline characteristics between the 2 groups were similar with the exception of total medications at time of discharge. The control group had more total medications at discharge compared to the study group (7.2 vs 6.4, P=0.04). The number of high‐risk medications and the number of scheduled medications were similar between both groups. During medication reconciliation, 380 discrepancies (46.2%) were found in the study group compared to 205 (19.9%) in the control group (P<0.0001). The higher number of identified discrepancies in the study group was expected due to the fact that the pharmacist did not complete a face‐to‐face medication history in the control patients. The average length of stay, REALM‐R scores, and reason for admissions were similar between the 2 groups (Table 2).

A total of 55 patients (39%) in the control arm were readmitted to an inpatient hospital or had an ED visit within 30‐days postdischarge compared to 34 patients (24.8%) in the study group (P=0.001) (Table 3). Of the patients readmitted to the ED, 21 were enrolled in the control arm (14.8%) compared to only 6 patients in the study arm (4.4%) (P=0.005). Reviewers concluded that 24% of the control group readmissions were medication‐related versus 23% of the study group (P=1.0). In total, 78 out of 89 readmissions were to Northwestern Memorial Hospital. Medication‐related causes to outside institutions were not evaluated. The causes for all readmissions were not evaluated.

A sensitivity analysis was undertaken to understand the impact of the lost to follow‐up rate in both the control and study groups. Undertaking an assumption that all 15 patients lost to follow‐up in the study group were readmitted and that 15 of 48 patients lost to follow‐up in the control group were readmitted, the intervention continued to show a significant benefit in reduction of composite ED and inpatient readmissions (35.7% study group vs 49.6% control group, P=0.022)

Multivariate logistic regression analysis that controlled for Charlson Comorbidity Index score, length of stay, total number of medications on discharge, and payer type showed an adjusted odds ratio of 0.55 (95% confidence interval [CI]: 0.32‐0.94) in the intervention cohort compared to controls for the combined endpoint of readmission and ED visit within 30‐days postdischarge. The adjusted odds ratio for 30‐day readmission alone was 0.88 (95% CI: 0.49‐1.61).

Eighteen of the 86 control patients who received a 30‐day postdischarge phone call experienced an ADE or ME compared to 11 of the 83 study patients (P=0.22). Patient satisfaction scores of both designated units as represented by the HCAHPS score in the medication knowledge domain increased from the prestudy period. Patients selected agree or strongly agree only 47% of the time at the 6‐month prestudy point compared to 56% of the time during the 6‐month study period.

DISCUSSION

Although previous studies show conflicting results regarding the impact of pharmacist interventions on readmissions, our study demonstrated a decrease in the composite measure of inpatient readmissions and ED visits. Its success stresses the need for a comprehensive approach that contains continuity of care by healthcare providers to reconcile and manage medications throughout the hospital stay, extending up to a full month postdischarge with multiple phone calls. This included (1) face‐to‐face medication reconciliation on admission, (2) development of a personalized medication plan discussed with the patient's physician, (3) addressing any medication discrepancies to the discharge instructions being given to the patient, (4) medication counseling performed at discharge, and (5) 3 postdischarge phone calls at 3, 14, and 30 days.

A study conducted in 2001 analyzed the Medicare Current Beneficiary Survey (MCBS) and found that living alone, having limited education, and lack of self‐management skills have significant associations with early readmission.[16] Approximately 80 million Americans have limited health literacy and are associated with poor health outcomes and healthcare utilization as seen in a review completed by Berkman and colleagues.[17] Because no difference was found between both groups, it would suggest health literacy did not influence or bias the study group. Additionally, no statistically different medication issues, such as total number of medications or rates of ADEs and MEs, were identified in the patients of this study. This may be explained by the small, final population size at the 30‐day period or that the impact of the pharmacist intervention did not reach the threshold that this study was powered to detect. Also, a lack of statistical significance may be due to the subjective nature of ADEs/MEs and the prevention of ADEs/MEs throughout all patients' hospitalizations from the clinical pharmacist's involvement in care, which was not collected. Although a combined endpoint collecting readmission to either the ED or rehospitalization was lower in the intervention cohort, the isolated rehospitalization endpoint was not significantly different between the 2 groups. ED utilization was markedly decreased, but we may have lacked the power to show a statistically significant decrease in rehospitalization. These results mirror those of the Project RED (Re‐Engineered Discharge) intervention.[17]

HCAHPS surveys are sent to only a small percent of randomly selected patients who are discharged from the hospital. Thus, respondents may or may not have been included in the study, indicating a possible greater impact of the intervention on individual patients than collected. Importantly, the described interventions appeared to improve patients' perception of understanding the purpose of their medications. We found that HCAHPS scores across the 2 units improved, though the intervention only impacted 16.8% of all patients discharged from these units due to the nature of the survey distribution.

The pharmacists' abilities to educate all eligible patients prior to discharge from 7:30 _am to 4:00 _pm each day of the week was a limitation of this study, as some patients were discharged outside of the duty hours. This may have allowed for a differential exclusion and could have led to selection bias. Another limitation is that a large number of patients were lost to follow‐up in the control group, likely because the first postdischarge contact with patients was not until the day 30 phone calls. The extensive exclusion criteria caused many patients not to be enrolled. Though the intervention arm received postdischarge phone calls at days 3 and 14, only postdischarge call‐backs at day 30 of the intervention arm were compared to the control arm, which could have led to bias in the 30‐day analysis of the intervention arm, as patients may have not reported previous issues that were resolved in earlier phone calls. Medication‐related readmissions were not statistically different between the groups, which could suggest that the difference in readmissions were not solely due to the intervention, and a decrease in healthcare utilization may be due to chance. The subjective nature of how ADEs and MEs were collected also serves as a limitation, as they were only screened for presence or absence and not classified by severity or category. This study was at a single‐center academic institution, which may limit the ability to apply the results to other institutions. Last, outcome assessments relied on participant report, including ADE and ME occurrence and presentation at outside hospitals. Future study evaluation conducted as a multicenter design while continuing to strengthen the continuity of the healthcare provider and patient relationship at each intervention would be ideal. Also, having an objective measure of ADEs and MEs with severity categorization would be beneficial.

Compared to previous literature, our study design was unique in the number of phone calls made to patients postdischarge and its prospective, randomized design. In the previously mentioned study by Walker et al., phone calls were made only at days 3 and 30.[13] Although the majority of readmissions occurred within the first 14 days of discharge, additional visits to the ED and readmissions may have been avoided by contacting patients twice within the critical 14‐day period. Another distinction of this study design was the expansion of a rather limited and peripheral pharmacist role in transitions of care to a much more integrated participation. We believe the relationship developed between patients and their pharmacy care team provided coordination and the continuity of communication regarding their care. Additionally, our study was unique through the use of pharmacy extenders via fourth‐year pharmacy students who were completing their advanced pharmacy practice rotations. Pharmacy extenders can also be certified and trained pharmacy technicians, which many hospitals utilize to perform medication reconciliations at a lower cost than pharmacists. As hospitals face increased demands to shrink budgets due to decreasing reimbursements, healthcare systems will be forced to find creative new ways to use existing resources.

In conclusion, transition of care is a high‐risk situation for many patients. A comprehensive approach by healthcare providers, including pharmacists and pharmacy extenders, may have a positive impact in reducing or preventing ADEs/MEs, inpatient admissions, and ED visits. Although our study focused directly on the impact of a pharmacy care team on transitions‐of‐care, we cannot conclude this applies strictly to pharmacists. Across the nation, the role of various disciplines of healthcare providers in admission, hospitalization, discharge, and postdischarge is not standardized and varies significantly by institution. Importantly, no mechanism currently exists to directly reimburse for such efforts, but demonstration of cost effectiveness through reduced posthospital utilization may justify this investment for accountable care organizations.[18]

We greatly appreciate the thoughtful points made by Dr. Kerman regarding our recently published study evaluating the association of hospitalist continuity on adverse events (AEs).[1] We agree that a 7‐on/7‐off staffing model may limit discontinuity relative to models using shorter rotations lengths. Many hospital medicine programs use a 7‐on/7‐off model to optimize continuity. Longer rotation lengths are uncommon, as they may lead to fatigue and negatively affect physician work‐life balance. Shorter rotation lengths do exist, and we acknowledge that a study in a setting with greater fragmentation may have detected an effect.

We respectfully disagree with Dr. Kerman's concern that our methods for AE detection and confirmation may have been insensitive. We did not rely on incident reports, as these systems suffer from under‐reporting and often represent only a fraction of true AEs. We used a modified version of the classic 2‐stage method to identify and confirm AEs.[2] In the first stage, we used computerized screens, based on criteria from the Harvard Medical Practice Study and Institute for Healthcare Improvement global trigger tool, to identify potential AEs.[3, 4, 5] A research nurse created narrative summaries of potential AEs. A physician researcher then reviewed the narrative summaries to confirm whether an AE was truly present. This time‐consuming method is much more sensitive and specific than other options for patient safety measurement, including administrative data analyses and incident reporting systems.[6, 7]

With respect to other outcomes that may be affected by hospitalist continuity, we recently published a separate study showing that lower inpatient physician continuity was significantly associated with modest increases in hospital costs.[8] We found no association between continuity and patient satisfaction, but were likely underpowered to detect one. Interestingly, some of the models in our study suggested a slightly reduced risk of readmission with lower continuity. We were surprised by this finding and hypothesized that countervailing forces may be at play during handoffs of care from 1 hospitalist to another. Transitions of care introduce the opportunity for critical information to be lost, but they also introduce the potential for patient reassessment. A hospitalist newly taking over care from another may not be anchored to the initial diagnostic impressions and management plan established by the first. Of course, the potential benefit of a reassessment could only occur if the new hospitalist has time to perform one. At extremely high patient volumes, this theoretical benefit is unlikely to exist.

