Despite calls for board-certified intensivist physicians to lead critical care delivery,^1-3 the intensivist shortage in the United States continues to worsen,⁴ with projected shortfalls of 22% by 2020 and 35% by 2030.⁵ Many hospitals currently have inadequate or no board-certified intensivist support.⁶ The intensivist shortage has necessitated the development of alternative intensive care unit (ICU) staffing models, including engagement in telemedicine,⁷ the utilization of advanced practice providers,⁸ and dependence on hospitalists⁹ to deliver critical care services to ICU patients. Presently, research does not clearly show consistent differences in clinical outcomes based on the training of the clinical provider, although optimized teamwork and team rounds in the ICU do seem to be associated with improved outcomes.^10-12

In its 2016 annual survey of hospital medicine (HM) leaders, the Society of Hospital Medicine (SHM) documented that most HM groups care for ICU patients, with up to 80% of hospitalist groups in some regions delivering critical care.¹³ In many United States hospitals, hospitalists serve as the primary if not lone physician providers of critical care.^6,14 HM, with its team-based approach and on-site presence, shares many of the key attributes and values that define high-functioning critical care teams, and many hospitalists likely capably deliver some critical care services.⁹ However, hospitalists are also a highly heterogeneous work force with varied exposure to and comfort with critical care medicine, making it difficult to generalize hospitalists’ scope of practice in the ICU.

Because hospitalists render a significant amount of critical care in the United States, we surveyed practicing hospitalists to understand their demographics and practice roles in the ICU setting and to ascertain how they are supported when doing so. Additionally, we sought to identify mismatches between the ICU services that hospitalists provide and what they feel prepared and supported to deliver. Finally, we attempted to elucidate how hospitalists who practice in the ICU might respond to novel educational offerings targeted to mitigate cognitive or procedural gaps.

We developed and deployed a survey to address the aforementioned questions. The survey content was developed iteratively by the Critical Care Task Force of SHM’s Education Committee and subsequently approved by SHM’s Education Committee and Board of Directors. Members of the Critical Care Task Force include critical care physicians and hospitalists. The survey included 25 items (supplemental Appendix A). Seventeen questions addressed the demographics and practice roles of hospitalists in the ICU, 5 addressed cognitive and procedural practice gaps, and 3 addressed how hospitalists would respond to educational opportunities in critical care. We used conditional formatting to ensure that only respondents who deliver ICU care could answer questions related to ICU practice. The survey was delivered by using an online survey platform (Survey Monkey, San Mateo, CA).

The survey was deployed in 3 phases from March to October of 2016. Initially, we distributed a pilot survey to professional contacts of the Critical Care Task Force to solicit feedback and refine the survey’s format and content. These contacts were largely academic hospitalists from our local institutions. We then distributed the survey to hospitalists via professional networks with instructions to forward the link to interested hospitalists. Finally, we distributed the survey to approximately 4000 hospitalists randomly selected from SHM’s national listserv of approximately 12,000 hospitalists. Respondents could enter a drawing for a monetary prize upon completion of the survey.

None of the survey questions changed during the 3 phases of survey deployment, and the data reported herein were compiled from all 3 phases of the survey deployment. Frequency tables were created using Tableau (version 10.0; Tableau Software, Seattle, WA). Comparisons between categorical questions were made by using χ² and Fischer exact tests to calculate P values for associations by using SAS (version 9.3; SAS Institute, Cary, NC). Associations with P values below .05 were considered statistically significant.

Objective 1: Demographics and Practice Role

Four hundred and twenty-five hospitalists responded to the survey. The first 2 phases (pilot survey and distribution via professional networks) generated 101 responses, and the third phase (via SHM’s listserv) generated an additional 324 responses. As the survey was anonymous, we could not determine which hospitals or geographic regions were represented. Three hundred and twenty-five of the 425 hospitalists who completed the survey (77%) reported that they delivered care in the ICU. Of these 325 hospitalists, 45 served only as consultants, while the remaining 280 (66% of the total sample) served as the primary attending physician in the ICU. Among these primary providers of care in the ICU, 60 (21%) practiced in rural settings and 220 (79%) practiced in nonrural settings (Figure 1).

The demographics of our respondents were similar to those of the SHM annual survey,¹³ in which 66% of respondents delivered ICU care. Forty-one percent of our respondents worked in critical access or small community hospitals, 24% in academic medical centers, and 34% in large community centers with an academic affiliation. The SHM annual survey cohort included more physicians from nonteaching hospitals (58.7%) and fewer from academic medical centers (14.8%).¹³

Hospitalists’ presence in the ICU varied by practice setting (Table 1).

Objective 2: Identifying the Practice Gap

Hospitalists’ perceptions of practicing critical care beyond their skill level and without sufficient board-certified intensivist support varied by both practice location and practice type (Table 3).

There were similar discrepancies between academic and nonacademic respondents. Forty-two percent of respondents practicing in nonacademic settings reported being expected to practice beyond their scope at least some of the time, and 18% reported that intensivist support was never sufficient. This contrasts with academic hospitalists, of whom 35% reported feeling expected to practice outside their scope, and less than 4% reported the available support from intensivists was never sufficient. For comparisons of academic and nonacademic respondents, only perceptions of sufficient board-certified intensivist support reached statistical significance (Table 3).

The role of intensivists in making management decisions and the strategy for ventilator management decisions correlated significantly with perception of intensivist support (P < .001) but not with the perception of practicing beyond one’s scope. The number of ventilated patients did not correlate significantly with either perception of intensivist support or of being expected to practice beyond scope.

Difficulty transferring patients to a higher level of care was the only attribute that significantly correlated with hospitalists’ perceptions of having to practice beyond their skill level (P < .05; Table 3). Difficulty of transfer was also significantly associated with perceived adequacy of board-certified intensivist support (P < .001). Total hours of intensivist coverage, intensivist role in decision making, and ventilator management arrangements also correlated significantly with the perceived adequacy of board-certified intensivist support (P < .001 for all; Table 3).

Objective 3: Assessing Interest in Critical Care Education

More than 85% of respondents indicated interest in obtaining additional critical care training and some form of certification short of fellowship training. Preferred modes of content delivery included courses or precourses at national meetings, academies, or online modules. Hospitalists in smaller communities indicated preference for online resources.

This survey of a large national cohort of hospitalists from diverse practice settings validates previous studies suggesting that hospitalists deliver critical care services, most notably in community and rural hospitals.¹³ A substantial subset of our respondents represented rural practice settings, which allowed us to compare rural and nonrural hospitalists as well as those practicing in academic and nonacademic settings. In assessing both the objective services that hospitalists provided as well as their subjective perceptions of how they practiced, we could correlate factors associated with the sense of practicing beyond one’s skill or feeling inadequately supported by board-certified intensivists.

More than a third of responding hospitalists who practiced in the ICU reported that they practiced beyond their self-perceived skill level, and almost three-fourths indicated that they practiced without consistent or adequate board-certified intensivist support. Rural and nonacademic hospitalists were far more likely to report delivering critical care beyond their comfort level and having insufficient board-certified intensivist support.

Calls for board-certified intensivists to deliver critical care to all critically ill patients do not reflect the reality in many American hospitals and, either by intent or by default, hospitalists have become the major and often sole providers of critical care services in many hospitals without robust intensivist support. We suspect that this phenomenon has been consistently underreported in the literature because academic hospitalists generally do not practice critical care.¹⁵

Many potential solutions to the intensivist shortage have been explored. Prior efforts in the United States have focused largely on care standardization and the recruitment of more trainees into existing critical care training pathways.¹⁶ Other countries have created multidisciplinary critical care training pathways that delink critical care from specific subspecialty training programs.¹⁷ Another potential solution to ensure that critically ill patients receive care from board-certified intensivists is to regionalize critical care such that the sickest patients are consistently transferred to referral centers with robust intensivist staffing.^1,18 While such an approach has been effectively implemented for trauma patients⁷, it has yet to materialize on a systemic basis for other critically ill cohorts. Moreover, our data suggest that hospitalists who attempt to transfer patients to higher levels of critical care find doing so burdensome and difficult.

Our surveyed hospitalists overwhelmingly expressed interest in augmenting their critical care skills and knowledge. However, most existing critical care educational offerings are not optimized for hospitalists, either focusing on very specific skills or knowledge (eg, procedural techniques or point-of-care ultrasound) or providing entry-level or very foundational education. None of these offerings provide comprehensive, structured training schemas for hospitalists who need to evolve beyond basic critical care skills to manage critically ill patients competently and consistently for extended periods of time.

Our study has several limitations. First, we estimate that about 10% of invited participants responded to this survey, but as respondents could forward the survey via professional networks, this is only an estimate. It is possible but unlikely that some respondents could have completed the survey more than once. Second, because our analysis identified only associations, we cannot infer causality for any of our findings. Third, the questionnaire was not designed to capture the acuity threshold at which point each respondent would prefer to transfer their patients into an ICU setting or to another institution for assistance in critical care management. We recognize that definitions and perceptions of patient acuity vary markedly from one hospital to the next, and a patient who can be comfortably managed in a floor setting in one hospital may require ICU care in a smaller or less well-resourced hospital. Practice patterns relating to acuity thresholds could have a substantial impact both on critical care patient volumes and on provider perceptions and, as such, warrant further study.

Finally, as respondents participated voluntarily, our sample may have overrepresented hospitalists who practice or are interested in critical care, thereby overestimating the scope of the problem and hospitalists’ interest in nonfellowship critical care training and certification. However, this seems unlikely given that, relative to SHM’s annual survey, we overrepresented hospitalists from academic and large community medical centers who generally provide less critical care than other hospitalists.¹³ Provided that roughly 85% of the estimated 50,000 American hospitalists practice outside of academic medical centers,¹³ perhaps as many as 37,000 hospitalists regularly deliver care to critically ill patients in ICUs. In light of the evolving intensivist shortage,^4,5 this number seems likely to continue to grow. Whatever biases may exist in our sample, it is evident that a substantial number of ICU patients are managed by hospitalists who feel unprepared and undersupported to perform the task.

Without a massive and sustained increase in the number of board-certified intensivists or a systemic national plan to regionalize critical care delivery, hospitalists will continue to practice critical care, frequently with inadequate knowledge, skills, or intensivist support. Fortunately, these same hospitalists appear to be highly interested in augmenting their skills to care for their critically ill patients. The HM and critical care communities must rise to this challenge and help these providers deliver safe, appropriate, and high-quality care to their critically ill patients.

Disclosure

Mark V. Williams, MD, FACP, MHM, receives funding from the Patient Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, Centers for Medicare & Medicaid Services, and Society of Hospital Medicine honoraria.

• SHM Offerings on Critical Care
• Critical Care for the Hospitalist Series on the SHM Learning Portal

Despite calls for board-certified intensivist physicians to lead critical care delivery,^1-3 the intensivist shortage in the United States continues to worsen,⁴ with projected shortfalls of 22% by 2020 and 35% by 2030.⁵ Many hospitals currently have inadequate or no board-certified intensivist support.⁶ The intensivist shortage has necessitated the development of alternative intensive care unit (ICU) staffing models, including engagement in telemedicine,⁷ the utilization of advanced practice providers,⁸ and dependence on hospitalists⁹ to deliver critical care services to ICU patients. Presently, research does not clearly show consistent differences in clinical outcomes based on the training of the clinical provider, although optimized teamwork and team rounds in the ICU do seem to be associated with improved outcomes.^10-12

In its 2016 annual survey of hospital medicine (HM) leaders, the Society of Hospital Medicine (SHM) documented that most HM groups care for ICU patients, with up to 80% of hospitalist groups in some regions delivering critical care.¹³ In many United States hospitals, hospitalists serve as the primary if not lone physician providers of critical care.^6,14 HM, with its team-based approach and on-site presence, shares many of the key attributes and values that define high-functioning critical care teams, and many hospitalists likely capably deliver some critical care services.⁹ However, hospitalists are also a highly heterogeneous work force with varied exposure to and comfort with critical care medicine, making it difficult to generalize hospitalists’ scope of practice in the ICU.

Because hospitalists render a significant amount of critical care in the United States, we surveyed practicing hospitalists to understand their demographics and practice roles in the ICU setting and to ascertain how they are supported when doing so. Additionally, we sought to identify mismatches between the ICU services that hospitalists provide and what they feel prepared and supported to deliver. Finally, we attempted to elucidate how hospitalists who practice in the ICU might respond to novel educational offerings targeted to mitigate cognitive or procedural gaps.

We developed and deployed a survey to address the aforementioned questions. The survey content was developed iteratively by the Critical Care Task Force of SHM’s Education Committee and subsequently approved by SHM’s Education Committee and Board of Directors. Members of the Critical Care Task Force include critical care physicians and hospitalists. The survey included 25 items (supplemental Appendix A). Seventeen questions addressed the demographics and practice roles of hospitalists in the ICU, 5 addressed cognitive and procedural practice gaps, and 3 addressed how hospitalists would respond to educational opportunities in critical care. We used conditional formatting to ensure that only respondents who deliver ICU care could answer questions related to ICU practice. The survey was delivered by using an online survey platform (Survey Monkey, San Mateo, CA).

The survey was deployed in 3 phases from March to October of 2016. Initially, we distributed a pilot survey to professional contacts of the Critical Care Task Force to solicit feedback and refine the survey’s format and content. These contacts were largely academic hospitalists from our local institutions. We then distributed the survey to hospitalists via professional networks with instructions to forward the link to interested hospitalists. Finally, we distributed the survey to approximately 4000 hospitalists randomly selected from SHM’s national listserv of approximately 12,000 hospitalists. Respondents could enter a drawing for a monetary prize upon completion of the survey.

None of the survey questions changed during the 3 phases of survey deployment, and the data reported herein were compiled from all 3 phases of the survey deployment. Frequency tables were created using Tableau (version 10.0; Tableau Software, Seattle, WA). Comparisons between categorical questions were made by using χ² and Fischer exact tests to calculate P values for associations by using SAS (version 9.3; SAS Institute, Cary, NC). Associations with P values below .05 were considered statistically significant.

Objective 1: Demographics and Practice Role

Four hundred and twenty-five hospitalists responded to the survey. The first 2 phases (pilot survey and distribution via professional networks) generated 101 responses, and the third phase (via SHM’s listserv) generated an additional 324 responses. As the survey was anonymous, we could not determine which hospitals or geographic regions were represented. Three hundred and twenty-five of the 425 hospitalists who completed the survey (77%) reported that they delivered care in the ICU. Of these 325 hospitalists, 45 served only as consultants, while the remaining 280 (66% of the total sample) served as the primary attending physician in the ICU. Among these primary providers of care in the ICU, 60 (21%) practiced in rural settings and 220 (79%) practiced in nonrural settings (Figure 1).

The demographics of our respondents were similar to those of the SHM annual survey,¹³ in which 66% of respondents delivered ICU care. Forty-one percent of our respondents worked in critical access or small community hospitals, 24% in academic medical centers, and 34% in large community centers with an academic affiliation. The SHM annual survey cohort included more physicians from nonteaching hospitals (58.7%) and fewer from academic medical centers (14.8%).¹³

Hospitalists’ presence in the ICU varied by practice setting (Table 1).

Objective 2: Identifying the Practice Gap

Hospitalists’ perceptions of practicing critical care beyond their skill level and without sufficient board-certified intensivist support varied by both practice location and practice type (Table 3).

There were similar discrepancies between academic and nonacademic respondents. Forty-two percent of respondents practicing in nonacademic settings reported being expected to practice beyond their scope at least some of the time, and 18% reported that intensivist support was never sufficient. This contrasts with academic hospitalists, of whom 35% reported feeling expected to practice outside their scope, and less than 4% reported the available support from intensivists was never sufficient. For comparisons of academic and nonacademic respondents, only perceptions of sufficient board-certified intensivist support reached statistical significance (Table 3).

The role of intensivists in making management decisions and the strategy for ventilator management decisions correlated significantly with perception of intensivist support (P < .001) but not with the perception of practicing beyond one’s scope. The number of ventilated patients did not correlate significantly with either perception of intensivist support or of being expected to practice beyond scope.

Difficulty transferring patients to a higher level of care was the only attribute that significantly correlated with hospitalists’ perceptions of having to practice beyond their skill level (P < .05; Table 3). Difficulty of transfer was also significantly associated with perceived adequacy of board-certified intensivist support (P < .001). Total hours of intensivist coverage, intensivist role in decision making, and ventilator management arrangements also correlated significantly with the perceived adequacy of board-certified intensivist support (P < .001 for all; Table 3).

Objective 3: Assessing Interest in Critical Care Education

More than 85% of respondents indicated interest in obtaining additional critical care training and some form of certification short of fellowship training. Preferred modes of content delivery included courses or precourses at national meetings, academies, or online modules. Hospitalists in smaller communities indicated preference for online resources.

This survey of a large national cohort of hospitalists from diverse practice settings validates previous studies suggesting that hospitalists deliver critical care services, most notably in community and rural hospitals.¹³ A substantial subset of our respondents represented rural practice settings, which allowed us to compare rural and nonrural hospitalists as well as those practicing in academic and nonacademic settings. In assessing both the objective services that hospitalists provided as well as their subjective perceptions of how they practiced, we could correlate factors associated with the sense of practicing beyond one’s skill or feeling inadequately supported by board-certified intensivists.

More than a third of responding hospitalists who practiced in the ICU reported that they practiced beyond their self-perceived skill level, and almost three-fourths indicated that they practiced without consistent or adequate board-certified intensivist support. Rural and nonacademic hospitalists were far more likely to report delivering critical care beyond their comfort level and having insufficient board-certified intensivist support.

Calls for board-certified intensivists to deliver critical care to all critically ill patients do not reflect the reality in many American hospitals and, either by intent or by default, hospitalists have become the major and often sole providers of critical care services in many hospitals without robust intensivist support. We suspect that this phenomenon has been consistently underreported in the literature because academic hospitalists generally do not practice critical care.¹⁵

Many potential solutions to the intensivist shortage have been explored. Prior efforts in the United States have focused largely on care standardization and the recruitment of more trainees into existing critical care training pathways.¹⁶ Other countries have created multidisciplinary critical care training pathways that delink critical care from specific subspecialty training programs.¹⁷ Another potential solution to ensure that critically ill patients receive care from board-certified intensivists is to regionalize critical care such that the sickest patients are consistently transferred to referral centers with robust intensivist staffing.^1,18 While such an approach has been effectively implemented for trauma patients⁷, it has yet to materialize on a systemic basis for other critically ill cohorts. Moreover, our data suggest that hospitalists who attempt to transfer patients to higher levels of critical care find doing so burdensome and difficult.

Our surveyed hospitalists overwhelmingly expressed interest in augmenting their critical care skills and knowledge. However, most existing critical care educational offerings are not optimized for hospitalists, either focusing on very specific skills or knowledge (eg, procedural techniques or point-of-care ultrasound) or providing entry-level or very foundational education. None of these offerings provide comprehensive, structured training schemas for hospitalists who need to evolve beyond basic critical care skills to manage critically ill patients competently and consistently for extended periods of time.

Our study has several limitations. First, we estimate that about 10% of invited participants responded to this survey, but as respondents could forward the survey via professional networks, this is only an estimate. It is possible but unlikely that some respondents could have completed the survey more than once. Second, because our analysis identified only associations, we cannot infer causality for any of our findings. Third, the questionnaire was not designed to capture the acuity threshold at which point each respondent would prefer to transfer their patients into an ICU setting or to another institution for assistance in critical care management. We recognize that definitions and perceptions of patient acuity vary markedly from one hospital to the next, and a patient who can be comfortably managed in a floor setting in one hospital may require ICU care in a smaller or less well-resourced hospital. Practice patterns relating to acuity thresholds could have a substantial impact both on critical care patient volumes and on provider perceptions and, as such, warrant further study.

Finally, as respondents participated voluntarily, our sample may have overrepresented hospitalists who practice or are interested in critical care, thereby overestimating the scope of the problem and hospitalists’ interest in nonfellowship critical care training and certification. However, this seems unlikely given that, relative to SHM’s annual survey, we overrepresented hospitalists from academic and large community medical centers who generally provide less critical care than other hospitalists.¹³ Provided that roughly 85% of the estimated 50,000 American hospitalists practice outside of academic medical centers,¹³ perhaps as many as 37,000 hospitalists regularly deliver care to critically ill patients in ICUs. In light of the evolving intensivist shortage,^4,5 this number seems likely to continue to grow. Whatever biases may exist in our sample, it is evident that a substantial number of ICU patients are managed by hospitalists who feel unprepared and undersupported to perform the task.

Without a massive and sustained increase in the number of board-certified intensivists or a systemic national plan to regionalize critical care delivery, hospitalists will continue to practice critical care, frequently with inadequate knowledge, skills, or intensivist support. Fortunately, these same hospitalists appear to be highly interested in augmenting their skills to care for their critically ill patients. The HM and critical care communities must rise to this challenge and help these providers deliver safe, appropriate, and high-quality care to their critically ill patients.

Disclosure

Mark V. Williams, MD, FACP, MHM, receives funding from the Patient Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, Centers for Medicare & Medicaid Services, and Society of Hospital Medicine honoraria.

• SHM Offerings on Critical Care
• Critical Care for the Hospitalist Series on the SHM Learning Portal

Despite calls for board-certified intensivist physicians to lead critical care delivery,^1-3 the intensivist shortage in the United States continues to worsen,⁴ with projected shortfalls of 22% by 2020 and 35% by 2030.⁵ Many hospitals currently have inadequate or no board-certified intensivist support.⁶ The intensivist shortage has necessitated the development of alternative intensive care unit (ICU) staffing models, including engagement in telemedicine,⁷ the utilization of advanced practice providers,⁸ and dependence on hospitalists⁹ to deliver critical care services to ICU patients. Presently, research does not clearly show consistent differences in clinical outcomes based on the training of the clinical provider, although optimized teamwork and team rounds in the ICU do seem to be associated with improved outcomes.^10-12

In its 2016 annual survey of hospital medicine (HM) leaders, the Society of Hospital Medicine (SHM) documented that most HM groups care for ICU patients, with up to 80% of hospitalist groups in some regions delivering critical care.¹³ In many United States hospitals, hospitalists serve as the primary if not lone physician providers of critical care.^6,14 HM, with its team-based approach and on-site presence, shares many of the key attributes and values that define high-functioning critical care teams, and many hospitalists likely capably deliver some critical care services.⁹ However, hospitalists are also a highly heterogeneous work force with varied exposure to and comfort with critical care medicine, making it difficult to generalize hospitalists’ scope of practice in the ICU.

Because hospitalists render a significant amount of critical care in the United States, we surveyed practicing hospitalists to understand their demographics and practice roles in the ICU setting and to ascertain how they are supported when doing so. Additionally, we sought to identify mismatches between the ICU services that hospitalists provide and what they feel prepared and supported to deliver. Finally, we attempted to elucidate how hospitalists who practice in the ICU might respond to novel educational offerings targeted to mitigate cognitive or procedural gaps.

We developed and deployed a survey to address the aforementioned questions. The survey content was developed iteratively by the Critical Care Task Force of SHM’s Education Committee and subsequently approved by SHM’s Education Committee and Board of Directors. Members of the Critical Care Task Force include critical care physicians and hospitalists. The survey included 25 items (supplemental Appendix A). Seventeen questions addressed the demographics and practice roles of hospitalists in the ICU, 5 addressed cognitive and procedural practice gaps, and 3 addressed how hospitalists would respond to educational opportunities in critical care. We used conditional formatting to ensure that only respondents who deliver ICU care could answer questions related to ICU practice. The survey was delivered by using an online survey platform (Survey Monkey, San Mateo, CA).

The survey was deployed in 3 phases from March to October of 2016. Initially, we distributed a pilot survey to professional contacts of the Critical Care Task Force to solicit feedback and refine the survey’s format and content. These contacts were largely academic hospitalists from our local institutions. We then distributed the survey to hospitalists via professional networks with instructions to forward the link to interested hospitalists. Finally, we distributed the survey to approximately 4000 hospitalists randomly selected from SHM’s national listserv of approximately 12,000 hospitalists. Respondents could enter a drawing for a monetary prize upon completion of the survey.

None of the survey questions changed during the 3 phases of survey deployment, and the data reported herein were compiled from all 3 phases of the survey deployment. Frequency tables were created using Tableau (version 10.0; Tableau Software, Seattle, WA). Comparisons between categorical questions were made by using χ² and Fischer exact tests to calculate P values for associations by using SAS (version 9.3; SAS Institute, Cary, NC). Associations with P values below .05 were considered statistically significant.

Objective 1: Demographics and Practice Role

Four hundred and twenty-five hospitalists responded to the survey. The first 2 phases (pilot survey and distribution via professional networks) generated 101 responses, and the third phase (via SHM’s listserv) generated an additional 324 responses. As the survey was anonymous, we could not determine which hospitals or geographic regions were represented. Three hundred and twenty-five of the 425 hospitalists who completed the survey (77%) reported that they delivered care in the ICU. Of these 325 hospitalists, 45 served only as consultants, while the remaining 280 (66% of the total sample) served as the primary attending physician in the ICU. Among these primary providers of care in the ICU, 60 (21%) practiced in rural settings and 220 (79%) practiced in nonrural settings (Figure 1).

The demographics of our respondents were similar to those of the SHM annual survey,¹³ in which 66% of respondents delivered ICU care. Forty-one percent of our respondents worked in critical access or small community hospitals, 24% in academic medical centers, and 34% in large community centers with an academic affiliation. The SHM annual survey cohort included more physicians from nonteaching hospitals (58.7%) and fewer from academic medical centers (14.8%).¹³

Hospitalists’ presence in the ICU varied by practice setting (Table 1).

Objective 2: Identifying the Practice Gap

Hospitalists’ perceptions of practicing critical care beyond their skill level and without sufficient board-certified intensivist support varied by both practice location and practice type (Table 3).

There were similar discrepancies between academic and nonacademic respondents. Forty-two percent of respondents practicing in nonacademic settings reported being expected to practice beyond their scope at least some of the time, and 18% reported that intensivist support was never sufficient. This contrasts with academic hospitalists, of whom 35% reported feeling expected to practice outside their scope, and less than 4% reported the available support from intensivists was never sufficient. For comparisons of academic and nonacademic respondents, only perceptions of sufficient board-certified intensivist support reached statistical significance (Table 3).

The role of intensivists in making management decisions and the strategy for ventilator management decisions correlated significantly with perception of intensivist support (P < .001) but not with the perception of practicing beyond one’s scope. The number of ventilated patients did not correlate significantly with either perception of intensivist support or of being expected to practice beyond scope.

Difficulty transferring patients to a higher level of care was the only attribute that significantly correlated with hospitalists’ perceptions of having to practice beyond their skill level (P < .05; Table 3). Difficulty of transfer was also significantly associated with perceived adequacy of board-certified intensivist support (P < .001). Total hours of intensivist coverage, intensivist role in decision making, and ventilator management arrangements also correlated significantly with the perceived adequacy of board-certified intensivist support (P < .001 for all; Table 3).

Objective 3: Assessing Interest in Critical Care Education

More than 85% of respondents indicated interest in obtaining additional critical care training and some form of certification short of fellowship training. Preferred modes of content delivery included courses or precourses at national meetings, academies, or online modules. Hospitalists in smaller communities indicated preference for online resources.

This survey of a large national cohort of hospitalists from diverse practice settings validates previous studies suggesting that hospitalists deliver critical care services, most notably in community and rural hospitals.¹³ A substantial subset of our respondents represented rural practice settings, which allowed us to compare rural and nonrural hospitalists as well as those practicing in academic and nonacademic settings. In assessing both the objective services that hospitalists provided as well as their subjective perceptions of how they practiced, we could correlate factors associated with the sense of practicing beyond one’s skill or feeling inadequately supported by board-certified intensivists.

More than a third of responding hospitalists who practiced in the ICU reported that they practiced beyond their self-perceived skill level, and almost three-fourths indicated that they practiced without consistent or adequate board-certified intensivist support. Rural and nonacademic hospitalists were far more likely to report delivering critical care beyond their comfort level and having insufficient board-certified intensivist support.

Calls for board-certified intensivists to deliver critical care to all critically ill patients do not reflect the reality in many American hospitals and, either by intent or by default, hospitalists have become the major and often sole providers of critical care services in many hospitals without robust intensivist support. We suspect that this phenomenon has been consistently underreported in the literature because academic hospitalists generally do not practice critical care.¹⁵

Many potential solutions to the intensivist shortage have been explored. Prior efforts in the United States have focused largely on care standardization and the recruitment of more trainees into existing critical care training pathways.¹⁶ Other countries have created multidisciplinary critical care training pathways that delink critical care from specific subspecialty training programs.¹⁷ Another potential solution to ensure that critically ill patients receive care from board-certified intensivists is to regionalize critical care such that the sickest patients are consistently transferred to referral centers with robust intensivist staffing.^1,18 While such an approach has been effectively implemented for trauma patients⁷, it has yet to materialize on a systemic basis for other critically ill cohorts. Moreover, our data suggest that hospitalists who attempt to transfer patients to higher levels of critical care find doing so burdensome and difficult.

Our surveyed hospitalists overwhelmingly expressed interest in augmenting their critical care skills and knowledge. However, most existing critical care educational offerings are not optimized for hospitalists, either focusing on very specific skills or knowledge (eg, procedural techniques or point-of-care ultrasound) or providing entry-level or very foundational education. None of these offerings provide comprehensive, structured training schemas for hospitalists who need to evolve beyond basic critical care skills to manage critically ill patients competently and consistently for extended periods of time.

Our study has several limitations. First, we estimate that about 10% of invited participants responded to this survey, but as respondents could forward the survey via professional networks, this is only an estimate. It is possible but unlikely that some respondents could have completed the survey more than once. Second, because our analysis identified only associations, we cannot infer causality for any of our findings. Third, the questionnaire was not designed to capture the acuity threshold at which point each respondent would prefer to transfer their patients into an ICU setting or to another institution for assistance in critical care management. We recognize that definitions and perceptions of patient acuity vary markedly from one hospital to the next, and a patient who can be comfortably managed in a floor setting in one hospital may require ICU care in a smaller or less well-resourced hospital. Practice patterns relating to acuity thresholds could have a substantial impact both on critical care patient volumes and on provider perceptions and, as such, warrant further study.

Finally, as respondents participated voluntarily, our sample may have overrepresented hospitalists who practice or are interested in critical care, thereby overestimating the scope of the problem and hospitalists’ interest in nonfellowship critical care training and certification. However, this seems unlikely given that, relative to SHM’s annual survey, we overrepresented hospitalists from academic and large community medical centers who generally provide less critical care than other hospitalists.¹³ Provided that roughly 85% of the estimated 50,000 American hospitalists practice outside of academic medical centers,¹³ perhaps as many as 37,000 hospitalists regularly deliver care to critically ill patients in ICUs. In light of the evolving intensivist shortage,^4,5 this number seems likely to continue to grow. Whatever biases may exist in our sample, it is evident that a substantial number of ICU patients are managed by hospitalists who feel unprepared and undersupported to perform the task.