We did not include length of stay (LOS) as an outcome because hospitalist continuity and LOS are interdependent. Although discontinuity may lead to longer LOS, longer LOS definitely increases the probability of discontinuity. Thus, we controlled for LOS in our statistical models to isolate the effect of continuity. The study by Epstein and colleagues did not take into account the interdependence between LOS and hospitalist continuity.[9] Observational studies are not ideal for determining the effect of continuity on LOS. The Combing Incentives and Continuity Leading to Efficiency (CICLE) study by Chandra and colleagues was a pre‐post evaluation of a hospitalist staffing model specifically designed to improve continuity.[10] In the CICLE model, physicians work in a 4‐day rotation. On day 1, physicians exclusively admit patients. On day 2, physicians care for patients admitted on day 1 and accept patients admitted overnight. On days 3 and 4, physicians continue to care for patients received on days 1 and 2, but receive no additional patients. The remaining patients are transitioned to the next physician entering the cycle at the end of day 4. Chandra and colleagues found a 7.5% reduction in LOS and an 8.5% reduction in charges. Interestingly, they also found a 13.5% increase in readmissions that did not achieve statistical significance (P=0.08). The CICLE study suggests continuity does affect LOS, but is limited in that it did not account for a potential preexisting trend toward lower LOS.

Dr. Kerman presents data showing that it takes longer for a physician to care for a patient who is new to him or her than for a patient who is previously known. This finding has face validity. However, as we have suggested, the extra time spent by the oncoming physician may have both advantages and disadvantages. The disadvantages include time‐consuming cognitive work for the physician and the potential for information loss affecting patient care. The potential advantage is a second physician reassessing the diagnosis and management decisions established by the first, potentially correcting errors and optimizing care.

Ultimately, more research is needed to illuminate the effect of hospitalist continuity on patient outcomes. For now, we feel that hospital medicine group leaders need not institute lengthy rotations or staffing models that prioritize continuity above all other factors, as continuity appears to have little impact on patient outcomes.

We greatly appreciate the thoughtful points made by Dr. Kerman regarding our recently published study evaluating the association of hospitalist continuity on adverse events (AEs).[1] We agree that a 7‐on/7‐off staffing model may limit discontinuity relative to models using shorter rotations lengths. Many hospital medicine programs use a 7‐on/7‐off model to optimize continuity. Longer rotation lengths are uncommon, as they may lead to fatigue and negatively affect physician work‐life balance. Shorter rotation lengths do exist, and we acknowledge that a study in a setting with greater fragmentation may have detected an effect.

We respectfully disagree with Dr. Kerman's concern that our methods for AE detection and confirmation may have been insensitive. We did not rely on incident reports, as these systems suffer from under‐reporting and often represent only a fraction of true AEs. We used a modified version of the classic 2‐stage method to identify and confirm AEs.[2] In the first stage, we used computerized screens, based on criteria from the Harvard Medical Practice Study and Institute for Healthcare Improvement global trigger tool, to identify potential AEs.[3, 4, 5] A research nurse created narrative summaries of potential AEs. A physician researcher then reviewed the narrative summaries to confirm whether an AE was truly present. This time‐consuming method is much more sensitive and specific than other options for patient safety measurement, including administrative data analyses and incident reporting systems.[6, 7]

With respect to other outcomes that may be affected by hospitalist continuity, we recently published a separate study showing that lower inpatient physician continuity was significantly associated with modest increases in hospital costs.[8] We found no association between continuity and patient satisfaction, but were likely underpowered to detect one. Interestingly, some of the models in our study suggested a slightly reduced risk of readmission with lower continuity. We were surprised by this finding and hypothesized that countervailing forces may be at play during handoffs of care from 1 hospitalist to another. Transitions of care introduce the opportunity for critical information to be lost, but they also introduce the potential for patient reassessment. A hospitalist newly taking over care from another may not be anchored to the initial diagnostic impressions and management plan established by the first. Of course, the potential benefit of a reassessment could only occur if the new hospitalist has time to perform one. At extremely high patient volumes, this theoretical benefit is unlikely to exist.

We did not include length of stay (LOS) as an outcome because hospitalist continuity and LOS are interdependent. Although discontinuity may lead to longer LOS, longer LOS definitely increases the probability of discontinuity. Thus, we controlled for LOS in our statistical models to isolate the effect of continuity. The study by Epstein and colleagues did not take into account the interdependence between LOS and hospitalist continuity.[9] Observational studies are not ideal for determining the effect of continuity on LOS. The Combing Incentives and Continuity Leading to Efficiency (CICLE) study by Chandra and colleagues was a pre‐post evaluation of a hospitalist staffing model specifically designed to improve continuity.[10] In the CICLE model, physicians work in a 4‐day rotation. On day 1, physicians exclusively admit patients. On day 2, physicians care for patients admitted on day 1 and accept patients admitted overnight. On days 3 and 4, physicians continue to care for patients received on days 1 and 2, but receive no additional patients. The remaining patients are transitioned to the next physician entering the cycle at the end of day 4. Chandra and colleagues found a 7.5% reduction in LOS and an 8.5% reduction in charges. Interestingly, they also found a 13.5% increase in readmissions that did not achieve statistical significance (P=0.08). The CICLE study suggests continuity does affect LOS, but is limited in that it did not account for a potential preexisting trend toward lower LOS.

Dr. Kerman presents data showing that it takes longer for a physician to care for a patient who is new to him or her than for a patient who is previously known. This finding has face validity. However, as we have suggested, the extra time spent by the oncoming physician may have both advantages and disadvantages. The disadvantages include time‐consuming cognitive work for the physician and the potential for information loss affecting patient care. The potential advantage is a second physician reassessing the diagnosis and management decisions established by the first, potentially correcting errors and optimizing care.

Ultimately, more research is needed to illuminate the effect of hospitalist continuity on patient outcomes. For now, we feel that hospital medicine group leaders need not institute lengthy rotations or staffing models that prioritize continuity above all other factors, as continuity appears to have little impact on patient outcomes.

We greatly appreciate the thoughtful points made by Dr. Kerman regarding our recently published study evaluating the association of hospitalist continuity on adverse events (AEs).[1] We agree that a 7‐on/7‐off staffing model may limit discontinuity relative to models using shorter rotations lengths. Many hospital medicine programs use a 7‐on/7‐off model to optimize continuity. Longer rotation lengths are uncommon, as they may lead to fatigue and negatively affect physician work‐life balance. Shorter rotation lengths do exist, and we acknowledge that a study in a setting with greater fragmentation may have detected an effect.

We respectfully disagree with Dr. Kerman's concern that our methods for AE detection and confirmation may have been insensitive. We did not rely on incident reports, as these systems suffer from under‐reporting and often represent only a fraction of true AEs. We used a modified version of the classic 2‐stage method to identify and confirm AEs.[2] In the first stage, we used computerized screens, based on criteria from the Harvard Medical Practice Study and Institute for Healthcare Improvement global trigger tool, to identify potential AEs.[3, 4, 5] A research nurse created narrative summaries of potential AEs. A physician researcher then reviewed the narrative summaries to confirm whether an AE was truly present. This time‐consuming method is much more sensitive and specific than other options for patient safety measurement, including administrative data analyses and incident reporting systems.[6, 7]

With respect to other outcomes that may be affected by hospitalist continuity, we recently published a separate study showing that lower inpatient physician continuity was significantly associated with modest increases in hospital costs.[8] We found no association between continuity and patient satisfaction, but were likely underpowered to detect one. Interestingly, some of the models in our study suggested a slightly reduced risk of readmission with lower continuity. We were surprised by this finding and hypothesized that countervailing forces may be at play during handoffs of care from 1 hospitalist to another. Transitions of care introduce the opportunity for critical information to be lost, but they also introduce the potential for patient reassessment. A hospitalist newly taking over care from another may not be anchored to the initial diagnostic impressions and management plan established by the first. Of course, the potential benefit of a reassessment could only occur if the new hospitalist has time to perform one. At extremely high patient volumes, this theoretical benefit is unlikely to exist.

We did not include length of stay (LOS) as an outcome because hospitalist continuity and LOS are interdependent. Although discontinuity may lead to longer LOS, longer LOS definitely increases the probability of discontinuity. Thus, we controlled for LOS in our statistical models to isolate the effect of continuity. The study by Epstein and colleagues did not take into account the interdependence between LOS and hospitalist continuity.[9] Observational studies are not ideal for determining the effect of continuity on LOS. The Combing Incentives and Continuity Leading to Efficiency (CICLE) study by Chandra and colleagues was a pre‐post evaluation of a hospitalist staffing model specifically designed to improve continuity.[10] In the CICLE model, physicians work in a 4‐day rotation. On day 1, physicians exclusively admit patients. On day 2, physicians care for patients admitted on day 1 and accept patients admitted overnight. On days 3 and 4, physicians continue to care for patients received on days 1 and 2, but receive no additional patients. The remaining patients are transitioned to the next physician entering the cycle at the end of day 4. Chandra and colleagues found a 7.5% reduction in LOS and an 8.5% reduction in charges. Interestingly, they also found a 13.5% increase in readmissions that did not achieve statistical significance (P=0.08). The CICLE study suggests continuity does affect LOS, but is limited in that it did not account for a potential preexisting trend toward lower LOS.