Without a massive and sustained increase in the number of board-certified intensivists or a systemic national plan to regionalize critical care delivery, hospitalists will continue to practice critical care, frequently with inadequate knowledge, skills, or intensivist support. Fortunately, these same hospitalists appear to be highly interested in augmenting their skills to care for their critically ill patients. The HM and critical care communities must rise to this challenge and help these providers deliver safe, appropriate, and high-quality care to their critically ill patients.

Disclosure

Mark V. Williams, MD, FACP, MHM, receives funding from the Patient Centered Outcomes Research Institute, Agency for Healthcare Research and Quality, Centers for Medicare & Medicaid Services, and Society of Hospital Medicine honoraria.

• SHM Offerings on Critical Care
• Critical Care for the Hospitalist Series on the SHM Learning Portal

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but that may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Acute gastroenteritis (AGE) remains a substantial cause of childhood illness and is 1 of the top 10 reasons for pediatric hospitalization nationwide. In the United States, AGE is responsible for 10% of hospital admissions and approximately 300 deaths annually.¹ The American Academy of Pediatrics (AAP) and other organizations have emphasized supportive care in the management of AGE. Routine diagnostic testing has been discouraged in national guidelines except in cases of severe dehydration or an otherwise complicated course. Despite AGE guidelines, diagnostic laboratory tests are still widely used even though they have been shown to be poor predictors of dehydration. Studies have shown that high test utilization in various pediatric disease processes often influences the decision for hospitalization without improvement in patient outcome. In children with AGE, the initial and follow-up laboratory tests may not only be something that we do for no reason, but something that is associated with more risk than benefit.

An 18-month-old healthy male is brought to the emergency department (ED) with a chief complaint of 2 days of nonbloody, nonbilious emesis and watery diarrhea. He has decreased energy but smiles and plays for a few minutes. He has had decreased wet diapers. His exam is notable for mild tachycardia, mildly dry lips, and capillary refill of 3 seconds. A serum electrolyte panel is normal except for a sodium of 134 mEq/L, a bicarbonate of 16 mEq/L, and an anion gap of 18, which are flagged as abnormal by the electronic medical record. These results prompt intravenous (IV) access, a normal saline bolus, and admission on maintenance fluids overnight. The next morning, his electrolyte panel is repeated, and his sodium is 140 mEq/L and bicarbonate is 15 mEq/L. He is now drinking well with no further episodes of emesis, so he is discharged home.

Many physicians across the United States continue to order electrolytes in AGE as a way to avoid missing severe dehydration, severe electrolyte abnormalities, or rare diagnoses, such as adrenal insufficiency or new-onset diabetes, in a child. Previous studies have revealed that bicarbonate and blood urea nitrogen (BUN) may be helpful predictors of severe dehydration. A retrospective study of 168 patients by Yilmaz et al.² showed that BUN and bicarbonate strongly correlated with dehydration severity (P < 0.00001 and P = 0.01, respectively). A 97-patient prospective study by Vega and Avner³ showed that bicarbonate <17 can help in predicting percent body weight loss (PBWL) (sensitivity of 77% for PBWL 6-10 and 94% for PBWL >10).

In AGE, obtaining laboratory data is often considered to be the more conservative approach. Some attribute this to the medical education and legal system rewarding the uncovering of rare diagnoses,⁴ while others believe physicians obtain laboratory data to avoid missing severe electrolyte disorders. One author notes, “physicians who are anxious about a patient’s problem may be tempted to do something—anything—decisive in order to diminish their own anxiety.”⁵ Severe electrolyte derangements are common in developing countries⁶ but less so in the United States. A prospective pediatric dehydration study over 1 year in the United States demonstrated rates of 6% and 3% of hypo- and hypernatremia, respectively (n = 182). Only 1 patient had a sodium level >160, and this patient had an underlying genetic syndrome, and none had hyponatremia <130. Hypoglycemia was the most common electrolyte abnormality, which was present in 9.8% of patients. Electrolyte results changed management in 10.4% of patients.⁷

In AGE with or without dehydration, guidelines from the AAP and other international organizations emphasize supportive care in the management of AGE and discourage routine diagnostic testing.^8-10 Yet, there continues to be wide variation in AGE management.^11-13 Most AGE cases presenting to an outpatient setting or ED are uncomplicated: age >6 months, nontoxic appearance, no comorbidities, no hematochezia, diarrhea <7 days, and mild-to-moderate dehydration.

Steiner et al.¹⁴ performed a systematic meta-analysis of the precision and accuracy of symptoms, signs, and laboratory tests for evaluating dehydration in children. They concluded that a standardized clinical assessment based on physical exam (PE) findings more accurately classifies the degree of dehydration than laboratory testing. Steiner et al¹⁴ specifically analyzed the works by Yilmaz et al.² and Vega and Avner,³ and determined that the positive likelihood ratios for >5% dehydration resulting from a BUN >45 or bicarbonate <17 were too small or had confidence intervals that were too wide to be clinically helpful alone. Therefore, Steiner et al.¹⁴ recommended that laboratory testing should not be considered definitive for dehydration.

Vega and Avner³ found that electrolyte testing is less helpful in distinguishing between <5% (mild) and 5% to 10% (moderate) dehydration compared to PBWL. Because both mild and moderate dehydration respond equally well to oral rehydration therapy (ORT),⁸ electrolyte testing is not helpful in managing these categories. Many studies have excluded children with hypernatremia, but generally, severe hypernatremia is uncommon in healthy patients with AGE. In most cases of mild hypernatremia, ORT is the preferred resuscitation method and is possibly safer than IV rehydration because ORT may induce less rapid shifts in intracellular water.¹⁵

Tieder et al.¹⁶ demonstrated that better hospital adherence to national recommendations to avoid diagnostic testing in children with AGE resulted in lower charges and equivalent outcomes. In this large, multicenter study among 27 children’s hospitals in the Pediatric Hospital Information System (PHIS) database, only 70% of the 188,000 patients received guideline-adherent care. Nonrecommended laboratory testing was common, especially in the admitted population. Electrolytes were measured in 22.1% of the ED and observation patients compared with 85% of admitted patients. Hospitals that were most guideline adherent in the ED demonstrated 50% lower charges. The authors estimate that standardizing AGE care and eliminating nonrecommended laboratory testing would decrease admissions by 45% and save more than $1 billion per year in direct medical costs.¹⁶ In a similar PHIS study, laboratory testing was strongly correlated with the percentage of children hospitalized for AGE at each hospital (r = 0.73, P < 0.001). Results were unchanged when excluding children <1 year of age (r = 0.75, P < 0.001). In contrast, the mean testing count was not correlated with return visits within 3 days for children discharged from the ED (r = 0.21, P = 0.235), nor was it correlated with hospital length of stay (r = −0.04, P = 0.804) or return visits within 7 days (r = 0.03, P = 0.862) for hospitalized children.¹² In addition, Freedman et al.¹⁷ revealed that the clinical dehydration score is independently associated with successful ED discharge without revisits, and laboratory testing does not prevent missed cases of severe dehydration.

Nonrecommended and often unnecessary laboratory testing in AGE results in IV procedures that are sometimes repeated because of abnormal values. “Shotgun testing,” or ordering a panel of labs, can result in abnormal laboratory values in healthy patients. Deyo et al.¹⁸ cite that for a panel of 12 laboratory values, there is a 46% chance of having at least 1 abnormal lab, even in healthy patients. These false-positive results can then drive further testing. In AGE, an abnormal bicarbonate may drive repeat testing to confirm normalization, but the bicarbonate may actually decrease once IV fluid therapy is initiated due to excessive chloride in isotonic fluids. Coon et al.¹⁹ have shown that seemingly innocuous testing or screening can lead to overdiagnosis, which can cause physical and psychological harm to the patient and has financial implications for the family and healthcare system. While this has not been directly investigated in pediatric AGE, it has been studied in common pediatric illnesses, including pneumonia and urinary tract infections.^20,21 For children, venipuncture and IV placements are often the most distressful components of a hospital visit and can affect future healthcare encounters, making children anxious and distrustful of the healthcare system.^22,23

Electrolyte panels may be useful in assessing children with severe dehydration (scores of 5-8 on the Clinical Dehydration Scale (CDS) or more than 10% weight loss) or in complicated cases of AGE (those that do not meet the criteria of age >6 months, nontoxic appearance, no comorbidities, no hematochezia, and diarrhea <7 days) to guide IV fluid management and correct markedly abnormal electrolytes.¹⁴

Electrolyte panels may also rarely uncover disease processes, such as new-onset diabetes, hemolytic uremic syndrome, adrenal insufficiency, or inborn errors of metabolism, allowing for early diagnosis and preventing adverse outcomes. Suspicion to investigate such entities should arise during a thorough history and PE instead of routinely screening all children with symptoms of AGE. One should also have a higher level of concern for other disease processes when clinical recovery does not occur within the expected amount of time; symptoms usually resolve within 2 to 3 days but sometimes will last up to a week.

• Perform a thorough history and PE to diagnose AGE.⁸
• Clinical assessment of dehydration should be performed upon initial presentation and repeatedly with vital signs throughout the stay using a validated CDS to classify the patient’s initial dehydration severity and monitor improvement. Obtain a current patient weight and compare with previously recorded weights, if available.^25,26
• Laboratory testing in patients with AGE should not be performed unless a patient is classified as severely dehydrated, is toxic appearing, has a comorbidity that increases the likelihood of complications, or is not improving as expected.
• Rehydration via ORT is preferred to an IV in mild and moderate dehydration.¹⁵
• If initial testing is performed and indicates an expected value indicative of dehydration, do not repeat testing to demonstrate normalization as long as the child is clinically improving as expected.

Children presenting with mild-to-moderate dehydration should be treated with supportive measures in accordance with current guidelines. Electrolyte panels very rarely provide clinical information that cannot be garnered through a thorough history and PE. As in our clinical scenario, the laboratory values obtained may have led to potential harm, including overdiagnosis, painful procedures, and psychological distress. Without testing, the patient likely could have been appropriately treated with ORT and discharged from the ED.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Disclosure

The authors have nothing to disclose.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but that may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Acute gastroenteritis (AGE) remains a substantial cause of childhood illness and is 1 of the top 10 reasons for pediatric hospitalization nationwide. In the United States, AGE is responsible for 10% of hospital admissions and approximately 300 deaths annually.¹ The American Academy of Pediatrics (AAP) and other organizations have emphasized supportive care in the management of AGE. Routine diagnostic testing has been discouraged in national guidelines except in cases of severe dehydration or an otherwise complicated course. Despite AGE guidelines, diagnostic laboratory tests are still widely used even though they have been shown to be poor predictors of dehydration. Studies have shown that high test utilization in various pediatric disease processes often influences the decision for hospitalization without improvement in patient outcome. In children with AGE, the initial and follow-up laboratory tests may not only be something that we do for no reason, but something that is associated with more risk than benefit.

An 18-month-old healthy male is brought to the emergency department (ED) with a chief complaint of 2 days of nonbloody, nonbilious emesis and watery diarrhea. He has decreased energy but smiles and plays for a few minutes. He has had decreased wet diapers. His exam is notable for mild tachycardia, mildly dry lips, and capillary refill of 3 seconds. A serum electrolyte panel is normal except for a sodium of 134 mEq/L, a bicarbonate of 16 mEq/L, and an anion gap of 18, which are flagged as abnormal by the electronic medical record. These results prompt intravenous (IV) access, a normal saline bolus, and admission on maintenance fluids overnight. The next morning, his electrolyte panel is repeated, and his sodium is 140 mEq/L and bicarbonate is 15 mEq/L. He is now drinking well with no further episodes of emesis, so he is discharged home.

Many physicians across the United States continue to order electrolytes in AGE as a way to avoid missing severe dehydration, severe electrolyte abnormalities, or rare diagnoses, such as adrenal insufficiency or new-onset diabetes, in a child. Previous studies have revealed that bicarbonate and blood urea nitrogen (BUN) may be helpful predictors of severe dehydration. A retrospective study of 168 patients by Yilmaz et al.² showed that BUN and bicarbonate strongly correlated with dehydration severity (P < 0.00001 and P = 0.01, respectively). A 97-patient prospective study by Vega and Avner³ showed that bicarbonate <17 can help in predicting percent body weight loss (PBWL) (sensitivity of 77% for PBWL 6-10 and 94% for PBWL >10).

In AGE, obtaining laboratory data is often considered to be the more conservative approach. Some attribute this to the medical education and legal system rewarding the uncovering of rare diagnoses,⁴ while others believe physicians obtain laboratory data to avoid missing severe electrolyte disorders. One author notes, “physicians who are anxious about a patient’s problem may be tempted to do something—anything—decisive in order to diminish their own anxiety.”⁵ Severe electrolyte derangements are common in developing countries⁶ but less so in the United States. A prospective pediatric dehydration study over 1 year in the United States demonstrated rates of 6% and 3% of hypo- and hypernatremia, respectively (n = 182). Only 1 patient had a sodium level >160, and this patient had an underlying genetic syndrome, and none had hyponatremia <130. Hypoglycemia was the most common electrolyte abnormality, which was present in 9.8% of patients. Electrolyte results changed management in 10.4% of patients.⁷

In AGE with or without dehydration, guidelines from the AAP and other international organizations emphasize supportive care in the management of AGE and discourage routine diagnostic testing.^8-10 Yet, there continues to be wide variation in AGE management.^11-13 Most AGE cases presenting to an outpatient setting or ED are uncomplicated: age >6 months, nontoxic appearance, no comorbidities, no hematochezia, diarrhea <7 days, and mild-to-moderate dehydration.

Steiner et al.¹⁴ performed a systematic meta-analysis of the precision and accuracy of symptoms, signs, and laboratory tests for evaluating dehydration in children. They concluded that a standardized clinical assessment based on physical exam (PE) findings more accurately classifies the degree of dehydration than laboratory testing. Steiner et al¹⁴ specifically analyzed the works by Yilmaz et al.² and Vega and Avner,³ and determined that the positive likelihood ratios for >5% dehydration resulting from a BUN >45 or bicarbonate <17 were too small or had confidence intervals that were too wide to be clinically helpful alone. Therefore, Steiner et al.¹⁴ recommended that laboratory testing should not be considered definitive for dehydration.

Vega and Avner³ found that electrolyte testing is less helpful in distinguishing between <5% (mild) and 5% to 10% (moderate) dehydration compared to PBWL. Because both mild and moderate dehydration respond equally well to oral rehydration therapy (ORT),⁸ electrolyte testing is not helpful in managing these categories. Many studies have excluded children with hypernatremia, but generally, severe hypernatremia is uncommon in healthy patients with AGE. In most cases of mild hypernatremia, ORT is the preferred resuscitation method and is possibly safer than IV rehydration because ORT may induce less rapid shifts in intracellular water.¹⁵

Tieder et al.¹⁶ demonstrated that better hospital adherence to national recommendations to avoid diagnostic testing in children with AGE resulted in lower charges and equivalent outcomes. In this large, multicenter study among 27 children’s hospitals in the Pediatric Hospital Information System (PHIS) database, only 70% of the 188,000 patients received guideline-adherent care. Nonrecommended laboratory testing was common, especially in the admitted population. Electrolytes were measured in 22.1% of the ED and observation patients compared with 85% of admitted patients. Hospitals that were most guideline adherent in the ED demonstrated 50% lower charges. The authors estimate that standardizing AGE care and eliminating nonrecommended laboratory testing would decrease admissions by 45% and save more than $1 billion per year in direct medical costs.¹⁶ In a similar PHIS study, laboratory testing was strongly correlated with the percentage of children hospitalized for AGE at each hospital (r = 0.73, P < 0.001). Results were unchanged when excluding children <1 year of age (r = 0.75, P < 0.001). In contrast, the mean testing count was not correlated with return visits within 3 days for children discharged from the ED (r = 0.21, P = 0.235), nor was it correlated with hospital length of stay (r = −0.04, P = 0.804) or return visits within 7 days (r = 0.03, P = 0.862) for hospitalized children.¹² In addition, Freedman et al.¹⁷ revealed that the clinical dehydration score is independently associated with successful ED discharge without revisits, and laboratory testing does not prevent missed cases of severe dehydration.

Nonrecommended and often unnecessary laboratory testing in AGE results in IV procedures that are sometimes repeated because of abnormal values. “Shotgun testing,” or ordering a panel of labs, can result in abnormal laboratory values in healthy patients. Deyo et al.¹⁸ cite that for a panel of 12 laboratory values, there is a 46% chance of having at least 1 abnormal lab, even in healthy patients. These false-positive results can then drive further testing. In AGE, an abnormal bicarbonate may drive repeat testing to confirm normalization, but the bicarbonate may actually decrease once IV fluid therapy is initiated due to excessive chloride in isotonic fluids. Coon et al.¹⁹ have shown that seemingly innocuous testing or screening can lead to overdiagnosis, which can cause physical and psychological harm to the patient and has financial implications for the family and healthcare system. While this has not been directly investigated in pediatric AGE, it has been studied in common pediatric illnesses, including pneumonia and urinary tract infections.^20,21 For children, venipuncture and IV placements are often the most distressful components of a hospital visit and can affect future healthcare encounters, making children anxious and distrustful of the healthcare system.^22,23

Electrolyte panels may be useful in assessing children with severe dehydration (scores of 5-8 on the Clinical Dehydration Scale (CDS) or more than 10% weight loss) or in complicated cases of AGE (those that do not meet the criteria of age >6 months, nontoxic appearance, no comorbidities, no hematochezia, and diarrhea <7 days) to guide IV fluid management and correct markedly abnormal electrolytes.¹⁴

Electrolyte panels may also rarely uncover disease processes, such as new-onset diabetes, hemolytic uremic syndrome, adrenal insufficiency, or inborn errors of metabolism, allowing for early diagnosis and preventing adverse outcomes. Suspicion to investigate such entities should arise during a thorough history and PE instead of routinely screening all children with symptoms of AGE. One should also have a higher level of concern for other disease processes when clinical recovery does not occur within the expected amount of time; symptoms usually resolve within 2 to 3 days but sometimes will last up to a week.

• Perform a thorough history and PE to diagnose AGE.⁸
• Clinical assessment of dehydration should be performed upon initial presentation and repeatedly with vital signs throughout the stay using a validated CDS to classify the patient’s initial dehydration severity and monitor improvement. Obtain a current patient weight and compare with previously recorded weights, if available.^25,26
• Laboratory testing in patients with AGE should not be performed unless a patient is classified as severely dehydrated, is toxic appearing, has a comorbidity that increases the likelihood of complications, or is not improving as expected.
• Rehydration via ORT is preferred to an IV in mild and moderate dehydration.¹⁵
• If initial testing is performed and indicates an expected value indicative of dehydration, do not repeat testing to demonstrate normalization as long as the child is clinically improving as expected.

Children presenting with mild-to-moderate dehydration should be treated with supportive measures in accordance with current guidelines. Electrolyte panels very rarely provide clinical information that cannot be garnered through a thorough history and PE. As in our clinical scenario, the laboratory values obtained may have led to potential harm, including overdiagnosis, painful procedures, and psychological distress. Without testing, the patient likely could have been appropriately treated with ORT and discharged from the ED.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Disclosure

The authors have nothing to disclose.

The “Things We Do for No Reason” (TWDFNR) series reviews practices that have become common parts of hospital care but that may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent “black and white” conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion. https://www.choosingwisely.org/

Acute gastroenteritis (AGE) remains a substantial cause of childhood illness and is 1 of the top 10 reasons for pediatric hospitalization nationwide. In the United States, AGE is responsible for 10% of hospital admissions and approximately 300 deaths annually.¹ The American Academy of Pediatrics (AAP) and other organizations have emphasized supportive care in the management of AGE. Routine diagnostic testing has been discouraged in national guidelines except in cases of severe dehydration or an otherwise complicated course. Despite AGE guidelines, diagnostic laboratory tests are still widely used even though they have been shown to be poor predictors of dehydration. Studies have shown that high test utilization in various pediatric disease processes often influences the decision for hospitalization without improvement in patient outcome. In children with AGE, the initial and follow-up laboratory tests may not only be something that we do for no reason, but something that is associated with more risk than benefit.

An 18-month-old healthy male is brought to the emergency department (ED) with a chief complaint of 2 days of nonbloody, nonbilious emesis and watery diarrhea. He has decreased energy but smiles and plays for a few minutes. He has had decreased wet diapers. His exam is notable for mild tachycardia, mildly dry lips, and capillary refill of 3 seconds. A serum electrolyte panel is normal except for a sodium of 134 mEq/L, a bicarbonate of 16 mEq/L, and an anion gap of 18, which are flagged as abnormal by the electronic medical record. These results prompt intravenous (IV) access, a normal saline bolus, and admission on maintenance fluids overnight. The next morning, his electrolyte panel is repeated, and his sodium is 140 mEq/L and bicarbonate is 15 mEq/L. He is now drinking well with no further episodes of emesis, so he is discharged home.

Many physicians across the United States continue to order electrolytes in AGE as a way to avoid missing severe dehydration, severe electrolyte abnormalities, or rare diagnoses, such as adrenal insufficiency or new-onset diabetes, in a child. Previous studies have revealed that bicarbonate and blood urea nitrogen (BUN) may be helpful predictors of severe dehydration. A retrospective study of 168 patients by Yilmaz et al.² showed that BUN and bicarbonate strongly correlated with dehydration severity (P < 0.00001 and P = 0.01, respectively). A 97-patient prospective study by Vega and Avner³ showed that bicarbonate <17 can help in predicting percent body weight loss (PBWL) (sensitivity of 77% for PBWL 6-10 and 94% for PBWL >10).

In AGE, obtaining laboratory data is often considered to be the more conservative approach. Some attribute this to the medical education and legal system rewarding the uncovering of rare diagnoses,⁴ while others believe physicians obtain laboratory data to avoid missing severe electrolyte disorders. One author notes, “physicians who are anxious about a patient’s problem may be tempted to do something—anything—decisive in order to diminish their own anxiety.”⁵ Severe electrolyte derangements are common in developing countries⁶ but less so in the United States. A prospective pediatric dehydration study over 1 year in the United States demonstrated rates of 6% and 3% of hypo- and hypernatremia, respectively (n = 182). Only 1 patient had a sodium level >160, and this patient had an underlying genetic syndrome, and none had hyponatremia <130. Hypoglycemia was the most common electrolyte abnormality, which was present in 9.8% of patients. Electrolyte results changed management in 10.4% of patients.⁷

In AGE with or without dehydration, guidelines from the AAP and other international organizations emphasize supportive care in the management of AGE and discourage routine diagnostic testing.^8-10 Yet, there continues to be wide variation in AGE management.^11-13 Most AGE cases presenting to an outpatient setting or ED are uncomplicated: age >6 months, nontoxic appearance, no comorbidities, no hematochezia, diarrhea <7 days, and mild-to-moderate dehydration.

Steiner et al.¹⁴ performed a systematic meta-analysis of the precision and accuracy of symptoms, signs, and laboratory tests for evaluating dehydration in children. They concluded that a standardized clinical assessment based on physical exam (PE) findings more accurately classifies the degree of dehydration than laboratory testing. Steiner et al¹⁴ specifically analyzed the works by Yilmaz et al.² and Vega and Avner,³ and determined that the positive likelihood ratios for >5% dehydration resulting from a BUN >45 or bicarbonate <17 were too small or had confidence intervals that were too wide to be clinically helpful alone. Therefore, Steiner et al.¹⁴ recommended that laboratory testing should not be considered definitive for dehydration.

Vega and Avner³ found that electrolyte testing is less helpful in distinguishing between <5% (mild) and 5% to 10% (moderate) dehydration compared to PBWL. Because both mild and moderate dehydration respond equally well to oral rehydration therapy (ORT),⁸ electrolyte testing is not helpful in managing these categories. Many studies have excluded children with hypernatremia, but generally, severe hypernatremia is uncommon in healthy patients with AGE. In most cases of mild hypernatremia, ORT is the preferred resuscitation method and is possibly safer than IV rehydration because ORT may induce less rapid shifts in intracellular water.¹⁵

Tieder et al.¹⁶ demonstrated that better hospital adherence to national recommendations to avoid diagnostic testing in children with AGE resulted in lower charges and equivalent outcomes. In this large, multicenter study among 27 children’s hospitals in the Pediatric Hospital Information System (PHIS) database, only 70% of the 188,000 patients received guideline-adherent care. Nonrecommended laboratory testing was common, especially in the admitted population. Electrolytes were measured in 22.1% of the ED and observation patients compared with 85% of admitted patients. Hospitals that were most guideline adherent in the ED demonstrated 50% lower charges. The authors estimate that standardizing AGE care and eliminating nonrecommended laboratory testing would decrease admissions by 45% and save more than $1 billion per year in direct medical costs.¹⁶ In a similar PHIS study, laboratory testing was strongly correlated with the percentage of children hospitalized for AGE at each hospital (r = 0.73, P < 0.001). Results were unchanged when excluding children <1 year of age (r = 0.75, P < 0.001). In contrast, the mean testing count was not correlated with return visits within 3 days for children discharged from the ED (r = 0.21, P = 0.235), nor was it correlated with hospital length of stay (r = −0.04, P = 0.804) or return visits within 7 days (r = 0.03, P = 0.862) for hospitalized children.¹² In addition, Freedman et al.¹⁷ revealed that the clinical dehydration score is independently associated with successful ED discharge without revisits, and laboratory testing does not prevent missed cases of severe dehydration.

Nonrecommended and often unnecessary laboratory testing in AGE results in IV procedures that are sometimes repeated because of abnormal values. “Shotgun testing,” or ordering a panel of labs, can result in abnormal laboratory values in healthy patients. Deyo et al.¹⁸ cite that for a panel of 12 laboratory values, there is a 46% chance of having at least 1 abnormal lab, even in healthy patients. These false-positive results can then drive further testing. In AGE, an abnormal bicarbonate may drive repeat testing to confirm normalization, but the bicarbonate may actually decrease once IV fluid therapy is initiated due to excessive chloride in isotonic fluids. Coon et al.¹⁹ have shown that seemingly innocuous testing or screening can lead to overdiagnosis, which can cause physical and psychological harm to the patient and has financial implications for the family and healthcare system. While this has not been directly investigated in pediatric AGE, it has been studied in common pediatric illnesses, including pneumonia and urinary tract infections.^20,21 For children, venipuncture and IV placements are often the most distressful components of a hospital visit and can affect future healthcare encounters, making children anxious and distrustful of the healthcare system.^22,23

Electrolyte panels may be useful in assessing children with severe dehydration (scores of 5-8 on the Clinical Dehydration Scale (CDS) or more than 10% weight loss) or in complicated cases of AGE (those that do not meet the criteria of age >6 months, nontoxic appearance, no comorbidities, no hematochezia, and diarrhea <7 days) to guide IV fluid management and correct markedly abnormal electrolytes.¹⁴

Electrolyte panels may also rarely uncover disease processes, such as new-onset diabetes, hemolytic uremic syndrome, adrenal insufficiency, or inborn errors of metabolism, allowing for early diagnosis and preventing adverse outcomes. Suspicion to investigate such entities should arise during a thorough history and PE instead of routinely screening all children with symptoms of AGE. One should also have a higher level of concern for other disease processes when clinical recovery does not occur within the expected amount of time; symptoms usually resolve within 2 to 3 days but sometimes will last up to a week.

• Perform a thorough history and PE to diagnose AGE.⁸
• Clinical assessment of dehydration should be performed upon initial presentation and repeatedly with vital signs throughout the stay using a validated CDS to classify the patient’s initial dehydration severity and monitor improvement. Obtain a current patient weight and compare with previously recorded weights, if available.^25,26
• Laboratory testing in patients with AGE should not be performed unless a patient is classified as severely dehydrated, is toxic appearing, has a comorbidity that increases the likelihood of complications, or is not improving as expected.
• Rehydration via ORT is preferred to an IV in mild and moderate dehydration.¹⁵
• If initial testing is performed and indicates an expected value indicative of dehydration, do not repeat testing to demonstrate normalization as long as the child is clinically improving as expected.

Children presenting with mild-to-moderate dehydration should be treated with supportive measures in accordance with current guidelines. Electrolyte panels very rarely provide clinical information that cannot be garnered through a thorough history and PE. As in our clinical scenario, the laboratory values obtained may have led to potential harm, including overdiagnosis, painful procedures, and psychological distress. Without testing, the patient likely could have been appropriately treated with ORT and discharged from the ED.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason?” Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason” topics by emailing [email protected].

Disclosure

The authors have nothing to disclose.

Physicians face growing pressure to reduce their use of “low value” care—services that provide either little to no benefit, little benefit relative to cost, or outsized potential harm compared to benefit. One emerging policy solution for deterring such services is to financially penalize physicians who prescribe them.^1,2

Physicians’ willingness to support such policies may depend on who they believe benefits from reductions in low-value care. In previous studies of cancer screening, the more that primary care physicians felt that the money saved from cost-containment efforts went to insurance company profits rather than to patients, the less willing they were to use less expensive cancer screening approaches.³

Similarly, physicians may be more likely to support financial penalty policies if they perceive that the benefits from reducing low-value care accrue to patients (eg, lower out-of-pocket costs) rather than insurers or hospitals (eg, profits and salaries of their leaders). If present, such perceptions could inform incentive design. We explored the hypothesis that support of financial penalties for low-value care would be associated with where physicians thought the money goes.

Study Sample

By using a panel of internists maintained by the American College of Physicians, we conducted a randomized, web-based survey among 484 physicians who were either internal medicine residents or internal medicine physicians practicing hospital medicine.

Survey Instrument

Respondents used a 5-point scale (“strongly disagree” to “strongly agree”) to indicate their agreement with a policy that financially penalizes physicians for prescribing services that provide few benefits to patients. Respondents were asked to simultaneously consider the following hospital medicine services, deemed to be low value based on medical evidence and consensus guidelines⁴: (1) placing, and leaving in, urinary catheters for urine output monitoring in noncritically ill patients, (2) ordering continuous telemetry monitoring for nonintensive care unit patients without a protocol governing continuation, and (3) prescribing stress ulcer prophylaxis for medical patients not at a high risk for gastrointestinal complications. Policy support was defined as “somewhat” or “strongly” agreeing with the policy. As part of another study of this physician cohort, this question varied in how the harm of low-value services was framed: either as harm to patients, to society, or to hospitals and insurers as institutions. Respondent characteristics were balanced across survey versions, and for the current analysis, we pooled responses across all versions.

All other questions in the survey, described in detail elsewhere,⁵ were identical for all respondents. For this analysis, we focused on a question that asked physicians to assume that reducing these services saves money without harming the quality of care and to rate on a 4-point scale (“none” to “a lot”) how much of the money saved would ultimately go to the following 6 nonmutually exclusive areas: (a) other healthcare services for patients, (b) reduced charges to patients’ employers or insurers, (c) reduced out-of-pocket costs for patients, (d) salaries and bonuses for physicians, (e) salaries and profits for insurance companies and their leaders, and (f) salaries and profits for hospitals and/or health systems and their leaders.