Dr. Kerman presents data showing that it takes longer for a physician to care for a patient who is new to him or her than for a patient who is previously known. This finding has face validity. However, as we have suggested, the extra time spent by the oncoming physician may have both advantages and disadvantages. The disadvantages include time‐consuming cognitive work for the physician and the potential for information loss affecting patient care. The potential advantage is a second physician reassessing the diagnosis and management decisions established by the first, potentially correcting errors and optimizing care.

Ultimately, more research is needed to illuminate the effect of hospitalist continuity on patient outcomes. For now, we feel that hospital medicine group leaders need not institute lengthy rotations or staffing models that prioritize continuity above all other factors, as continuity appears to have little impact on patient outcomes.

The national healthcare improvement paradigm is shifting toward a more comprehensive, value‐focused, and patient‐centered approach. Reducing hospital readmissions has become a focal point as a policy strategy to improve care quality while reducing cost. Section 3025 of the Affordable Care Act mandated the Centers for Medicare and Medicaid Services to make progressive reductions in Medicare payments to hospitals that have higher than expected readmission rates for 3 conditions (heart failure, acute myocardial infarction, and pneumonia), and expanding to include chronic obstructive pulmonary disease and total hip and knee arthroplasty in 2015.[1] In response, hospitals and systems are developing and implementing programs that coordinate care beyond hospital walls to reduce readmissions and healthcare costs.[2, 3] However, patients are readmitted for a variety of reasons, and programs that address the needs of some may not address the distinct needs of others. Understanding the characteristics of patients with frequent readmissions will permit the well‐informed creation of solutions specific to this population to reduce cost, free resources, and provide better care.

Although a solid body of literature already exists that describes the characteristics of patients who frequently visit the emergency department (ED),[4, 5, 6, 7, 8, 9, 10, 11, 12] it is not clear to what extent these characteristics also apply to patients with frequent hospital admissions. Frequent ED visitors have been found to be largely insured (85%) although with over‐representation of public insurance, and to be heavy users of the healthcare system overall.[6] A high disease burden associated with multiple chronic conditions has been found to predict frequent ED use.[4, 9, 11, 12] Some characteristics may vary by location; for example, alcohol abuse and psychiatric morbidity have been found to be associated with frequent ED use in New York and San Francisco, but it is not clear to what extent they are a factor in less urban areas.[4, 6, 12]

Several previous studies have investigated the characteristics of frequently admitted patients at single sites.[13, 14, 15, 16] Nguyen et al. (2013) studied patients with the highest costs and the most admissions at a large academic medical center in San Francisco.[13] High admit patients were defined as those responsible for the top decile of admissions, and were grouped into equal‐sized high‐ and low‐cost cohorts. The high‐admission/high‐cost group represented 5% of all patients, 25% of all costs, and 16% of all admissions. These patients were hospitalized primarily for medical conditions (78%) and had a high 30‐day readmission rate (47%). The high‐admission/low‐cost group accounted for 5% of all patients, 12% of all admissions, and 7% of all costs. These patients were also predominantly admitted for medical conditions (87%), with the most common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions.[13]

Hwa (2012) conducted an analysis of 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco.[14] These patients represented just 1% of all patients, but 13% of readmissions. Fifty‐five percent of these patients had a psychiatric diagnosis, and 52% had chronic pain. Ninety percent had a primary care physician in the hospital system, 100% were insured either privately or publicly, and 93% had housing, although for 17% housing was described as marginal.[14]

In a third study, Boonyasai et al. (2012) identified 76 patients with 82 readmissions at a Baltimore, Maryland, hospital and classified them as isolated (1 readmission per 6‐month period) or serial (more than 1 readmission per 6‐month period) readmissions.[15] Patients with serial readmissions accounted for 70% of the total. Isolated readmissions were most likely to be related to suboptimal quality of care and care coordination, whereas serial readmissions were more likely to result from disease progression, psychiatric illness, and substance abuse.[15]

All of these studies were conducted at single‐site academic medical centers serving inner city populations. We undertook this study to identify patient and hospital‐level characteristics of frequently admitted patients in a broad sample of 101 US academic medical centers to determine whether previously reported findings are generalizable, and to identify characteristics of frequently admitted patients that can inform interventions designed to meet the needs of this relatively small but resource‐intensive group of patients.

METHODS

All data were obtained from the University HealthSystem Consortium (UHC) (Chicago, IL) Clinical Data Base/Resource Manager (CDB), a large administrative database to which UHC principal members submit comprehensive administrative data files. UHC's principal members include approximately 120 US academic medical centers delivering tertiary and quaternary care, with an average of 647 acute care beds. The CDB includes primary and secondary diagnoses using International Classification of Diseases, Ninth Revision (ICD‐9)[17] codes.

The data of 101 academic medical centers with complete datasets for the study period (October 1, 2011, to September 30, 2012) were included in this analysis. Frequently admitted patients were defined as patients admitted 5 or more times to the same facility in a 12‐month period; all admissions were included, even those more than 30 days apart. This definition was established based on a naturally occurring break in the frequency distribution (Figure 1) and our intention to focus on the unique characteristics of patients at the far right of the distribution. We excluded obstetric (MDC 14, ICD‐9)[17] admissions and pediatric (<18 years of age at index admission) patients, as well as admissions with principal diagnoses for chemotherapy (ICD‐9 diagnosis codes v5811v5812), dialysis (ICD‐9 diagnosis codes v560v568), and rehabilitation (ICD‐9 diagnosis codes v570v579), which are typically planned. The Agency for Healthcare Research and Quality (AHRQ) comorbidity software was used to identify comorbid conditions,[18, 19] and a score based on the Elixhauser comorbidity measures was calculated using a modified acuity point system.[20] For comparisons based on safety net status, we used a definition of payer mix being 25% Medicaid or uninsured.

Figure 1

Our analyses included patient demographics, admission source and discharge status, clinical diagnoses, procedures, and comorbidities, cost, and length of stay. Patients defined as frequently admitted were compared in aggregate to all other hospitalized patients (all other admissions).

To evaluate associations, we used [2] tests for categorical variables and t tests for continuous variables. When comparing the non‐normally distributed comorbidities of the control group to the normally distributed comorbidities of the frequently admitted patients, we performed a Kruskal‐Wallis test on the medians.

RESULTS

During a 1‐year period (October 1, 2011, to September 30, 2012), 1,758,027 patients were admitted 2,388,124 times at 101 academic medical centers. Of these, 28,291 patients had 5 or more admissions during this period, resulting in 180,185 admissions. These frequently admitted patients represented 1.6% of all patients (Figure 1) and 7.6% of all inpatient admissions. By comparison, nonfrequently admitted patients were admitted once (79%), twice (14%), 3 times (4%), or 4 times (2%).

Among hospitals, the volume and impact of frequently admitted patients varied widely. The frequently admitted patient population ranged from 64 patients (0.7% of all patients) to 785 patients (3.5%), with an average of 280 patients (1.6%). To look for differences that might explain this range, we compared hospitals in the top and bottom deciles with respect to geographic region and to safety net status, but found no significant or meaningful differences. The average number of admissions per patient was 6.4, with a range of 5 to 76. Days per patient ranged from 5 to 434 days, with an average of 42. The average patient‐day percentage (frequently admitted patient days/total patient days) was 8.4%, and ranged from 3.2% to 15.4%.

Frequently admitted patients were more likely to be younger than all other patients (71.9% under the age of 65 years, as compared with 65.3% of all other patients (P<0.001)). They were also more likely to have either Medicaid or no healthcare insurance (27.6% compared with 21.6%, P<0.001), although nearly three‐quarters had either private insurance or Medicare coverage.

Eighty‐four percent of frequently admitted patient admissions were to medical services (vs 58% of all other patients (P<0.001)). The admission status for these patients was much less likely to be elective (9.1% of frequently admitted patient admissions vs 26.6% of all other patients' admissions [P<0.001]). Frequently admitted patients were more likely to be discharged to a skilled nursing facility (9.3% vs 8.4%, [P<0.001]) or with home health services (19.7% vs 13.4% [P<0.001]).

The 10 most common primary diagnoses for patient admissions are shown in Table 1. No single primary diagnosis accounted for a large share of the admissions of these patients; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4% of admissions. The 10 most common diagnoses accounted for <20% of all admissions. The remainder of the diagnoses was spread over more than 3000 diagnosis codes; only about 300 codes had more than 100 admissions each.

Secondary diagnoses were mainly chronic conditions, including hypertension, hyperlipidemia, esophageal reflux, and diabetes mellitus type 2 (Table 1.) Combined, congestive heart failure and diabetes mellitus accounted for 43.3% of the secondary diagnoses of admissions of frequently admitted patients, but for only 28.6% of other patients. Acute kidney failure was more common in frequently admitted patients (13.8% vs 9.4% [P<0.001]). Psychiatric disorders accounted for <1% of primary diagnoses for both frequently admitted patients and all other patients. As a secondary diagnosis, depressive disorder appeared in the top 10 for both groups, although more commonly for frequently admitted patients (15.4% vs 10.3% [P<0.001]).

The most commonly performed principal procedures are also shown in Table 1. These include hemodialysis (6.32%) and packed cell transfusion (4.49%), nonoperating room procedures associated with chronic medical conditions.

Comorbidities were compared using the AHRQ comorbidity software.[18, 19] Comorbid conditions were counted once per patient, regardless of the number of admissions in which the condition was coded. Frequently admitted patients have a significantly higher mean number of comorbidities: 7.1 compared to 2.5 for all other patients (P<0.001; Figure 2). In an additional analysis using the Elixhauser comorbidity measures to determine acuity scores, the mean scores were 13.1 for frequently admitted patients and 3.17 for all others (P<0.001). The most common comorbidities were hypertension (74%), fluid and electrolyte disorders (73%), and deficiency anemias (66%). The only behavioral health comorbidity that affected more than a quarter of frequently admitted patients was depression (40% as compared to 13% for all others).