Based on the positive correlation identified between the first 4 items (a to d) and negative correlation with the other 2 items (e and f), we reverse-coded the latter 2 and summed all 6 into a single-outcome scale, effectively representing the degree to which the money saved from reducing low-value services accrues generally to patients or physicians instead of to hospitals, insurance companies, and their leaders. The Cronbach alpha for the scale was 0.74, indicating acceptable reliability. Based on scale responses, we dichotomized respondents at the median into those who believe that the money saved from reducing low-value services would accrue as benefits to patients or physicians and those who believe benefits accrue to insurance companies or hospitals and/or health systems and their leaders. The protocol was exempted by the University of Pennsylvania Institutional Review Board.

Statistical Analysis

We used a χ² test and multivariable logistic regression analysis to evaluate the association between policy support and physician beliefs about who benefits from reductions in low-value care. A χ² test and a Kruskal-Wallis test were also used to evaluate the association between other respondent characteristics and beliefs about who benefits from reductions in low-value care. Analyses were performed by using Stata version 14.1 (StataCorp, College Station, TX). Tests of significance were 2-tailed at an alpha of .05.

Compared with nonrespondents, the 187 physicians who responded (39% response rate) were more likely to be female (30% vs 26%, P = 0.001), older (mean age 41 vs 36 years old, P < 0.001), and practicing clinicians rather than internal medicine residents (87% vs 69%, P < 0.001). Twenty-one percent reported that their personal compensation was tied to cost incentives.

Overall, respondents believed that more of any money saved from reducing low-value services would go to profits and leadership salaries for insurance companies and hospitals and/or health systems rather than to patients (panel A of Figure). Few respondents felt that the money saved would ultimately go toward physician compensation.

Despite ongoing efforts to highlight how reducing low-value care benefits patients, physicians in our sample did not believe that much of the money saved would benefit patients.

This result may reflect that while some care patterns are considered low value because they provide little benefit at a high cost, others yield potential harm, regardless of cost. For example, limiting stress ulcer prophylaxis largely aims to avoid clinical harm (eg, adverse drug effects and nosocomial infections). Limiting telemetric monitoring largely aims to reduce costly care that provides only limited benefit. Therefore, the nature of potential benefit to patients is very different—improved clinical outcomes in the former and potential cost savings in the latter. Future studies could separately assess physician attitudes about these 2 different definitions of low-value services.

Our study also demonstrates that the more physicians believe that much of any money saved goes to the profits and salaries of insurance companies, hospitals and/or health systems, and their leaders rather than to patients, the less likely they are to support policies financially penalizing physicians for prescribing low-value services.

Our study does not address why physicians have the beliefs that they have, but a likely explanation, at least in part, is that financial flows in healthcare are complex and tangled. Indeed, a clear understanding of who actually benefits is so hard to determine that these stated beliefs may really derive from views of power or justice rather than from some understanding of funds flow. Whether or not ideological attitudes underlie these expressed beliefs, policymakers and healthcare institutions might be advised to increase transparency about how cost savings are realized and whom they benefit.

Our analysis has limitations. Although it provides insight into where physicians believe relative amounts of money saved go with respect to 6 common options, the study did not include an exhaustive list of possibilities. The response rate also limits the representativeness of our results. Additionally, the study design prevents conclusions about causality; we cannot determine whether the belief that savings go to insurance companies and their executives is what reduces physicians’ enthusiasm for penalties, whether the causal association is in the opposite direction, or whether the 2 factors are linked in another way.

Nonetheless, our findings are consistent with a sense of healthcare justice in which physicians support penalties imposed on themselves only if the resulting benefits accrue to patients rather than to corporate or organizational interests. Effective physician penalties will likely need to address the belief that insurers and provider organizations stand to gain more than patients when low-value care services are reduced.

Disclosure 

Drs. Liao, Schapira, Mitra, and Weissman have no conflicts to disclose. Dr. Navathe serves as advisor to Navvis and Company, Navigant Inc., Lynx Medical, Indegene Inc., and Sutherland Global Services and receives an honorarium from Elsevier Press, none of which have relationship to this manuscript. Dr. Asch is a partner and partial owner of VAL Health, which has no relationship to this manuscript.

Funding

This work was supported by The Leonard Davis Institute of Health Economics at the University of Pennsylvania, which had no role in the study design, data collection, analysis, or interpretation of results.

Physicians face growing pressure to reduce their use of “low value” care—services that provide either little to no benefit, little benefit relative to cost, or outsized potential harm compared to benefit. One emerging policy solution for deterring such services is to financially penalize physicians who prescribe them.^1,2

Physicians’ willingness to support such policies may depend on who they believe benefits from reductions in low-value care. In previous studies of cancer screening, the more that primary care physicians felt that the money saved from cost-containment efforts went to insurance company profits rather than to patients, the less willing they were to use less expensive cancer screening approaches.³

Similarly, physicians may be more likely to support financial penalty policies if they perceive that the benefits from reducing low-value care accrue to patients (eg, lower out-of-pocket costs) rather than insurers or hospitals (eg, profits and salaries of their leaders). If present, such perceptions could inform incentive design. We explored the hypothesis that support of financial penalties for low-value care would be associated with where physicians thought the money goes.

Study Sample

By using a panel of internists maintained by the American College of Physicians, we conducted a randomized, web-based survey among 484 physicians who were either internal medicine residents or internal medicine physicians practicing hospital medicine.

Survey Instrument

Respondents used a 5-point scale (“strongly disagree” to “strongly agree”) to indicate their agreement with a policy that financially penalizes physicians for prescribing services that provide few benefits to patients. Respondents were asked to simultaneously consider the following hospital medicine services, deemed to be low value based on medical evidence and consensus guidelines⁴: (1) placing, and leaving in, urinary catheters for urine output monitoring in noncritically ill patients, (2) ordering continuous telemetry monitoring for nonintensive care unit patients without a protocol governing continuation, and (3) prescribing stress ulcer prophylaxis for medical patients not at a high risk for gastrointestinal complications. Policy support was defined as “somewhat” or “strongly” agreeing with the policy. As part of another study of this physician cohort, this question varied in how the harm of low-value services was framed: either as harm to patients, to society, or to hospitals and insurers as institutions. Respondent characteristics were balanced across survey versions, and for the current analysis, we pooled responses across all versions.

All other questions in the survey, described in detail elsewhere,⁵ were identical for all respondents. For this analysis, we focused on a question that asked physicians to assume that reducing these services saves money without harming the quality of care and to rate on a 4-point scale (“none” to “a lot”) how much of the money saved would ultimately go to the following 6 nonmutually exclusive areas: (a) other healthcare services for patients, (b) reduced charges to patients’ employers or insurers, (c) reduced out-of-pocket costs for patients, (d) salaries and bonuses for physicians, (e) salaries and profits for insurance companies and their leaders, and (f) salaries and profits for hospitals and/or health systems and their leaders.

Based on the positive correlation identified between the first 4 items (a to d) and negative correlation with the other 2 items (e and f), we reverse-coded the latter 2 and summed all 6 into a single-outcome scale, effectively representing the degree to which the money saved from reducing low-value services accrues generally to patients or physicians instead of to hospitals, insurance companies, and their leaders. The Cronbach alpha for the scale was 0.74, indicating acceptable reliability. Based on scale responses, we dichotomized respondents at the median into those who believe that the money saved from reducing low-value services would accrue as benefits to patients or physicians and those who believe benefits accrue to insurance companies or hospitals and/or health systems and their leaders. The protocol was exempted by the University of Pennsylvania Institutional Review Board.

Statistical Analysis

We used a χ² test and multivariable logistic regression analysis to evaluate the association between policy support and physician beliefs about who benefits from reductions in low-value care. A χ² test and a Kruskal-Wallis test were also used to evaluate the association between other respondent characteristics and beliefs about who benefits from reductions in low-value care. Analyses were performed by using Stata version 14.1 (StataCorp, College Station, TX). Tests of significance were 2-tailed at an alpha of .05.

Compared with nonrespondents, the 187 physicians who responded (39% response rate) were more likely to be female (30% vs 26%, P = 0.001), older (mean age 41 vs 36 years old, P < 0.001), and practicing clinicians rather than internal medicine residents (87% vs 69%, P < 0.001). Twenty-one percent reported that their personal compensation was tied to cost incentives.

Overall, respondents believed that more of any money saved from reducing low-value services would go to profits and leadership salaries for insurance companies and hospitals and/or health systems rather than to patients (panel A of Figure). Few respondents felt that the money saved would ultimately go toward physician compensation.

Despite ongoing efforts to highlight how reducing low-value care benefits patients, physicians in our sample did not believe that much of the money saved would benefit patients.

This result may reflect that while some care patterns are considered low value because they provide little benefit at a high cost, others yield potential harm, regardless of cost. For example, limiting stress ulcer prophylaxis largely aims to avoid clinical harm (eg, adverse drug effects and nosocomial infections). Limiting telemetric monitoring largely aims to reduce costly care that provides only limited benefit. Therefore, the nature of potential benefit to patients is very different—improved clinical outcomes in the former and potential cost savings in the latter. Future studies could separately assess physician attitudes about these 2 different definitions of low-value services.

Our study also demonstrates that the more physicians believe that much of any money saved goes to the profits and salaries of insurance companies, hospitals and/or health systems, and their leaders rather than to patients, the less likely they are to support policies financially penalizing physicians for prescribing low-value services.

Our study does not address why physicians have the beliefs that they have, but a likely explanation, at least in part, is that financial flows in healthcare are complex and tangled. Indeed, a clear understanding of who actually benefits is so hard to determine that these stated beliefs may really derive from views of power or justice rather than from some understanding of funds flow. Whether or not ideological attitudes underlie these expressed beliefs, policymakers and healthcare institutions might be advised to increase transparency about how cost savings are realized and whom they benefit.

Our analysis has limitations. Although it provides insight into where physicians believe relative amounts of money saved go with respect to 6 common options, the study did not include an exhaustive list of possibilities. The response rate also limits the representativeness of our results. Additionally, the study design prevents conclusions about causality; we cannot determine whether the belief that savings go to insurance companies and their executives is what reduces physicians’ enthusiasm for penalties, whether the causal association is in the opposite direction, or whether the 2 factors are linked in another way.

Nonetheless, our findings are consistent with a sense of healthcare justice in which physicians support penalties imposed on themselves only if the resulting benefits accrue to patients rather than to corporate or organizational interests. Effective physician penalties will likely need to address the belief that insurers and provider organizations stand to gain more than patients when low-value care services are reduced.

Disclosure 

Drs. Liao, Schapira, Mitra, and Weissman have no conflicts to disclose. Dr. Navathe serves as advisor to Navvis and Company, Navigant Inc., Lynx Medical, Indegene Inc., and Sutherland Global Services and receives an honorarium from Elsevier Press, none of which have relationship to this manuscript. Dr. Asch is a partner and partial owner of VAL Health, which has no relationship to this manuscript.

Funding

This work was supported by The Leonard Davis Institute of Health Economics at the University of Pennsylvania, which had no role in the study design, data collection, analysis, or interpretation of results.

Physicians face growing pressure to reduce their use of “low value” care—services that provide either little to no benefit, little benefit relative to cost, or outsized potential harm compared to benefit. One emerging policy solution for deterring such services is to financially penalize physicians who prescribe them.^1,2

Physicians’ willingness to support such policies may depend on who they believe benefits from reductions in low-value care. In previous studies of cancer screening, the more that primary care physicians felt that the money saved from cost-containment efforts went to insurance company profits rather than to patients, the less willing they were to use less expensive cancer screening approaches.³

Similarly, physicians may be more likely to support financial penalty policies if they perceive that the benefits from reducing low-value care accrue to patients (eg, lower out-of-pocket costs) rather than insurers or hospitals (eg, profits and salaries of their leaders). If present, such perceptions could inform incentive design. We explored the hypothesis that support of financial penalties for low-value care would be associated with where physicians thought the money goes.

Study Sample

By using a panel of internists maintained by the American College of Physicians, we conducted a randomized, web-based survey among 484 physicians who were either internal medicine residents or internal medicine physicians practicing hospital medicine.

Survey Instrument

Respondents used a 5-point scale (“strongly disagree” to “strongly agree”) to indicate their agreement with a policy that financially penalizes physicians for prescribing services that provide few benefits to patients. Respondents were asked to simultaneously consider the following hospital medicine services, deemed to be low value based on medical evidence and consensus guidelines⁴: (1) placing, and leaving in, urinary catheters for urine output monitoring in noncritically ill patients, (2) ordering continuous telemetry monitoring for nonintensive care unit patients without a protocol governing continuation, and (3) prescribing stress ulcer prophylaxis for medical patients not at a high risk for gastrointestinal complications. Policy support was defined as “somewhat” or “strongly” agreeing with the policy. As part of another study of this physician cohort, this question varied in how the harm of low-value services was framed: either as harm to patients, to society, or to hospitals and insurers as institutions. Respondent characteristics were balanced across survey versions, and for the current analysis, we pooled responses across all versions.

All other questions in the survey, described in detail elsewhere,⁵ were identical for all respondents. For this analysis, we focused on a question that asked physicians to assume that reducing these services saves money without harming the quality of care and to rate on a 4-point scale (“none” to “a lot”) how much of the money saved would ultimately go to the following 6 nonmutually exclusive areas: (a) other healthcare services for patients, (b) reduced charges to patients’ employers or insurers, (c) reduced out-of-pocket costs for patients, (d) salaries and bonuses for physicians, (e) salaries and profits for insurance companies and their leaders, and (f) salaries and profits for hospitals and/or health systems and their leaders.

Based on the positive correlation identified between the first 4 items (a to d) and negative correlation with the other 2 items (e and f), we reverse-coded the latter 2 and summed all 6 into a single-outcome scale, effectively representing the degree to which the money saved from reducing low-value services accrues generally to patients or physicians instead of to hospitals, insurance companies, and their leaders. The Cronbach alpha for the scale was 0.74, indicating acceptable reliability. Based on scale responses, we dichotomized respondents at the median into those who believe that the money saved from reducing low-value services would accrue as benefits to patients or physicians and those who believe benefits accrue to insurance companies or hospitals and/or health systems and their leaders. The protocol was exempted by the University of Pennsylvania Institutional Review Board.

Statistical Analysis

We used a χ² test and multivariable logistic regression analysis to evaluate the association between policy support and physician beliefs about who benefits from reductions in low-value care. A χ² test and a Kruskal-Wallis test were also used to evaluate the association between other respondent characteristics and beliefs about who benefits from reductions in low-value care. Analyses were performed by using Stata version 14.1 (StataCorp, College Station, TX). Tests of significance were 2-tailed at an alpha of .05.

Compared with nonrespondents, the 187 physicians who responded (39% response rate) were more likely to be female (30% vs 26%, P = 0.001), older (mean age 41 vs 36 years old, P < 0.001), and practicing clinicians rather than internal medicine residents (87% vs 69%, P < 0.001). Twenty-one percent reported that their personal compensation was tied to cost incentives.

Overall, respondents believed that more of any money saved from reducing low-value services would go to profits and leadership salaries for insurance companies and hospitals and/or health systems rather than to patients (panel A of Figure). Few respondents felt that the money saved would ultimately go toward physician compensation.

Despite ongoing efforts to highlight how reducing low-value care benefits patients, physicians in our sample did not believe that much of the money saved would benefit patients.

This result may reflect that while some care patterns are considered low value because they provide little benefit at a high cost, others yield potential harm, regardless of cost. For example, limiting stress ulcer prophylaxis largely aims to avoid clinical harm (eg, adverse drug effects and nosocomial infections). Limiting telemetric monitoring largely aims to reduce costly care that provides only limited benefit. Therefore, the nature of potential benefit to patients is very different—improved clinical outcomes in the former and potential cost savings in the latter. Future studies could separately assess physician attitudes about these 2 different definitions of low-value services.