Figure 2

Additionally, frequently admitted patients were significantly more likely to have comorbidities of psychosis (18% vs 5% [P<0.001]), alcohol abuse (16% vs 7% [P<0.001]), and drug abuse (20% vs 7% [P<0.001]). Among hospitals, these comorbidities ranged widely: psychosis (3% 48%); alcohol abuse (3%46%); and drug abuse (3%58%). Hospitals with the highest rates (top decile) of frequently admitted patients with alcohol and drug abuse comorbidities were more likely to be safety net hospitals than those in the lowest decile (P<0.05 for each independently), but no such difference was found regarding rates of patients with psychosis.

Although the frequently admitted patient population accounted for only 1.6% of patients, they accounted for an average of 8.4% of all bed days and 7.1% of direct cost. The average cost per day was $1746, compared to $2144 for all other patients (Table 2).

DISCUSSION

An extensive analysis of the characteristics of frequently admitted patients at 101 US academic medical centers, from October 1, 2011 to September 30, 2012, revealed that these patients comprised 1.6% of all patients, but accounted for 8% of all admissions and 7% of direct costs. Relative to all other hospitalized patients, frequently admitted patients were likely to be younger, of lower socioeconomic status, in poorer health, and more often affected by mental health or substance abuse conditions that may mediate their health behaviors. However, the prevalence of patients with psychiatric or behavior conditions varied widely among hospitals, and hospitals with the highest rates of patients with substance abuse comorbidities were more likely to be safety net hospitals. Frequently admitted patients' diagnoses and procedures suggest that their admissions were related to complex chronic diseases; more than three‐quarters were admitted to medicine services, and their average length of stay was nearly 7 days. No single primary diagnosis accounted for a predominant share of their admissions; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4%. The cost of their care was lower than that of other patients, reflecting the preponderance of their admissions to medicine service lines.

In many ways, frequently admitted patients seem similar to frequent ED visitors. Their visits were driven by a high disease burden associated with multiple chronic conditions, and they were heavy users of the healthcare system overall.[4, 6] The majority of both groups were insured, although there was over‐representation of public insurance.[6] As with frequent ED users, some frequently admitted patients are affected by psychiatric morbidity and substance abuse.[4, 12]

Our results in some ways confirmed, and in some ways differed from, findings of prior studies of patients with frequent hospital admissions. Although each study performed to date has defined the population differently, comparison of findings is useful. Our population was similar to the high‐admission groups identified by Nguyen et al. (patients responsible for the top decile of admissions).[13] These patients were also predominantly admitted for medical conditions, with common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions. However, the median length of stay (3 days for the high‐admission/low‐cost group and 5 days for the high‐admission/high‐cost group) was lower than that of our population (5.5 days).

Hwa, who studied 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco,[14] found that 55% of frequently admitted patients had a psychiatric diagnosis, higher than our patient population. Our findings are similar to those of Boonyasai et al.[15] whose serial readmitters had admissions resulting from disease progression, psychiatric illness, and substance abuse.

Our more nationally representative analysis documented a wide range of patient volumes and clinical characteristics, including psychiatric and substance abuse comorbidities, across study hospitals. It demonstrates that different approachesand resourcesare needed to meet the needs of these varied groups of patients. Each hospital must identify, evaluate, and understand its own population of frequently admitted patients to create well‐informed solutions to prevent repeat hospitalization for these patients.

Our ability to create a distinctive picture of the population of frequently admitted patients in US academic medical centers is based on access to an expansive dataset that captures complete diagnostic and demographic information on the universe of patients admitted to our member hospitals. The availability of clinical and administrative data for the entire population of patients permits both an accurate description of patient characteristics and a standardized comparison of groups. All data conform to accepted formats and definitions; their validity is universally recognized by contributing database participants.

CONCLUSION

The valuable information reported from analysis of nearly 2 million patients in the UHC Clinical Data Base/Resource Manager can be used to better understand the characteristics of frequently admitted patients. This important cohort of individuals has complex care needs that often result in hospitalization, but may be amenable to solutions that allow patients to remain in their communities. By understanding the demographic, social, and medical characteristics of these patients, hospitals can develop and implement solutions that address the needs of this small group of patients who consume a highly disproportionate share of healthcare resources.

The national healthcare improvement paradigm is shifting toward a more comprehensive, value‐focused, and patient‐centered approach. Reducing hospital readmissions has become a focal point as a policy strategy to improve care quality while reducing cost. Section 3025 of the Affordable Care Act mandated the Centers for Medicare and Medicaid Services to make progressive reductions in Medicare payments to hospitals that have higher than expected readmission rates for 3 conditions (heart failure, acute myocardial infarction, and pneumonia), and expanding to include chronic obstructive pulmonary disease and total hip and knee arthroplasty in 2015.[1] In response, hospitals and systems are developing and implementing programs that coordinate care beyond hospital walls to reduce readmissions and healthcare costs.[2, 3] However, patients are readmitted for a variety of reasons, and programs that address the needs of some may not address the distinct needs of others. Understanding the characteristics of patients with frequent readmissions will permit the well‐informed creation of solutions specific to this population to reduce cost, free resources, and provide better care.

Although a solid body of literature already exists that describes the characteristics of patients who frequently visit the emergency department (ED),[4, 5, 6, 7, 8, 9, 10, 11, 12] it is not clear to what extent these characteristics also apply to patients with frequent hospital admissions. Frequent ED visitors have been found to be largely insured (85%) although with over‐representation of public insurance, and to be heavy users of the healthcare system overall.[6] A high disease burden associated with multiple chronic conditions has been found to predict frequent ED use.[4, 9, 11, 12] Some characteristics may vary by location; for example, alcohol abuse and psychiatric morbidity have been found to be associated with frequent ED use in New York and San Francisco, but it is not clear to what extent they are a factor in less urban areas.[4, 6, 12]

Several previous studies have investigated the characteristics of frequently admitted patients at single sites.[13, 14, 15, 16] Nguyen et al. (2013) studied patients with the highest costs and the most admissions at a large academic medical center in San Francisco.[13] High admit patients were defined as those responsible for the top decile of admissions, and were grouped into equal‐sized high‐ and low‐cost cohorts. The high‐admission/high‐cost group represented 5% of all patients, 25% of all costs, and 16% of all admissions. These patients were hospitalized primarily for medical conditions (78%) and had a high 30‐day readmission rate (47%). The high‐admission/low‐cost group accounted for 5% of all patients, 12% of all admissions, and 7% of all costs. These patients were also predominantly admitted for medical conditions (87%), with the most common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions.[13]

Hwa (2012) conducted an analysis of 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco.[14] These patients represented just 1% of all patients, but 13% of readmissions. Fifty‐five percent of these patients had a psychiatric diagnosis, and 52% had chronic pain. Ninety percent had a primary care physician in the hospital system, 100% were insured either privately or publicly, and 93% had housing, although for 17% housing was described as marginal.[14]

In a third study, Boonyasai et al. (2012) identified 76 patients with 82 readmissions at a Baltimore, Maryland, hospital and classified them as isolated (1 readmission per 6‐month period) or serial (more than 1 readmission per 6‐month period) readmissions.[15] Patients with serial readmissions accounted for 70% of the total. Isolated readmissions were most likely to be related to suboptimal quality of care and care coordination, whereas serial readmissions were more likely to result from disease progression, psychiatric illness, and substance abuse.[15]

All of these studies were conducted at single‐site academic medical centers serving inner city populations. We undertook this study to identify patient and hospital‐level characteristics of frequently admitted patients in a broad sample of 101 US academic medical centers to determine whether previously reported findings are generalizable, and to identify characteristics of frequently admitted patients that can inform interventions designed to meet the needs of this relatively small but resource‐intensive group of patients.

METHODS

All data were obtained from the University HealthSystem Consortium (UHC) (Chicago, IL) Clinical Data Base/Resource Manager (CDB), a large administrative database to which UHC principal members submit comprehensive administrative data files. UHC's principal members include approximately 120 US academic medical centers delivering tertiary and quaternary care, with an average of 647 acute care beds. The CDB includes primary and secondary diagnoses using International Classification of Diseases, Ninth Revision (ICD‐9)[17] codes.

The data of 101 academic medical centers with complete datasets for the study period (October 1, 2011, to September 30, 2012) were included in this analysis. Frequently admitted patients were defined as patients admitted 5 or more times to the same facility in a 12‐month period; all admissions were included, even those more than 30 days apart. This definition was established based on a naturally occurring break in the frequency distribution (Figure 1) and our intention to focus on the unique characteristics of patients at the far right of the distribution. We excluded obstetric (MDC 14, ICD‐9)[17] admissions and pediatric (<18 years of age at index admission) patients, as well as admissions with principal diagnoses for chemotherapy (ICD‐9 diagnosis codes v5811v5812), dialysis (ICD‐9 diagnosis codes v560v568), and rehabilitation (ICD‐9 diagnosis codes v570v579), which are typically planned. The Agency for Healthcare Research and Quality (AHRQ) comorbidity software was used to identify comorbid conditions,[18, 19] and a score based on the Elixhauser comorbidity measures was calculated using a modified acuity point system.[20] For comparisons based on safety net status, we used a definition of payer mix being 25% Medicaid or uninsured.