Our study also demonstrates that the more physicians believe that much of any money saved goes to the profits and salaries of insurance companies, hospitals and/or health systems, and their leaders rather than to patients, the less likely they are to support policies financially penalizing physicians for prescribing low-value services.

Our study does not address why physicians have the beliefs that they have, but a likely explanation, at least in part, is that financial flows in healthcare are complex and tangled. Indeed, a clear understanding of who actually benefits is so hard to determine that these stated beliefs may really derive from views of power or justice rather than from some understanding of funds flow. Whether or not ideological attitudes underlie these expressed beliefs, policymakers and healthcare institutions might be advised to increase transparency about how cost savings are realized and whom they benefit.

Our analysis has limitations. Although it provides insight into where physicians believe relative amounts of money saved go with respect to 6 common options, the study did not include an exhaustive list of possibilities. The response rate also limits the representativeness of our results. Additionally, the study design prevents conclusions about causality; we cannot determine whether the belief that savings go to insurance companies and their executives is what reduces physicians’ enthusiasm for penalties, whether the causal association is in the opposite direction, or whether the 2 factors are linked in another way.

Nonetheless, our findings are consistent with a sense of healthcare justice in which physicians support penalties imposed on themselves only if the resulting benefits accrue to patients rather than to corporate or organizational interests. Effective physician penalties will likely need to address the belief that insurers and provider organizations stand to gain more than patients when low-value care services are reduced.

Disclosure 

Drs. Liao, Schapira, Mitra, and Weissman have no conflicts to disclose. Dr. Navathe serves as advisor to Navvis and Company, Navigant Inc., Lynx Medical, Indegene Inc., and Sutherland Global Services and receives an honorarium from Elsevier Press, none of which have relationship to this manuscript. Dr. Asch is a partner and partial owner of VAL Health, which has no relationship to this manuscript.

Funding

This work was supported by The Leonard Davis Institute of Health Economics at the University of Pennsylvania, which had no role in the study design, data collection, analysis, or interpretation of results.

Reducing low-value care—services for which there is little to no benefit, little benefit relative to cost, or outsized potential harm compared with benefit—is an essential step toward maintaining or improving quality while lowering cost. Unfortunately, low-value services persist widelydespite professional consensus, guidelines, and national campaigns aimed to reduce them.^1-3 In turn, policy makers are beginning to consider financially penalizing physicians in order to deter low-value services.^4,5 Physician support for such penalties remains unknown. In this study, we used a randomized survey experiment to evaluate how the framing of harms from low-value care—in terms of those to patients, healthcare institutions, or society—influenced physician support of financial penalties for low-value care services.

Study Sample

By using a stratified random sample maintained by the American College of Physicians, we conducted a web-based survey among 484 physicians who were either internal medicine residents or internists practicing hospital medicine.

Instrument Design and Administration

Our study focused on 3 low-value services relevant to inpatient medicine: (1) placing, and leaving in, urinary catheters for urine output monitoring in noncritically ill patients; (2) ordering continuous telemetry monitoring for nonintensive care unit (non-ICU) patients without a protocol governing continuation; and (3) prescribing stress ulcer prophylaxis for medical patients not at a high risk for gastrointestinal (GI) complications. Although the nature and trade-offs between costs, harms, and benefits vary by individual service, all 3 are promulgated through the Choosing Wisely^® guidelines as low value based on existing data and professional consensus from the Society of Hospital Medicine.⁶

To evaluate intended behavior related to these 3 low-value services, respondents were first presented with 3 clinical vignettes focused on the care of patients hospitalized for pneumonia, congestive heart failure, and alcohol withdrawal, which were selected to reflect common inpatient medicine scenarios. Respondents were asked to use a 4-point scale (very likely to very unlikely) to estimate how likely they were to recommend various tests or treatments, including the low-value services noted above. Respondents who were “somewhat unlikely” and “very unlikely” to recommend low-value services were considered concordant with low-value care guidelines.

Following the vignettes, respondents then used a 5-point scale (strongly agree to strongly disagree) to indicate their agreement with a policy that financially penalizes physicians for prescribing each service. Support was defined as “somewhat or strongly” agreeing with the policy. Respondents were randomized to receive 1 of 3 versions of this question (supplementary Appendix).

All versions stated that, “According to research and expert opinion, certain aspects of inpatient care provide little benefit to patients” and listed the 3 low-value services noted above. The “patient harm” version also described the harm of low-value care as costs to patients and risk for clinical harms and complications. The “societal harm” version described the harms as costs to society and utilization of limited healthcare resources. The “institutional harm” version described harms as costs to hospitals and insurers.

Other survey items were adapted from existing literature^7-9 and evaluated respondent beliefs about the effectiveness of physician incentives in improving the value of care, as well as the appropriateness of including cost considerations in clinical decision-making.

The instrument was pilot tested among study team members and several independent internists affiliated with the University of Pennsylvania. After incorporating feedback into the final instrument, the web-based survey was distributed to eligible physicians via e-mail. Responses were anonymous and respondents received a $15 gift card for participation. The protocol was reviewed and deemed exempt by the University of Pennsylvania Institutional Review Board.

Statistical Analysis

Respondent characteristics (sociodemographic, intended clinical behavior, and cost control attitudes) were described by using percentages for categorical variables and medians and interquartile ranges for continuous variables. Balance in respondent characteristics across survey versions was evaluated using χ² and Kruskal-Wallis tests. Multivariable logistic regression, adjusted for characteristics in the Table, was used to evaluate the association between survey version and policy support. All tests of significance were 2-tailed with significance level alpha = 0.05. Analyses were performed using STATA version 14.1 (StataCorp LLC, College Station, TX, http://www.stata.com).

Of 484 eligible respondents, 187 (39%) completed the survey. Compared with nonrespondents, respondents were more likely to be female (30% vs 26%, P = 0.001), older (mean age 41 vs 36 years, P < 0.001), and practicing clinicians rather than internal medicine residents (87% vs 69%, P < 0.001). Physician characteristics were similar across the 3 survey versions (Table). Most respondents agreed that financial incentives for individual physicians is an effective way to improve the value of healthcare (73.3%) and that physicians should consider the costs of a test or treatment to society when making clinical decisions for patients (79.1%). The majority also felt that clinicians have a duty to offer a test or treatment to a patient if it has any chance of helping them (70.1%) and that it is inappropriate for anyone beyond the clinician and patient to decide if a test or treatment is “worth the cost” (63.6%).

To our knowledge, this study is the first to prospectively evaluate physician support of financial penalties for low-value services relevant to hospital medicine. It has 2 main findings.

First, although overall policy support was relatively low (39.6%), it varied significantly on the basis of how the harms of low-value care were framed. Support was highest in the “societal harm” version, suggesting that emphasizing these harms may increase acceptability of financial penalties among physicians and contribute to the larger effort to decrease low-value care in hospital settings. The comparatively low support for the “patient harm” version is somewhat surprising but may reflect variation in the nature of harm, benefit, and cost trade-offs for individual low-value services, as noted above, and physician belief that some low-value services do not in fact produce significant clinical harms.

For example, whereas evidence demonstrates that stress ulcer prophylaxis in non-ICU patients can harm patients through nosocomial infections and adverse drug effects,^10,11 the clinical harms of telemetry are less obvious. Telemetry’s low value derives more from its high cost relative to benefit, rather than its potential for clinical harm.⁶ The many paths to “low value” underscore the need to examine attitudes and uptake toward these services separately and may explain the wide range in concordance between intended clinical behavior and low-value care guidelines (11.8% to 78.6%).

Reinforcing policies could more effectively deter low-value care. For example, multiple forces, including Medicare payment reform and national accreditation policies,^12,13 have converged to discourage low-value use of urinary catheters in hospitalized patients. In contrast, there has been little reinforcement beyond consensus guidelines to reduce low-value use of telemetric monitoring. Given questions about whether consensus methods alone can deter low-value care beyond obvious “low hanging fruit,”¹⁴ policy makers could coordinate policies to accelerate progress within other priority areas.

Broad policies should also be paired with local initiatives to influence physician behavior. For example, health systems have begun successfully leveraging the electronic medical record and utilizing behavioral economics principles to design interventions to reduce inappropriate overuse of antibiotics for upper respiratory infections in primary care clinics.¹⁵ Organizations are also redesigning care processes in response to resource utilization imperatives under ongoing value-based care payment reform. Care redesign and behavioral interventions embedded at the point of care can both help deter low-value services in inpatient settings.

Study limitations include a relatively low response rate, which limits generalizability. However, all 3 randomized groups were similar on measured characteristics, and experimental randomization reduces the nonresponse bias concerns accompanying descriptive surveys. Additionally, although we evaluated intended clinical behavior in a national sample, our results may not reflect actual behavior among all physicians practicing hospital medicine. Future work could include assessments of actual or self-reported practices or examine additional factors, including site, years of practice, knowledge about guidelines, and other possible determinants of guideline-concordant behaviors.

Despite these limitations, our study provides important early evidence about physician support of financial penalties for low-value care relevant to hospital medicine. As policy makers design and organizational leaders implement financial incentive policies, this information can help increase their acceptability among physicians and more effectively reduce low-value care within hospitals.

Disclosure

Drs. Liao, Schapira, Mitra, and Weissman have no conflicts to disclose. Dr. Navathe serves as advisor to Navvis and Company, Navigant Inc, Lynx Medical, Indegene Inc, and Sutherland Global Services and receives an honorarium from Elsevier Press, none of which have relationship to this manuscript. Dr. Asch is a partner and part owner of VAL Health, which has no relationship to this manuscript.

Funding

This work was supported by The Leonard Davis Institute of Health Economics at the University of Pennsylvania, which had no role in the study design, data collection, analysis, or interpretation of results.

Reducing low-value care—services for which there is little to no benefit, little benefit relative to cost, or outsized potential harm compared with benefit—is an essential step toward maintaining or improving quality while lowering cost. Unfortunately, low-value services persist widelydespite professional consensus, guidelines, and national campaigns aimed to reduce them.^1-3 In turn, policy makers are beginning to consider financially penalizing physicians in order to deter low-value services.^4,5 Physician support for such penalties remains unknown. In this study, we used a randomized survey experiment to evaluate how the framing of harms from low-value care—in terms of those to patients, healthcare institutions, or society—influenced physician support of financial penalties for low-value care services.

Study Sample

By using a stratified random sample maintained by the American College of Physicians, we conducted a web-based survey among 484 physicians who were either internal medicine residents or internists practicing hospital medicine.

Instrument Design and Administration

Our study focused on 3 low-value services relevant to inpatient medicine: (1) placing, and leaving in, urinary catheters for urine output monitoring in noncritically ill patients; (2) ordering continuous telemetry monitoring for nonintensive care unit (non-ICU) patients without a protocol governing continuation; and (3) prescribing stress ulcer prophylaxis for medical patients not at a high risk for gastrointestinal (GI) complications. Although the nature and trade-offs between costs, harms, and benefits vary by individual service, all 3 are promulgated through the Choosing Wisely^® guidelines as low value based on existing data and professional consensus from the Society of Hospital Medicine.⁶

To evaluate intended behavior related to these 3 low-value services, respondents were first presented with 3 clinical vignettes focused on the care of patients hospitalized for pneumonia, congestive heart failure, and alcohol withdrawal, which were selected to reflect common inpatient medicine scenarios. Respondents were asked to use a 4-point scale (very likely to very unlikely) to estimate how likely they were to recommend various tests or treatments, including the low-value services noted above. Respondents who were “somewhat unlikely” and “very unlikely” to recommend low-value services were considered concordant with low-value care guidelines.

Following the vignettes, respondents then used a 5-point scale (strongly agree to strongly disagree) to indicate their agreement with a policy that financially penalizes physicians for prescribing each service. Support was defined as “somewhat or strongly” agreeing with the policy. Respondents were randomized to receive 1 of 3 versions of this question (supplementary Appendix).

All versions stated that, “According to research and expert opinion, certain aspects of inpatient care provide little benefit to patients” and listed the 3 low-value services noted above. The “patient harm” version also described the harm of low-value care as costs to patients and risk for clinical harms and complications. The “societal harm” version described the harms as costs to society and utilization of limited healthcare resources. The “institutional harm” version described harms as costs to hospitals and insurers.

Other survey items were adapted from existing literature^7-9 and evaluated respondent beliefs about the effectiveness of physician incentives in improving the value of care, as well as the appropriateness of including cost considerations in clinical decision-making.

The instrument was pilot tested among study team members and several independent internists affiliated with the University of Pennsylvania. After incorporating feedback into the final instrument, the web-based survey was distributed to eligible physicians via e-mail. Responses were anonymous and respondents received a $15 gift card for participation. The protocol was reviewed and deemed exempt by the University of Pennsylvania Institutional Review Board.

Statistical Analysis

Respondent characteristics (sociodemographic, intended clinical behavior, and cost control attitudes) were described by using percentages for categorical variables and medians and interquartile ranges for continuous variables. Balance in respondent characteristics across survey versions was evaluated using χ² and Kruskal-Wallis tests. Multivariable logistic regression, adjusted for characteristics in the Table, was used to evaluate the association between survey version and policy support. All tests of significance were 2-tailed with significance level alpha = 0.05. Analyses were performed using STATA version 14.1 (StataCorp LLC, College Station, TX, http://www.stata.com).

Of 484 eligible respondents, 187 (39%) completed the survey. Compared with nonrespondents, respondents were more likely to be female (30% vs 26%, P = 0.001), older (mean age 41 vs 36 years, P < 0.001), and practicing clinicians rather than internal medicine residents (87% vs 69%, P < 0.001). Physician characteristics were similar across the 3 survey versions (Table). Most respondents agreed that financial incentives for individual physicians is an effective way to improve the value of healthcare (73.3%) and that physicians should consider the costs of a test or treatment to society when making clinical decisions for patients (79.1%). The majority also felt that clinicians have a duty to offer a test or treatment to a patient if it has any chance of helping them (70.1%) and that it is inappropriate for anyone beyond the clinician and patient to decide if a test or treatment is “worth the cost” (63.6%).

To our knowledge, this study is the first to prospectively evaluate physician support of financial penalties for low-value services relevant to hospital medicine. It has 2 main findings.

First, although overall policy support was relatively low (39.6%), it varied significantly on the basis of how the harms of low-value care were framed. Support was highest in the “societal harm” version, suggesting that emphasizing these harms may increase acceptability of financial penalties among physicians and contribute to the larger effort to decrease low-value care in hospital settings. The comparatively low support for the “patient harm” version is somewhat surprising but may reflect variation in the nature of harm, benefit, and cost trade-offs for individual low-value services, as noted above, and physician belief that some low-value services do not in fact produce significant clinical harms.

For example, whereas evidence demonstrates that stress ulcer prophylaxis in non-ICU patients can harm patients through nosocomial infections and adverse drug effects,^10,11 the clinical harms of telemetry are less obvious. Telemetry’s low value derives more from its high cost relative to benefit, rather than its potential for clinical harm.⁶ The many paths to “low value” underscore the need to examine attitudes and uptake toward these services separately and may explain the wide range in concordance between intended clinical behavior and low-value care guidelines (11.8% to 78.6%).

Reinforcing policies could more effectively deter low-value care. For example, multiple forces, including Medicare payment reform and national accreditation policies,^12,13 have converged to discourage low-value use of urinary catheters in hospitalized patients. In contrast, there has been little reinforcement beyond consensus guidelines to reduce low-value use of telemetric monitoring. Given questions about whether consensus methods alone can deter low-value care beyond obvious “low hanging fruit,”¹⁴ policy makers could coordinate policies to accelerate progress within other priority areas.