Figure 1

Our analyses included patient demographics, admission source and discharge status, clinical diagnoses, procedures, and comorbidities, cost, and length of stay. Patients defined as frequently admitted were compared in aggregate to all other hospitalized patients (all other admissions).

To evaluate associations, we used [2] tests for categorical variables and t tests for continuous variables. When comparing the non‐normally distributed comorbidities of the control group to the normally distributed comorbidities of the frequently admitted patients, we performed a Kruskal‐Wallis test on the medians.

RESULTS

During a 1‐year period (October 1, 2011, to September 30, 2012), 1,758,027 patients were admitted 2,388,124 times at 101 academic medical centers. Of these, 28,291 patients had 5 or more admissions during this period, resulting in 180,185 admissions. These frequently admitted patients represented 1.6% of all patients (Figure 1) and 7.6% of all inpatient admissions. By comparison, nonfrequently admitted patients were admitted once (79%), twice (14%), 3 times (4%), or 4 times (2%).

Among hospitals, the volume and impact of frequently admitted patients varied widely. The frequently admitted patient population ranged from 64 patients (0.7% of all patients) to 785 patients (3.5%), with an average of 280 patients (1.6%). To look for differences that might explain this range, we compared hospitals in the top and bottom deciles with respect to geographic region and to safety net status, but found no significant or meaningful differences. The average number of admissions per patient was 6.4, with a range of 5 to 76. Days per patient ranged from 5 to 434 days, with an average of 42. The average patient‐day percentage (frequently admitted patient days/total patient days) was 8.4%, and ranged from 3.2% to 15.4%.

Frequently admitted patients were more likely to be younger than all other patients (71.9% under the age of 65 years, as compared with 65.3% of all other patients (P<0.001)). They were also more likely to have either Medicaid or no healthcare insurance (27.6% compared with 21.6%, P<0.001), although nearly three‐quarters had either private insurance or Medicare coverage.

Eighty‐four percent of frequently admitted patient admissions were to medical services (vs 58% of all other patients (P<0.001)). The admission status for these patients was much less likely to be elective (9.1% of frequently admitted patient admissions vs 26.6% of all other patients' admissions [P<0.001]). Frequently admitted patients were more likely to be discharged to a skilled nursing facility (9.3% vs 8.4%, [P<0.001]) or with home health services (19.7% vs 13.4% [P<0.001]).

The 10 most common primary diagnoses for patient admissions are shown in Table 1. No single primary diagnosis accounted for a large share of the admissions of these patients; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4% of admissions. The 10 most common diagnoses accounted for <20% of all admissions. The remainder of the diagnoses was spread over more than 3000 diagnosis codes; only about 300 codes had more than 100 admissions each.

Secondary diagnoses were mainly chronic conditions, including hypertension, hyperlipidemia, esophageal reflux, and diabetes mellitus type 2 (Table 1.) Combined, congestive heart failure and diabetes mellitus accounted for 43.3% of the secondary diagnoses of admissions of frequently admitted patients, but for only 28.6% of other patients. Acute kidney failure was more common in frequently admitted patients (13.8% vs 9.4% [P<0.001]). Psychiatric disorders accounted for <1% of primary diagnoses for both frequently admitted patients and all other patients. As a secondary diagnosis, depressive disorder appeared in the top 10 for both groups, although more commonly for frequently admitted patients (15.4% vs 10.3% [P<0.001]).

The most commonly performed principal procedures are also shown in Table 1. These include hemodialysis (6.32%) and packed cell transfusion (4.49%), nonoperating room procedures associated with chronic medical conditions.

Comorbidities were compared using the AHRQ comorbidity software.[18, 19] Comorbid conditions were counted once per patient, regardless of the number of admissions in which the condition was coded. Frequently admitted patients have a significantly higher mean number of comorbidities: 7.1 compared to 2.5 for all other patients (P<0.001; Figure 2). In an additional analysis using the Elixhauser comorbidity measures to determine acuity scores, the mean scores were 13.1 for frequently admitted patients and 3.17 for all others (P<0.001). The most common comorbidities were hypertension (74%), fluid and electrolyte disorders (73%), and deficiency anemias (66%). The only behavioral health comorbidity that affected more than a quarter of frequently admitted patients was depression (40% as compared to 13% for all others).

Figure 2

Additionally, frequently admitted patients were significantly more likely to have comorbidities of psychosis (18% vs 5% [P<0.001]), alcohol abuse (16% vs 7% [P<0.001]), and drug abuse (20% vs 7% [P<0.001]). Among hospitals, these comorbidities ranged widely: psychosis (3% 48%); alcohol abuse (3%46%); and drug abuse (3%58%). Hospitals with the highest rates (top decile) of frequently admitted patients with alcohol and drug abuse comorbidities were more likely to be safety net hospitals than those in the lowest decile (P<0.05 for each independently), but no such difference was found regarding rates of patients with psychosis.

Although the frequently admitted patient population accounted for only 1.6% of patients, they accounted for an average of 8.4% of all bed days and 7.1% of direct cost. The average cost per day was $1746, compared to $2144 for all other patients (Table 2).

DISCUSSION

An extensive analysis of the characteristics of frequently admitted patients at 101 US academic medical centers, from October 1, 2011 to September 30, 2012, revealed that these patients comprised 1.6% of all patients, but accounted for 8% of all admissions and 7% of direct costs. Relative to all other hospitalized patients, frequently admitted patients were likely to be younger, of lower socioeconomic status, in poorer health, and more often affected by mental health or substance abuse conditions that may mediate their health behaviors. However, the prevalence of patients with psychiatric or behavior conditions varied widely among hospitals, and hospitals with the highest rates of patients with substance abuse comorbidities were more likely to be safety net hospitals. Frequently admitted patients' diagnoses and procedures suggest that their admissions were related to complex chronic diseases; more than three‐quarters were admitted to medicine services, and their average length of stay was nearly 7 days. No single primary diagnosis accounted for a predominant share of their admissions; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4%. The cost of their care was lower than that of other patients, reflecting the preponderance of their admissions to medicine service lines.

In many ways, frequently admitted patients seem similar to frequent ED visitors. Their visits were driven by a high disease burden associated with multiple chronic conditions, and they were heavy users of the healthcare system overall.[4, 6] The majority of both groups were insured, although there was over‐representation of public insurance.[6] As with frequent ED users, some frequently admitted patients are affected by psychiatric morbidity and substance abuse.[4, 12]

Our results in some ways confirmed, and in some ways differed from, findings of prior studies of patients with frequent hospital admissions. Although each study performed to date has defined the population differently, comparison of findings is useful. Our population was similar to the high‐admission groups identified by Nguyen et al. (patients responsible for the top decile of admissions).[13] These patients were also predominantly admitted for medical conditions, with common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions. However, the median length of stay (3 days for the high‐admission/low‐cost group and 5 days for the high‐admission/high‐cost group) was lower than that of our population (5.5 days).

Hwa, who studied 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco,[14] found that 55% of frequently admitted patients had a psychiatric diagnosis, higher than our patient population. Our findings are similar to those of Boonyasai et al.[15] whose serial readmitters had admissions resulting from disease progression, psychiatric illness, and substance abuse.

Our more nationally representative analysis documented a wide range of patient volumes and clinical characteristics, including psychiatric and substance abuse comorbidities, across study hospitals. It demonstrates that different approachesand resourcesare needed to meet the needs of these varied groups of patients. Each hospital must identify, evaluate, and understand its own population of frequently admitted patients to create well‐informed solutions to prevent repeat hospitalization for these patients.

Our ability to create a distinctive picture of the population of frequently admitted patients in US academic medical centers is based on access to an expansive dataset that captures complete diagnostic and demographic information on the universe of patients admitted to our member hospitals. The availability of clinical and administrative data for the entire population of patients permits both an accurate description of patient characteristics and a standardized comparison of groups. All data conform to accepted formats and definitions; their validity is universally recognized by contributing database participants.

CONCLUSION

The valuable information reported from analysis of nearly 2 million patients in the UHC Clinical Data Base/Resource Manager can be used to better understand the characteristics of frequently admitted patients. This important cohort of individuals has complex care needs that often result in hospitalization, but may be amenable to solutions that allow patients to remain in their communities. By understanding the demographic, social, and medical characteristics of these patients, hospitals can develop and implement solutions that address the needs of this small group of patients who consume a highly disproportionate share of healthcare resources.

The national healthcare improvement paradigm is shifting toward a more comprehensive, value‐focused, and patient‐centered approach. Reducing hospital readmissions has become a focal point as a policy strategy to improve care quality while reducing cost. Section 3025 of the Affordable Care Act mandated the Centers for Medicare and Medicaid Services to make progressive reductions in Medicare payments to hospitals that have higher than expected readmission rates for 3 conditions (heart failure, acute myocardial infarction, and pneumonia), and expanding to include chronic obstructive pulmonary disease and total hip and knee arthroplasty in 2015.[1] In response, hospitals and systems are developing and implementing programs that coordinate care beyond hospital walls to reduce readmissions and healthcare costs.[2, 3] However, patients are readmitted for a variety of reasons, and programs that address the needs of some may not address the distinct needs of others. Understanding the characteristics of patients with frequent readmissions will permit the well‐informed creation of solutions specific to this population to reduce cost, free resources, and provide better care.