Broad policies should also be paired with local initiatives to influence physician behavior. For example, health systems have begun successfully leveraging the electronic medical record and utilizing behavioral economics principles to design interventions to reduce inappropriate overuse of antibiotics for upper respiratory infections in primary care clinics.¹⁵ Organizations are also redesigning care processes in response to resource utilization imperatives under ongoing value-based care payment reform. Care redesign and behavioral interventions embedded at the point of care can both help deter low-value services in inpatient settings.

Study limitations include a relatively low response rate, which limits generalizability. However, all 3 randomized groups were similar on measured characteristics, and experimental randomization reduces the nonresponse bias concerns accompanying descriptive surveys. Additionally, although we evaluated intended clinical behavior in a national sample, our results may not reflect actual behavior among all physicians practicing hospital medicine. Future work could include assessments of actual or self-reported practices or examine additional factors, including site, years of practice, knowledge about guidelines, and other possible determinants of guideline-concordant behaviors.

Despite these limitations, our study provides important early evidence about physician support of financial penalties for low-value care relevant to hospital medicine. As policy makers design and organizational leaders implement financial incentive policies, this information can help increase their acceptability among physicians and more effectively reduce low-value care within hospitals.

Disclosure

Drs. Liao, Schapira, Mitra, and Weissman have no conflicts to disclose. Dr. Navathe serves as advisor to Navvis and Company, Navigant Inc, Lynx Medical, Indegene Inc, and Sutherland Global Services and receives an honorarium from Elsevier Press, none of which have relationship to this manuscript. Dr. Asch is a partner and part owner of VAL Health, which has no relationship to this manuscript.

Funding

This work was supported by The Leonard Davis Institute of Health Economics at the University of Pennsylvania, which had no role in the study design, data collection, analysis, or interpretation of results.

Reducing low-value care—services for which there is little to no benefit, little benefit relative to cost, or outsized potential harm compared with benefit—is an essential step toward maintaining or improving quality while lowering cost. Unfortunately, low-value services persist widelydespite professional consensus, guidelines, and national campaigns aimed to reduce them.^1-3 In turn, policy makers are beginning to consider financially penalizing physicians in order to deter low-value services.^4,5 Physician support for such penalties remains unknown. In this study, we used a randomized survey experiment to evaluate how the framing of harms from low-value care—in terms of those to patients, healthcare institutions, or society—influenced physician support of financial penalties for low-value care services.

Study Sample

By using a stratified random sample maintained by the American College of Physicians, we conducted a web-based survey among 484 physicians who were either internal medicine residents or internists practicing hospital medicine.

Instrument Design and Administration

Our study focused on 3 low-value services relevant to inpatient medicine: (1) placing, and leaving in, urinary catheters for urine output monitoring in noncritically ill patients; (2) ordering continuous telemetry monitoring for nonintensive care unit (non-ICU) patients without a protocol governing continuation; and (3) prescribing stress ulcer prophylaxis for medical patients not at a high risk for gastrointestinal (GI) complications. Although the nature and trade-offs between costs, harms, and benefits vary by individual service, all 3 are promulgated through the Choosing Wisely^® guidelines as low value based on existing data and professional consensus from the Society of Hospital Medicine.⁶

To evaluate intended behavior related to these 3 low-value services, respondents were first presented with 3 clinical vignettes focused on the care of patients hospitalized for pneumonia, congestive heart failure, and alcohol withdrawal, which were selected to reflect common inpatient medicine scenarios. Respondents were asked to use a 4-point scale (very likely to very unlikely) to estimate how likely they were to recommend various tests or treatments, including the low-value services noted above. Respondents who were “somewhat unlikely” and “very unlikely” to recommend low-value services were considered concordant with low-value care guidelines.

Following the vignettes, respondents then used a 5-point scale (strongly agree to strongly disagree) to indicate their agreement with a policy that financially penalizes physicians for prescribing each service. Support was defined as “somewhat or strongly” agreeing with the policy. Respondents were randomized to receive 1 of 3 versions of this question (supplementary Appendix).

All versions stated that, “According to research and expert opinion, certain aspects of inpatient care provide little benefit to patients” and listed the 3 low-value services noted above. The “patient harm” version also described the harm of low-value care as costs to patients and risk for clinical harms and complications. The “societal harm” version described the harms as costs to society and utilization of limited healthcare resources. The “institutional harm” version described harms as costs to hospitals and insurers.

Other survey items were adapted from existing literature^7-9 and evaluated respondent beliefs about the effectiveness of physician incentives in improving the value of care, as well as the appropriateness of including cost considerations in clinical decision-making.

The instrument was pilot tested among study team members and several independent internists affiliated with the University of Pennsylvania. After incorporating feedback into the final instrument, the web-based survey was distributed to eligible physicians via e-mail. Responses were anonymous and respondents received a $15 gift card for participation. The protocol was reviewed and deemed exempt by the University of Pennsylvania Institutional Review Board.

Statistical Analysis

Respondent characteristics (sociodemographic, intended clinical behavior, and cost control attitudes) were described by using percentages for categorical variables and medians and interquartile ranges for continuous variables. Balance in respondent characteristics across survey versions was evaluated using χ² and Kruskal-Wallis tests. Multivariable logistic regression, adjusted for characteristics in the Table, was used to evaluate the association between survey version and policy support. All tests of significance were 2-tailed with significance level alpha = 0.05. Analyses were performed using STATA version 14.1 (StataCorp LLC, College Station, TX, http://www.stata.com).

Of 484 eligible respondents, 187 (39%) completed the survey. Compared with nonrespondents, respondents were more likely to be female (30% vs 26%, P = 0.001), older (mean age 41 vs 36 years, P < 0.001), and practicing clinicians rather than internal medicine residents (87% vs 69%, P < 0.001). Physician characteristics were similar across the 3 survey versions (Table). Most respondents agreed that financial incentives for individual physicians is an effective way to improve the value of healthcare (73.3%) and that physicians should consider the costs of a test or treatment to society when making clinical decisions for patients (79.1%). The majority also felt that clinicians have a duty to offer a test or treatment to a patient if it has any chance of helping them (70.1%) and that it is inappropriate for anyone beyond the clinician and patient to decide if a test or treatment is “worth the cost” (63.6%).

To our knowledge, this study is the first to prospectively evaluate physician support of financial penalties for low-value services relevant to hospital medicine. It has 2 main findings.

First, although overall policy support was relatively low (39.6%), it varied significantly on the basis of how the harms of low-value care were framed. Support was highest in the “societal harm” version, suggesting that emphasizing these harms may increase acceptability of financial penalties among physicians and contribute to the larger effort to decrease low-value care in hospital settings. The comparatively low support for the “patient harm” version is somewhat surprising but may reflect variation in the nature of harm, benefit, and cost trade-offs for individual low-value services, as noted above, and physician belief that some low-value services do not in fact produce significant clinical harms.

For example, whereas evidence demonstrates that stress ulcer prophylaxis in non-ICU patients can harm patients through nosocomial infections and adverse drug effects,^10,11 the clinical harms of telemetry are less obvious. Telemetry’s low value derives more from its high cost relative to benefit, rather than its potential for clinical harm.⁶ The many paths to “low value” underscore the need to examine attitudes and uptake toward these services separately and may explain the wide range in concordance between intended clinical behavior and low-value care guidelines (11.8% to 78.6%).

Reinforcing policies could more effectively deter low-value care. For example, multiple forces, including Medicare payment reform and national accreditation policies,^12,13 have converged to discourage low-value use of urinary catheters in hospitalized patients. In contrast, there has been little reinforcement beyond consensus guidelines to reduce low-value use of telemetric monitoring. Given questions about whether consensus methods alone can deter low-value care beyond obvious “low hanging fruit,”¹⁴ policy makers could coordinate policies to accelerate progress within other priority areas.

Broad policies should also be paired with local initiatives to influence physician behavior. For example, health systems have begun successfully leveraging the electronic medical record and utilizing behavioral economics principles to design interventions to reduce inappropriate overuse of antibiotics for upper respiratory infections in primary care clinics.¹⁵ Organizations are also redesigning care processes in response to resource utilization imperatives under ongoing value-based care payment reform. Care redesign and behavioral interventions embedded at the point of care can both help deter low-value services in inpatient settings.

Study limitations include a relatively low response rate, which limits generalizability. However, all 3 randomized groups were similar on measured characteristics, and experimental randomization reduces the nonresponse bias concerns accompanying descriptive surveys. Additionally, although we evaluated intended clinical behavior in a national sample, our results may not reflect actual behavior among all physicians practicing hospital medicine. Future work could include assessments of actual or self-reported practices or examine additional factors, including site, years of practice, knowledge about guidelines, and other possible determinants of guideline-concordant behaviors.

Despite these limitations, our study provides important early evidence about physician support of financial penalties for low-value care relevant to hospital medicine. As policy makers design and organizational leaders implement financial incentive policies, this information can help increase their acceptability among physicians and more effectively reduce low-value care within hospitals.

Disclosure

Drs. Liao, Schapira, Mitra, and Weissman have no conflicts to disclose. Dr. Navathe serves as advisor to Navvis and Company, Navigant Inc, Lynx Medical, Indegene Inc, and Sutherland Global Services and receives an honorarium from Elsevier Press, none of which have relationship to this manuscript. Dr. Asch is a partner and part owner of VAL Health, which has no relationship to this manuscript.

Funding

This work was supported by The Leonard Davis Institute of Health Economics at the University of Pennsylvania, which had no role in the study design, data collection, analysis, or interpretation of results.

As part of the Choosing Wisely^® campaign, the Society of Hospital Medicine recommends against performing “repetitive complete blood count [CBC] and chemistry testing in the face of clinical and lab stability.”¹ This recommendation stems from a body of research that shows that frequent or excessive phlebotomy can have negative consequences, including iatrogenic anemia (termed hospital-acquired anemia), which may necessitate blood transfusion.² The downstream effects of potentially unnecessary testing, including the evaluation of false-positive results, must also be considered. Additional important effects include patient discomfort and disruption of sleep and unproductive work by hospital staff, including nurses, phlebotomists, and laboratory technicians.

Though interventions to reduce unnecessary daily labs have been previously evaluated, there are no studies that focus on decreasing lab testing on patients deemed clinically stable and close to discharge. This is in part due to the absence of clear criteria or guidelines to define clinical stability in the context of lab utilization.

We therefore aimed to implement a multifaceted, patient-centered initiative—the Necessity of Labs Assessed Bedside (NO LABS)—that focused on reducing lab testing in patients at 24 to 48 hours before discharge. We targeted the 24 to 48-hour period before the anticipated date of discharge, as this may be a period of greater stability and provide an opportunity to identify and decrease unnecessary testing.

The study took place at Mount Sinai Hospital, which is an 1174-bed tertiary care teaching hospital in New York City. We targeted 2 inpatient medicine units where virtually all patients are assigned to a hospitalist rotating for a 2- to 4-week period, for the period of July 1, 2015, to July 31, 2016. These units employed bedside interdisciplinary rounds (IDR) attended by the hospitalist, social worker, case manager, nurse, nurse manager, and medical director. Bedside IDR focuses on the daily plan and patient safety by utilizing a scripted format.³ Our multifaceted intervention included prompting the hospitalist physician during bedside IDR, education of the clinicians, and regular data review for the hospitalists and unit staff.

As described by Dunn et al.,³ the IDR script included the following: a review of the plan of care by the hospitalist, identifying a patient’s personal goals for the day, a brief update of discharge planning (as appropriate), and a safety assessment performed by the nurse (identifying Foley catheters, falls risk, etc). We incorporated an inquiry into the daily IDR script identifying clinically stable patients for discharge in the next 24 to 48 hours (based on physician judgment), followed by a prompt to the hospitalist to discontinue labs when appropriate. The unit medical director and nurse manager were both tasked with prompting the hospitalist at the bedside. Our hospital utilizes computerized physician order entry. Lab orders were then discontinued by the clinician during rounds using a computer on wheels (or after rounds when one was not available). The hospitalist, unit medical director, and nurse manager were reminded about the project through weekly e-mails and in-person communication.

To assess whether the prompt was being incorporated consistently, an observer was added to rounds beginning in the second month of the project. The observer was present at least 3 times a week for the subsequent 3 months of the project. Our intervention also included education geared towards hospitalists, including a brief presentation on reducing unnecessary lab testing during a monthly hospitalist faculty meeting (the first and sixth month of the intervention). The group’s data on laboratory testing within the 24 to 48 hours prior to discharge were also presented at these monthly meetings (beginning 2 months into the intervention and monthly thereafter). Lastly, we provided the unit staff with unit-level metrics, biweekly for the first 3 months and every 2 to 3 months thereafter.

We extracted electronic medical record (EMR) data on lab utilization for patients on the 2 hospitalist units for the intervention period. Baseline data were obtained from July 1, 2014, to June 30, 2015. Patients with a length of stay (LOS) ≤7 days (75th percentile) were included; on these units, longer stays were considered more likely to have complex social issues delaying discharge and thus less likely to require laboratory testing. We tracked ordering for 4 common lab tests: basic metabolic panel, CBC, CBC with differential, and the comprehensive metabolic panel. The primary outcome was the monthly percentage of patients for whom testing was ordered in the 24 and 48 hours preceding discharge. A secondary outcome was testing at 72 hours preceding discharge to identify any potential compensatory (increased) testing the evening prior. We applied a quasi-experimental interrupted time series design with a segmented regression analysis to estimate changes before and after our intervention, expressed in acute changes (change in intercept) and over time (changes in trend) while adjusting for preintervention trends. All analyses were performed with SAS v9.4 statistical software (SAS Institute, Cary, NC). Our project was deemed a quality improvement project and thus an IRB submission was not required.

There were 1579 discharges in the preintervention period and 1308 discharges in the postintervention period. The average age of the patient population was similar in the baseline and postintervention groups (61.5 vs 59.3 years; P = 0.400), and there was no difference in the mean LOS before and after implementation (3.67 vs 3.68 days; P = 0.817).

Our structured, multifaceted approach effectively reduced daily lab testing in the 24 to 48 hours prior to discharge. Bedside IDR provided a unique opportunity to effectively communicate to the patient about necessary (or unnecessary) testing. Moreover, given the complexity of identifying clinical stability, our strategy focused on the onset of discharge planning, a more easily discernible and less obtrusive focal point to promote the discontinuation of lab testing.

Though the nature of bundled interventions can make it difficult to identify which intervention is most effective, we believe that all interventions were effective in different capacities during various phases in the intervention period. We believe that the decrease in lab testing in the 24 to 48 hours preceding discharge was primarily driven by the new rounding structure. This is evident in the significant decrease seen in the first few months of the intervention period. Six months into the intervention, we begin to see a decrease at 72 hours prior to discharge. Additionally, we see a decrease in the mean number of labs per patient day over the entire hospitalization period. We attribute these results to a gradual shift in the culture in our division as a direct consequence of educational sessions and individual feedback provided during this time.

To our knowledge, this is the first study to use anticipated discharge as a correlate for clinical stability and therefore as an opportunity to prompt discontinuation of laboratory testing. Other studies evaluated interventions targeting the EMR and the ease with which providers can order recurring labs. These include restricting recurring orders in the EMR,⁴ a robust education and awareness campaign targeting house staff,⁵ and other multifaceted approaches to decreasing lab utilization,⁶ all of which have shown promising results. While these approaches show varying degrees of success, ours is unique in its focus on the period prior to discharge. In addition, the intervention can be readily implemented in settings that utilize scripted IDR. It also brings high-value decision-making to the bedside by informing the patient that in the setting of presumed clinical stability, no additional tests are warranted.