Although a solid body of literature already exists that describes the characteristics of patients who frequently visit the emergency department (ED),[4, 5, 6, 7, 8, 9, 10, 11, 12] it is not clear to what extent these characteristics also apply to patients with frequent hospital admissions. Frequent ED visitors have been found to be largely insured (85%) although with over‐representation of public insurance, and to be heavy users of the healthcare system overall.[6] A high disease burden associated with multiple chronic conditions has been found to predict frequent ED use.[4, 9, 11, 12] Some characteristics may vary by location; for example, alcohol abuse and psychiatric morbidity have been found to be associated with frequent ED use in New York and San Francisco, but it is not clear to what extent they are a factor in less urban areas.[4, 6, 12]

Several previous studies have investigated the characteristics of frequently admitted patients at single sites.[13, 14, 15, 16] Nguyen et al. (2013) studied patients with the highest costs and the most admissions at a large academic medical center in San Francisco.[13] High admit patients were defined as those responsible for the top decile of admissions, and were grouped into equal‐sized high‐ and low‐cost cohorts. The high‐admission/high‐cost group represented 5% of all patients, 25% of all costs, and 16% of all admissions. These patients were hospitalized primarily for medical conditions (78%) and had a high 30‐day readmission rate (47%). The high‐admission/low‐cost group accounted for 5% of all patients, 12% of all admissions, and 7% of all costs. These patients were also predominantly admitted for medical conditions (87%), with the most common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions.[13]

Hwa (2012) conducted an analysis of 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco.[14] These patients represented just 1% of all patients, but 13% of readmissions. Fifty‐five percent of these patients had a psychiatric diagnosis, and 52% had chronic pain. Ninety percent had a primary care physician in the hospital system, 100% were insured either privately or publicly, and 93% had housing, although for 17% housing was described as marginal.[14]

In a third study, Boonyasai et al. (2012) identified 76 patients with 82 readmissions at a Baltimore, Maryland, hospital and classified them as isolated (1 readmission per 6‐month period) or serial (more than 1 readmission per 6‐month period) readmissions.[15] Patients with serial readmissions accounted for 70% of the total. Isolated readmissions were most likely to be related to suboptimal quality of care and care coordination, whereas serial readmissions were more likely to result from disease progression, psychiatric illness, and substance abuse.[15]

All of these studies were conducted at single‐site academic medical centers serving inner city populations. We undertook this study to identify patient and hospital‐level characteristics of frequently admitted patients in a broad sample of 101 US academic medical centers to determine whether previously reported findings are generalizable, and to identify characteristics of frequently admitted patients that can inform interventions designed to meet the needs of this relatively small but resource‐intensive group of patients.

METHODS

All data were obtained from the University HealthSystem Consortium (UHC) (Chicago, IL) Clinical Data Base/Resource Manager (CDB), a large administrative database to which UHC principal members submit comprehensive administrative data files. UHC's principal members include approximately 120 US academic medical centers delivering tertiary and quaternary care, with an average of 647 acute care beds. The CDB includes primary and secondary diagnoses using International Classification of Diseases, Ninth Revision (ICD‐9)[17] codes.

The data of 101 academic medical centers with complete datasets for the study period (October 1, 2011, to September 30, 2012) were included in this analysis. Frequently admitted patients were defined as patients admitted 5 or more times to the same facility in a 12‐month period; all admissions were included, even those more than 30 days apart. This definition was established based on a naturally occurring break in the frequency distribution (Figure 1) and our intention to focus on the unique characteristics of patients at the far right of the distribution. We excluded obstetric (MDC 14, ICD‐9)[17] admissions and pediatric (<18 years of age at index admission) patients, as well as admissions with principal diagnoses for chemotherapy (ICD‐9 diagnosis codes v5811v5812), dialysis (ICD‐9 diagnosis codes v560v568), and rehabilitation (ICD‐9 diagnosis codes v570v579), which are typically planned. The Agency for Healthcare Research and Quality (AHRQ) comorbidity software was used to identify comorbid conditions,[18, 19] and a score based on the Elixhauser comorbidity measures was calculated using a modified acuity point system.[20] For comparisons based on safety net status, we used a definition of payer mix being 25% Medicaid or uninsured.

Figure 1

Our analyses included patient demographics, admission source and discharge status, clinical diagnoses, procedures, and comorbidities, cost, and length of stay. Patients defined as frequently admitted were compared in aggregate to all other hospitalized patients (all other admissions).

To evaluate associations, we used [2] tests for categorical variables and t tests for continuous variables. When comparing the non‐normally distributed comorbidities of the control group to the normally distributed comorbidities of the frequently admitted patients, we performed a Kruskal‐Wallis test on the medians.

RESULTS

During a 1‐year period (October 1, 2011, to September 30, 2012), 1,758,027 patients were admitted 2,388,124 times at 101 academic medical centers. Of these, 28,291 patients had 5 or more admissions during this period, resulting in 180,185 admissions. These frequently admitted patients represented 1.6% of all patients (Figure 1) and 7.6% of all inpatient admissions. By comparison, nonfrequently admitted patients were admitted once (79%), twice (14%), 3 times (4%), or 4 times (2%).

Among hospitals, the volume and impact of frequently admitted patients varied widely. The frequently admitted patient population ranged from 64 patients (0.7% of all patients) to 785 patients (3.5%), with an average of 280 patients (1.6%). To look for differences that might explain this range, we compared hospitals in the top and bottom deciles with respect to geographic region and to safety net status, but found no significant or meaningful differences. The average number of admissions per patient was 6.4, with a range of 5 to 76. Days per patient ranged from 5 to 434 days, with an average of 42. The average patient‐day percentage (frequently admitted patient days/total patient days) was 8.4%, and ranged from 3.2% to 15.4%.

Frequently admitted patients were more likely to be younger than all other patients (71.9% under the age of 65 years, as compared with 65.3% of all other patients (P<0.001)). They were also more likely to have either Medicaid or no healthcare insurance (27.6% compared with 21.6%, P<0.001), although nearly three‐quarters had either private insurance or Medicare coverage.

Eighty‐four percent of frequently admitted patient admissions were to medical services (vs 58% of all other patients (P<0.001)). The admission status for these patients was much less likely to be elective (9.1% of frequently admitted patient admissions vs 26.6% of all other patients' admissions [P<0.001]). Frequently admitted patients were more likely to be discharged to a skilled nursing facility (9.3% vs 8.4%, [P<0.001]) or with home health services (19.7% vs 13.4% [P<0.001]).

The 10 most common primary diagnoses for patient admissions are shown in Table 1. No single primary diagnosis accounted for a large share of the admissions of these patients; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4% of admissions. The 10 most common diagnoses accounted for <20% of all admissions. The remainder of the diagnoses was spread over more than 3000 diagnosis codes; only about 300 codes had more than 100 admissions each.

Secondary diagnoses were mainly chronic conditions, including hypertension, hyperlipidemia, esophageal reflux, and diabetes mellitus type 2 (Table 1.) Combined, congestive heart failure and diabetes mellitus accounted for 43.3% of the secondary diagnoses of admissions of frequently admitted patients, but for only 28.6% of other patients. Acute kidney failure was more common in frequently admitted patients (13.8% vs 9.4% [P<0.001]). Psychiatric disorders accounted for <1% of primary diagnoses for both frequently admitted patients and all other patients. As a secondary diagnosis, depressive disorder appeared in the top 10 for both groups, although more commonly for frequently admitted patients (15.4% vs 10.3% [P<0.001]).

The most commonly performed principal procedures are also shown in Table 1. These include hemodialysis (6.32%) and packed cell transfusion (4.49%), nonoperating room procedures associated with chronic medical conditions.

Comorbidities were compared using the AHRQ comorbidity software.[18, 19] Comorbid conditions were counted once per patient, regardless of the number of admissions in which the condition was coded. Frequently admitted patients have a significantly higher mean number of comorbidities: 7.1 compared to 2.5 for all other patients (P<0.001; Figure 2). In an additional analysis using the Elixhauser comorbidity measures to determine acuity scores, the mean scores were 13.1 for frequently admitted patients and 3.17 for all others (P<0.001). The most common comorbidities were hypertension (74%), fluid and electrolyte disorders (73%), and deficiency anemias (66%). The only behavioral health comorbidity that affected more than a quarter of frequently admitted patients was depression (40% as compared to 13% for all others).

Figure 2

Additionally, frequently admitted patients were significantly more likely to have comorbidities of psychosis (18% vs 5% [P<0.001]), alcohol abuse (16% vs 7% [P<0.001]), and drug abuse (20% vs 7% [P<0.001]). Among hospitals, these comorbidities ranged widely: psychosis (3% 48%); alcohol abuse (3%46%); and drug abuse (3%58%). Hospitals with the highest rates (top decile) of frequently admitted patients with alcohol and drug abuse comorbidities were more likely to be safety net hospitals than those in the lowest decile (P<0.05 for each independently), but no such difference was found regarding rates of patients with psychosis.

Although the frequently admitted patient population accounted for only 1.6% of patients, they accounted for an average of 8.4% of all bed days and 7.1% of direct cost. The average cost per day was $1746, compared to $2144 for all other patients (Table 2).

DISCUSSION

An extensive analysis of the characteristics of frequently admitted patients at 101 US academic medical centers, from October 1, 2011 to September 30, 2012, revealed that these patients comprised 1.6% of all patients, but accounted for 8% of all admissions and 7% of direct costs. Relative to all other hospitalized patients, frequently admitted patients were likely to be younger, of lower socioeconomic status, in poorer health, and more often affected by mental health or substance abuse conditions that may mediate their health behaviors. However, the prevalence of patients with psychiatric or behavior conditions varied widely among hospitals, and hospitals with the highest rates of patients with substance abuse comorbidities were more likely to be safety net hospitals. Frequently admitted patients' diagnoses and procedures suggest that their admissions were related to complex chronic diseases; more than three‐quarters were admitted to medicine services, and their average length of stay was nearly 7 days. No single primary diagnosis accounted for a predominant share of their admissions; the most common diagnosis, sickle cell disease with crisis, accounted for only about 4%. The cost of their care was lower than that of other patients, reflecting the preponderance of their admissions to medicine service lines.

In many ways, frequently admitted patients seem similar to frequent ED visitors. Their visits were driven by a high disease burden associated with multiple chronic conditions, and they were heavy users of the healthcare system overall.[4, 6] The majority of both groups were insured, although there was over‐representation of public insurance.[6] As with frequent ED users, some frequently admitted patients are affected by psychiatric morbidity and substance abuse.[4, 12]

Our results in some ways confirmed, and in some ways differed from, findings of prior studies of patients with frequent hospital admissions. Although each study performed to date has defined the population differently, comparison of findings is useful. Our population was similar to the high‐admission groups identified by Nguyen et al. (patients responsible for the top decile of admissions).[13] These patients were also predominantly admitted for medical conditions, with common admitting diagnoses representing respiratory, gastrointestinal, and cardiovascular conditions. However, the median length of stay (3 days for the high‐admission/low‐cost group and 5 days for the high‐admission/high‐cost group) was lower than that of our population (5.5 days).

Hwa, who studied 29 patients admitted 6 or more times in 1 year to an inpatient medical service in San Francisco,[14] found that 55% of frequently admitted patients had a psychiatric diagnosis, higher than our patient population. Our findings are similar to those of Boonyasai et al.[15] whose serial readmitters had admissions resulting from disease progression, psychiatric illness, and substance abuse.

Our more nationally representative analysis documented a wide range of patient volumes and clinical characteristics, including psychiatric and substance abuse comorbidities, across study hospitals. It demonstrates that different approachesand resourcesare needed to meet the needs of these varied groups of patients. Each hospital must identify, evaluate, and understand its own population of frequently admitted patients to create well‐informed solutions to prevent repeat hospitalization for these patients.

Our ability to create a distinctive picture of the population of frequently admitted patients in US academic medical centers is based on access to an expansive dataset that captures complete diagnostic and demographic information on the universe of patients admitted to our member hospitals. The availability of clinical and administrative data for the entire population of patients permits both an accurate description of patient characteristics and a standardized comparison of groups. All data conform to accepted formats and definitions; their validity is universally recognized by contributing database participants.

CONCLUSION

The valuable information reported from analysis of nearly 2 million patients in the UHC Clinical Data Base/Resource Manager can be used to better understand the characteristics of frequently admitted patients. This important cohort of individuals has complex care needs that often result in hospitalization, but may be amenable to solutions that allow patients to remain in their communities. By understanding the demographic, social, and medical characteristics of these patients, hospitals can develop and implement solutions that address the needs of this small group of patients who consume a highly disproportionate share of healthcare resources.

We have known for years that the distribution of healthcare expenditures in the United States is skewed, with a small portion of the population consuming a disproportionately high share of resources. In 2010, 1% of the population accounted for 21.4% of the $1.3 trillion spent on healthcare.[1] Growing evidence documents that most of these high‐cost patients are not receiving coordinated care, preventive care, or care in the most appropriate settings.[2] The term superutilizer describes individuals with complex physical, behavioral, and social needs who have frequent emergency department (ED) visits and multiple costly hospital admissions.[3] Not surprisingly, multiple superutilizer programs and new funding opportunities target this population attempting to reduce their healthcare costs while improving their care, as public and private insurers shift to value‐based care.[4]

Beginning in 2006, the Robert Wood Johnson Foundation supported the Camden Coalition[5] with 3 grants to develop a community‐based approach to identify high‐utilizer patients and provide them with coordinated medical and social services.[6] These programs include community‐based teams that focus on the highest utilizers in a specific geographic area and provide intensive outpatient case management. Building on these efforts, the Center for Medicare and Medicaid Innovation (CMMI) awarded 2 Health Care Innovation Awards totaling $17.2 million to target Medicaid superutilizers.[7] Through its State Innovation Models initiative, CMMI also encourages states to pilot superutilizer programs to increase care coordination and support of persons with certain risk factors such as homelessness or mental illness.[8] Additionally, the National Governors Association developed a 1‐year, multistate policy academy to develop state‐level capacity and state action plans that guide how to improve the delivery and financing of care for superutilizers.[9]

With all these ongoing activities in the setting of a paucity of research identifying the most cost‐efficient practices to manage super‐utilizers, we are glad to see the Journal of Hospital Medicine publish an evaluation of a quality‐improvement project targeting superutilizers.[10] Mercer and colleagues at Duke University Hospital show that developing an individualized care plan and integrating it into their electronic health record (EHR) reduced hospital admissions, but not ED visits. Although we applaud the reportedly individualized patient approach and recognize the effort required to refer patients to a more appropriate care setting, we believe the researchers neglected 3 important components for the intervention: (1) patient engagement in developing individualized care plans, (2) care coordination integrated with community collaboration, and (3) feedback on continuum of care relayed back to providers. The managing strategies mentioned in the article seem to have evolved exclusively from the provider's perspective, a common mistake that the Patient‐Centered Outcomes Research Institute emphasizes must be avoided. We are concerned about the lack of clarity regarding the set of management strategies focused on providing high‐quality care while limiting unnecessary admissions reported by them. We fear this strategy was imposed on patients and not developed collaboratively with them. Effective interventions for superutilizers should do more than just guide providers actions, but also connect services to the patient's needs. There should be coordination and continuous improvement of these efforts, which requires engagement of the patient and their community with feedback to the system.

Possibly most important, an individualized approach to superutilizers needs to be patient‐centeredprioritizing patient goals and preferences, selecting interventions and services guided by the needs of the individual, and emphasizing modifiable outcomes that matter to the patient. Such a patient‐centered approach goes beyond the individual patient to incorporate information about social support and family dynamics, highlighting the role of caregivers. Patients and their caregivers must be engaged or activated to ensure adherence to appropriate care and behaviors in any superutilizer programs. Additionally, individualized patient‐centered care plans should be dynamic and bidirectional to accommodate changes in health priorities that may occur over time. Such lack of patient and community engagement may explain why ED‐visit frequency was unchanged in their study.

The approach of having a Complex Care Plan Committee deserves attention as it appropriately included the right people at the academic medical center. However, why is it voluntary? Should not an important, or even essential, committee such as this be supported by the health system? Moreover, although the care plan developed by members of the committee possesses understandable aspects to be considered in a patient's care, why is this not shown to the patient for their input? Instead of being done to the patient, we recommend including patients in this process, believing such patient engagement would improve care further and likely yield sustained changes. We suggest the researchers remember the maxim nothing about me, without me.

Patients who use the most healthcare services typically have complicated social situations that directly impact their ability to improve their health and stay well.[2, 11] Addressing the social determinants of health is not a new concept; however, creating healthy communities as a core responsibility of the healthcare industry is. Contributing to the dizzying state of change in US healthcare are efforts to shift to value‐based purchasing and population health management.[12] This transformation from a fee‐for‐service hospital‐centric industry into one focused on the continuum of care requires outreach into communities where superutilizers live. Ultimately, all healthcare is local, as this is where patients receive the vast majority of their care. Improving quality and reducing costs requires healthcare providers to work together on a collaborative mission that focuses on the needs of patients and community, not just efforts to reduce utilization. Even hospitalists must forge collaborative relationships with skilled nursing facilities and patient‐centered medical homes.

Given the successes of some superutilizer programs,[3] a key issue is how to scale or disseminate such labor‐intensive highly individualized programs. Each patient has very complex and specific medical, behavioral, and social needs that require creativity and flexibility to adequately address these needs. Without question, patients and/or their caregivers should be members of the care team aiming to optimize their care. Unfortunately, our current healthcare system is not designed to address the complexity and uniqueness of each superutilizer. Nonetheless, summarizing patients history into the EHR and integrating recommendations offers an opportunity to share information as originally hoped by the transition from paper‐based records. It additionally offers an opportunity to learn from use of this information as academic medical centers aim to become learning health systems.[13] Future implementation science research in this area should assess how to scale patient‐centered approaches to care, particularly for those with chronic illness and other vulnerabilities. We must eschew efforts that solely focus on reducing utilization by patients without involving them; after all, they are the focus of healthcare.

We have known for years that the distribution of healthcare expenditures in the United States is skewed, with a small portion of the population consuming a disproportionately high share of resources. In 2010, 1% of the population accounted for 21.4% of the $1.3 trillion spent on healthcare.[1] Growing evidence documents that most of these high‐cost patients are not receiving coordinated care, preventive care, or care in the most appropriate settings.[2] The term superutilizer describes individuals with complex physical, behavioral, and social needs who have frequent emergency department (ED) visits and multiple costly hospital admissions.[3] Not surprisingly, multiple superutilizer programs and new funding opportunities target this population attempting to reduce their healthcare costs while improving their care, as public and private insurers shift to value‐based care.[4]

Beginning in 2006, the Robert Wood Johnson Foundation supported the Camden Coalition[5] with 3 grants to develop a community‐based approach to identify high‐utilizer patients and provide them with coordinated medical and social services.[6] These programs include community‐based teams that focus on the highest utilizers in a specific geographic area and provide intensive outpatient case management. Building on these efforts, the Center for Medicare and Medicaid Innovation (CMMI) awarded 2 Health Care Innovation Awards totaling $17.2 million to target Medicaid superutilizers.[7] Through its State Innovation Models initiative, CMMI also encourages states to pilot superutilizer programs to increase care coordination and support of persons with certain risk factors such as homelessness or mental illness.[8] Additionally, the National Governors Association developed a 1‐year, multistate policy academy to develop state‐level capacity and state action plans that guide how to improve the delivery and financing of care for superutilizers.[9]

With all these ongoing activities in the setting of a paucity of research identifying the most cost‐efficient practices to manage super‐utilizers, we are glad to see the Journal of Hospital Medicine publish an evaluation of a quality‐improvement project targeting superutilizers.[10] Mercer and colleagues at Duke University Hospital show that developing an individualized care plan and integrating it into their electronic health record (EHR) reduced hospital admissions, but not ED visits. Although we applaud the reportedly individualized patient approach and recognize the effort required to refer patients to a more appropriate care setting, we believe the researchers neglected 3 important components for the intervention: (1) patient engagement in developing individualized care plans, (2) care coordination integrated with community collaboration, and (3) feedback on continuum of care relayed back to providers. The managing strategies mentioned in the article seem to have evolved exclusively from the provider's perspective, a common mistake that the Patient‐Centered Outcomes Research Institute emphasizes must be avoided. We are concerned about the lack of clarity regarding the set of management strategies focused on providing high‐quality care while limiting unnecessary admissions reported by them. We fear this strategy was imposed on patients and not developed collaboratively with them. Effective interventions for superutilizers should do more than just guide providers actions, but also connect services to the patient's needs. There should be coordination and continuous improvement of these efforts, which requires engagement of the patient and their community with feedback to the system.

Possibly most important, an individualized approach to superutilizers needs to be patient‐centeredprioritizing patient goals and preferences, selecting interventions and services guided by the needs of the individual, and emphasizing modifiable outcomes that matter to the patient. Such a patient‐centered approach goes beyond the individual patient to incorporate information about social support and family dynamics, highlighting the role of caregivers. Patients and their caregivers must be engaged or activated to ensure adherence to appropriate care and behaviors in any superutilizer programs. Additionally, individualized patient‐centered care plans should be dynamic and bidirectional to accommodate changes in health priorities that may occur over time. Such lack of patient and community engagement may explain why ED‐visit frequency was unchanged in their study.

The approach of having a Complex Care Plan Committee deserves attention as it appropriately included the right people at the academic medical center. However, why is it voluntary? Should not an important, or even essential, committee such as this be supported by the health system? Moreover, although the care plan developed by members of the committee possesses understandable aspects to be considered in a patient's care, why is this not shown to the patient for their input? Instead of being done to the patient, we recommend including patients in this process, believing such patient engagement would improve care further and likely yield sustained changes. We suggest the researchers remember the maxim nothing about me, without me.

Patients who use the most healthcare services typically have complicated social situations that directly impact their ability to improve their health and stay well.[2, 11] Addressing the social determinants of health is not a new concept; however, creating healthy communities as a core responsibility of the healthcare industry is. Contributing to the dizzying state of change in US healthcare are efforts to shift to value‐based purchasing and population health management.[12] This transformation from a fee‐for‐service hospital‐centric industry into one focused on the continuum of care requires outreach into communities where superutilizers live. Ultimately, all healthcare is local, as this is where patients receive the vast majority of their care. Improving quality and reducing costs requires healthcare providers to work together on a collaborative mission that focuses on the needs of patients and community, not just efforts to reduce utilization. Even hospitalists must forge collaborative relationships with skilled nursing facilities and patient‐centered medical homes.

Given the successes of some superutilizer programs,[3] a key issue is how to scale or disseminate such labor‐intensive highly individualized programs. Each patient has very complex and specific medical, behavioral, and social needs that require creativity and flexibility to adequately address these needs. Without question, patients and/or their caregivers should be members of the care team aiming to optimize their care. Unfortunately, our current healthcare system is not designed to address the complexity and uniqueness of each superutilizer. Nonetheless, summarizing patients history into the EHR and integrating recommendations offers an opportunity to share information as originally hoped by the transition from paper‐based records. It additionally offers an opportunity to learn from use of this information as academic medical centers aim to become learning health systems.[13] Future implementation science research in this area should assess how to scale patient‐centered approaches to care, particularly for those with chronic illness and other vulnerabilities. We must eschew efforts that solely focus on reducing utilization by patients without involving them; after all, they are the focus of healthcare.

We have known for years that the distribution of healthcare expenditures in the United States is skewed, with a small portion of the population consuming a disproportionately high share of resources. In 2010, 1% of the population accounted for 21.4% of the $1.3 trillion spent on healthcare.[1] Growing evidence documents that most of these high‐cost patients are not receiving coordinated care, preventive care, or care in the most appropriate settings.[2] The term superutilizer describes individuals with complex physical, behavioral, and social needs who have frequent emergency department (ED) visits and multiple costly hospital admissions.[3] Not surprisingly, multiple superutilizer programs and new funding opportunities target this population attempting to reduce their healthcare costs while improving their care, as public and private insurers shift to value‐based care.[4]

Beginning in 2006, the Robert Wood Johnson Foundation supported the Camden Coalition[5] with 3 grants to develop a community‐based approach to identify high‐utilizer patients and provide them with coordinated medical and social services.[6] These programs include community‐based teams that focus on the highest utilizers in a specific geographic area and provide intensive outpatient case management. Building on these efforts, the Center for Medicare and Medicaid Innovation (CMMI) awarded 2 Health Care Innovation Awards totaling $17.2 million to target Medicaid superutilizers.[7] Through its State Innovation Models initiative, CMMI also encourages states to pilot superutilizer programs to increase care coordination and support of persons with certain risk factors such as homelessness or mental illness.[8] Additionally, the National Governors Association developed a 1‐year, multistate policy academy to develop state‐level capacity and state action plans that guide how to improve the delivery and financing of care for superutilizers.[9]

With all these ongoing activities in the setting of a paucity of research identifying the most cost‐efficient practices to manage super‐utilizers, we are glad to see the Journal of Hospital Medicine publish an evaluation of a quality‐improvement project targeting superutilizers.[10] Mercer and colleagues at Duke University Hospital show that developing an individualized care plan and integrating it into their electronic health record (EHR) reduced hospital admissions, but not ED visits. Although we applaud the reportedly individualized patient approach and recognize the effort required to refer patients to a more appropriate care setting, we believe the researchers neglected 3 important components for the intervention: (1) patient engagement in developing individualized care plans, (2) care coordination integrated with community collaboration, and (3) feedback on continuum of care relayed back to providers. The managing strategies mentioned in the article seem to have evolved exclusively from the provider's perspective, a common mistake that the Patient‐Centered Outcomes Research Institute emphasizes must be avoided. We are concerned about the lack of clarity regarding the set of management strategies focused on providing high‐quality care while limiting unnecessary admissions reported by them. We fear this strategy was imposed on patients and not developed collaboratively with them. Effective interventions for superutilizers should do more than just guide providers actions, but also connect services to the patient's needs. There should be coordination and continuous improvement of these efforts, which requires engagement of the patient and their community with feedback to the system.

Possibly most important, an individualized approach to superutilizers needs to be patient‐centeredprioritizing patient goals and preferences, selecting interventions and services guided by the needs of the individual, and emphasizing modifiable outcomes that matter to the patient. Such a patient‐centered approach goes beyond the individual patient to incorporate information about social support and family dynamics, highlighting the role of caregivers. Patients and their caregivers must be engaged or activated to ensure adherence to appropriate care and behaviors in any superutilizer programs. Additionally, individualized patient‐centered care plans should be dynamic and bidirectional to accommodate changes in health priorities that may occur over time. Such lack of patient and community engagement may explain why ED‐visit frequency was unchanged in their study.

The approach of having a Complex Care Plan Committee deserves attention as it appropriately included the right people at the academic medical center. However, why is it voluntary? Should not an important, or even essential, committee such as this be supported by the health system? Moreover, although the care plan developed by members of the committee possesses understandable aspects to be considered in a patient's care, why is this not shown to the patient for their input? Instead of being done to the patient, we recommend including patients in this process, believing such patient engagement would improve care further and likely yield sustained changes. We suggest the researchers remember the maxim nothing about me, without me.

Patients who use the most healthcare services typically have complicated social situations that directly impact their ability to improve their health and stay well.[2, 11] Addressing the social determinants of health is not a new concept; however, creating healthy communities as a core responsibility of the healthcare industry is. Contributing to the dizzying state of change in US healthcare are efforts to shift to value‐based purchasing and population health management.[12] This transformation from a fee‐for‐service hospital‐centric industry into one focused on the continuum of care requires outreach into communities where superutilizers live. Ultimately, all healthcare is local, as this is where patients receive the vast majority of their care. Improving quality and reducing costs requires healthcare providers to work together on a collaborative mission that focuses on the needs of patients and community, not just efforts to reduce utilization. Even hospitalists must forge collaborative relationships with skilled nursing facilities and patient‐centered medical homes.

Given the successes of some superutilizer programs,[3] a key issue is how to scale or disseminate such labor‐intensive highly individualized programs. Each patient has very complex and specific medical, behavioral, and social needs that require creativity and flexibility to adequately address these needs. Without question, patients and/or their caregivers should be members of the care team aiming to optimize their care. Unfortunately, our current healthcare system is not designed to address the complexity and uniqueness of each superutilizer. Nonetheless, summarizing patients history into the EHR and integrating recommendations offers an opportunity to share information as originally hoped by the transition from paper‐based records. It additionally offers an opportunity to learn from use of this information as academic medical centers aim to become learning health systems.[13] Future implementation science research in this area should assess how to scale patient‐centered approaches to care, particularly for those with chronic illness and other vulnerabilities. We must eschew efforts that solely focus on reducing utilization by patients without involving them; after all, they are the focus of healthcare.