Our study has several limitations. First, while interdisciplinary discharge rounds are widely implemented,^7,8 our rounds occur at the bedside and employ a script, potentially limiting generalizability. The structured prompting may be feasible during structured IDR in a standard conference room setting, though we did not assess this model. Second, bedside rounds only included patients who were able to participate. Rounding on patients unable to participate, such as patients with delirium with agitation, was done outside the patient room rather than at the bedside. A modified script was used in these instances (absent questions addressed to the patient), allowing for the prompt to be incorporated. These patients were included in the analysis. Lastly, as previously stated, we cannot clearly identify which intervention (the prompt, education, or feedback) most effectively led to a sustained decrease in lab ordering.

Our structured, multifaceted intervention reduced laboratory testing during the last 48 hours of admission. Hospitals that aim to decrease potentially unnecessary lab testing should consider implementing a bundle, including a prompt at a uniform and structured point during the hospitalization of patients who are expected to be discharged within 24 to 48 hours, clinician education, an audit, and feedback.

Disclosure

 All authors report no conflicts of interest to disclose.

As part of the Choosing Wisely^® campaign, the Society of Hospital Medicine recommends against performing “repetitive complete blood count [CBC] and chemistry testing in the face of clinical and lab stability.”¹ This recommendation stems from a body of research that shows that frequent or excessive phlebotomy can have negative consequences, including iatrogenic anemia (termed hospital-acquired anemia), which may necessitate blood transfusion.² The downstream effects of potentially unnecessary testing, including the evaluation of false-positive results, must also be considered. Additional important effects include patient discomfort and disruption of sleep and unproductive work by hospital staff, including nurses, phlebotomists, and laboratory technicians.

Though interventions to reduce unnecessary daily labs have been previously evaluated, there are no studies that focus on decreasing lab testing on patients deemed clinically stable and close to discharge. This is in part due to the absence of clear criteria or guidelines to define clinical stability in the context of lab utilization.

We therefore aimed to implement a multifaceted, patient-centered initiative—the Necessity of Labs Assessed Bedside (NO LABS)—that focused on reducing lab testing in patients at 24 to 48 hours before discharge. We targeted the 24 to 48-hour period before the anticipated date of discharge, as this may be a period of greater stability and provide an opportunity to identify and decrease unnecessary testing.

The study took place at Mount Sinai Hospital, which is an 1174-bed tertiary care teaching hospital in New York City. We targeted 2 inpatient medicine units where virtually all patients are assigned to a hospitalist rotating for a 2- to 4-week period, for the period of July 1, 2015, to July 31, 2016. These units employed bedside interdisciplinary rounds (IDR) attended by the hospitalist, social worker, case manager, nurse, nurse manager, and medical director. Bedside IDR focuses on the daily plan and patient safety by utilizing a scripted format.³ Our multifaceted intervention included prompting the hospitalist physician during bedside IDR, education of the clinicians, and regular data review for the hospitalists and unit staff.

As described by Dunn et al.,³ the IDR script included the following: a review of the plan of care by the hospitalist, identifying a patient’s personal goals for the day, a brief update of discharge planning (as appropriate), and a safety assessment performed by the nurse (identifying Foley catheters, falls risk, etc). We incorporated an inquiry into the daily IDR script identifying clinically stable patients for discharge in the next 24 to 48 hours (based on physician judgment), followed by a prompt to the hospitalist to discontinue labs when appropriate. The unit medical director and nurse manager were both tasked with prompting the hospitalist at the bedside. Our hospital utilizes computerized physician order entry. Lab orders were then discontinued by the clinician during rounds using a computer on wheels (or after rounds when one was not available). The hospitalist, unit medical director, and nurse manager were reminded about the project through weekly e-mails and in-person communication.

To assess whether the prompt was being incorporated consistently, an observer was added to rounds beginning in the second month of the project. The observer was present at least 3 times a week for the subsequent 3 months of the project. Our intervention also included education geared towards hospitalists, including a brief presentation on reducing unnecessary lab testing during a monthly hospitalist faculty meeting (the first and sixth month of the intervention). The group’s data on laboratory testing within the 24 to 48 hours prior to discharge were also presented at these monthly meetings (beginning 2 months into the intervention and monthly thereafter). Lastly, we provided the unit staff with unit-level metrics, biweekly for the first 3 months and every 2 to 3 months thereafter.

We extracted electronic medical record (EMR) data on lab utilization for patients on the 2 hospitalist units for the intervention period. Baseline data were obtained from July 1, 2014, to June 30, 2015. Patients with a length of stay (LOS) ≤7 days (75th percentile) were included; on these units, longer stays were considered more likely to have complex social issues delaying discharge and thus less likely to require laboratory testing. We tracked ordering for 4 common lab tests: basic metabolic panel, CBC, CBC with differential, and the comprehensive metabolic panel. The primary outcome was the monthly percentage of patients for whom testing was ordered in the 24 and 48 hours preceding discharge. A secondary outcome was testing at 72 hours preceding discharge to identify any potential compensatory (increased) testing the evening prior. We applied a quasi-experimental interrupted time series design with a segmented regression analysis to estimate changes before and after our intervention, expressed in acute changes (change in intercept) and over time (changes in trend) while adjusting for preintervention trends. All analyses were performed with SAS v9.4 statistical software (SAS Institute, Cary, NC). Our project was deemed a quality improvement project and thus an IRB submission was not required.

There were 1579 discharges in the preintervention period and 1308 discharges in the postintervention period. The average age of the patient population was similar in the baseline and postintervention groups (61.5 vs 59.3 years; P = 0.400), and there was no difference in the mean LOS before and after implementation (3.67 vs 3.68 days; P = 0.817).

Our structured, multifaceted approach effectively reduced daily lab testing in the 24 to 48 hours prior to discharge. Bedside IDR provided a unique opportunity to effectively communicate to the patient about necessary (or unnecessary) testing. Moreover, given the complexity of identifying clinical stability, our strategy focused on the onset of discharge planning, a more easily discernible and less obtrusive focal point to promote the discontinuation of lab testing.

Though the nature of bundled interventions can make it difficult to identify which intervention is most effective, we believe that all interventions were effective in different capacities during various phases in the intervention period. We believe that the decrease in lab testing in the 24 to 48 hours preceding discharge was primarily driven by the new rounding structure. This is evident in the significant decrease seen in the first few months of the intervention period. Six months into the intervention, we begin to see a decrease at 72 hours prior to discharge. Additionally, we see a decrease in the mean number of labs per patient day over the entire hospitalization period. We attribute these results to a gradual shift in the culture in our division as a direct consequence of educational sessions and individual feedback provided during this time.

To our knowledge, this is the first study to use anticipated discharge as a correlate for clinical stability and therefore as an opportunity to prompt discontinuation of laboratory testing. Other studies evaluated interventions targeting the EMR and the ease with which providers can order recurring labs. These include restricting recurring orders in the EMR,⁴ a robust education and awareness campaign targeting house staff,⁵ and other multifaceted approaches to decreasing lab utilization,⁶ all of which have shown promising results. While these approaches show varying degrees of success, ours is unique in its focus on the period prior to discharge. In addition, the intervention can be readily implemented in settings that utilize scripted IDR. It also brings high-value decision-making to the bedside by informing the patient that in the setting of presumed clinical stability, no additional tests are warranted.

Our study has several limitations. First, while interdisciplinary discharge rounds are widely implemented,^7,8 our rounds occur at the bedside and employ a script, potentially limiting generalizability. The structured prompting may be feasible during structured IDR in a standard conference room setting, though we did not assess this model. Second, bedside rounds only included patients who were able to participate. Rounding on patients unable to participate, such as patients with delirium with agitation, was done outside the patient room rather than at the bedside. A modified script was used in these instances (absent questions addressed to the patient), allowing for the prompt to be incorporated. These patients were included in the analysis. Lastly, as previously stated, we cannot clearly identify which intervention (the prompt, education, or feedback) most effectively led to a sustained decrease in lab ordering.

Our structured, multifaceted intervention reduced laboratory testing during the last 48 hours of admission. Hospitals that aim to decrease potentially unnecessary lab testing should consider implementing a bundle, including a prompt at a uniform and structured point during the hospitalization of patients who are expected to be discharged within 24 to 48 hours, clinician education, an audit, and feedback.

Disclosure

 All authors report no conflicts of interest to disclose.

As part of the Choosing Wisely^® campaign, the Society of Hospital Medicine recommends against performing “repetitive complete blood count [CBC] and chemistry testing in the face of clinical and lab stability.”¹ This recommendation stems from a body of research that shows that frequent or excessive phlebotomy can have negative consequences, including iatrogenic anemia (termed hospital-acquired anemia), which may necessitate blood transfusion.² The downstream effects of potentially unnecessary testing, including the evaluation of false-positive results, must also be considered. Additional important effects include patient discomfort and disruption of sleep and unproductive work by hospital staff, including nurses, phlebotomists, and laboratory technicians.

Though interventions to reduce unnecessary daily labs have been previously evaluated, there are no studies that focus on decreasing lab testing on patients deemed clinically stable and close to discharge. This is in part due to the absence of clear criteria or guidelines to define clinical stability in the context of lab utilization.

We therefore aimed to implement a multifaceted, patient-centered initiative—the Necessity of Labs Assessed Bedside (NO LABS)—that focused on reducing lab testing in patients at 24 to 48 hours before discharge. We targeted the 24 to 48-hour period before the anticipated date of discharge, as this may be a period of greater stability and provide an opportunity to identify and decrease unnecessary testing.

The study took place at Mount Sinai Hospital, which is an 1174-bed tertiary care teaching hospital in New York City. We targeted 2 inpatient medicine units where virtually all patients are assigned to a hospitalist rotating for a 2- to 4-week period, for the period of July 1, 2015, to July 31, 2016. These units employed bedside interdisciplinary rounds (IDR) attended by the hospitalist, social worker, case manager, nurse, nurse manager, and medical director. Bedside IDR focuses on the daily plan and patient safety by utilizing a scripted format.³ Our multifaceted intervention included prompting the hospitalist physician during bedside IDR, education of the clinicians, and regular data review for the hospitalists and unit staff.

As described by Dunn et al.,³ the IDR script included the following: a review of the plan of care by the hospitalist, identifying a patient’s personal goals for the day, a brief update of discharge planning (as appropriate), and a safety assessment performed by the nurse (identifying Foley catheters, falls risk, etc). We incorporated an inquiry into the daily IDR script identifying clinically stable patients for discharge in the next 24 to 48 hours (based on physician judgment), followed by a prompt to the hospitalist to discontinue labs when appropriate. The unit medical director and nurse manager were both tasked with prompting the hospitalist at the bedside. Our hospital utilizes computerized physician order entry. Lab orders were then discontinued by the clinician during rounds using a computer on wheels (or after rounds when one was not available). The hospitalist, unit medical director, and nurse manager were reminded about the project through weekly e-mails and in-person communication.

To assess whether the prompt was being incorporated consistently, an observer was added to rounds beginning in the second month of the project. The observer was present at least 3 times a week for the subsequent 3 months of the project. Our intervention also included education geared towards hospitalists, including a brief presentation on reducing unnecessary lab testing during a monthly hospitalist faculty meeting (the first and sixth month of the intervention). The group’s data on laboratory testing within the 24 to 48 hours prior to discharge were also presented at these monthly meetings (beginning 2 months into the intervention and monthly thereafter). Lastly, we provided the unit staff with unit-level metrics, biweekly for the first 3 months and every 2 to 3 months thereafter.

We extracted electronic medical record (EMR) data on lab utilization for patients on the 2 hospitalist units for the intervention period. Baseline data were obtained from July 1, 2014, to June 30, 2015. Patients with a length of stay (LOS) ≤7 days (75th percentile) were included; on these units, longer stays were considered more likely to have complex social issues delaying discharge and thus less likely to require laboratory testing. We tracked ordering for 4 common lab tests: basic metabolic panel, CBC, CBC with differential, and the comprehensive metabolic panel. The primary outcome was the monthly percentage of patients for whom testing was ordered in the 24 and 48 hours preceding discharge. A secondary outcome was testing at 72 hours preceding discharge to identify any potential compensatory (increased) testing the evening prior. We applied a quasi-experimental interrupted time series design with a segmented regression analysis to estimate changes before and after our intervention, expressed in acute changes (change in intercept) and over time (changes in trend) while adjusting for preintervention trends. All analyses were performed with SAS v9.4 statistical software (SAS Institute, Cary, NC). Our project was deemed a quality improvement project and thus an IRB submission was not required.

There were 1579 discharges in the preintervention period and 1308 discharges in the postintervention period. The average age of the patient population was similar in the baseline and postintervention groups (61.5 vs 59.3 years; P = 0.400), and there was no difference in the mean LOS before and after implementation (3.67 vs 3.68 days; P = 0.817).

Our structured, multifaceted approach effectively reduced daily lab testing in the 24 to 48 hours prior to discharge. Bedside IDR provided a unique opportunity to effectively communicate to the patient about necessary (or unnecessary) testing. Moreover, given the complexity of identifying clinical stability, our strategy focused on the onset of discharge planning, a more easily discernible and less obtrusive focal point to promote the discontinuation of lab testing.

Though the nature of bundled interventions can make it difficult to identify which intervention is most effective, we believe that all interventions were effective in different capacities during various phases in the intervention period. We believe that the decrease in lab testing in the 24 to 48 hours preceding discharge was primarily driven by the new rounding structure. This is evident in the significant decrease seen in the first few months of the intervention period. Six months into the intervention, we begin to see a decrease at 72 hours prior to discharge. Additionally, we see a decrease in the mean number of labs per patient day over the entire hospitalization period. We attribute these results to a gradual shift in the culture in our division as a direct consequence of educational sessions and individual feedback provided during this time.

To our knowledge, this is the first study to use anticipated discharge as a correlate for clinical stability and therefore as an opportunity to prompt discontinuation of laboratory testing. Other studies evaluated interventions targeting the EMR and the ease with which providers can order recurring labs. These include restricting recurring orders in the EMR,⁴ a robust education and awareness campaign targeting house staff,⁵ and other multifaceted approaches to decreasing lab utilization,⁶ all of which have shown promising results. While these approaches show varying degrees of success, ours is unique in its focus on the period prior to discharge. In addition, the intervention can be readily implemented in settings that utilize scripted IDR. It also brings high-value decision-making to the bedside by informing the patient that in the setting of presumed clinical stability, no additional tests are warranted.

Our study has several limitations. First, while interdisciplinary discharge rounds are widely implemented,^7,8 our rounds occur at the bedside and employ a script, potentially limiting generalizability. The structured prompting may be feasible during structured IDR in a standard conference room setting, though we did not assess this model. Second, bedside rounds only included patients who were able to participate. Rounding on patients unable to participate, such as patients with delirium with agitation, was done outside the patient room rather than at the bedside. A modified script was used in these instances (absent questions addressed to the patient), allowing for the prompt to be incorporated. These patients were included in the analysis. Lastly, as previously stated, we cannot clearly identify which intervention (the prompt, education, or feedback) most effectively led to a sustained decrease in lab ordering.

Our structured, multifaceted intervention reduced laboratory testing during the last 48 hours of admission. Hospitals that aim to decrease potentially unnecessary lab testing should consider implementing a bundle, including a prompt at a uniform and structured point during the hospitalization of patients who are expected to be discharged within 24 to 48 hours, clinician education, an audit, and feedback.

Disclosure

 All authors report no conflicts of interest to disclose.

Pneumonia is a major cause of hospitalization, mortality, and healthcare cost. ^1,2 The diagnosis involves clinical features plus radiographic evidence of infection. Hospital-acquired pneumonia (HAP) is defined by the Infectious Disease Society of America (IDSA) as a pneumonia that occurs ≥48 hours after admission and is not associated with mechanical ventilation. ³

IDSA recommendations suggest that patients with suspected HAP be treated based on results of noninvasively obtained sputum cultures rather than being treated empirically. ³ This recommendation is graded as weak with low-quality evidence based on a lack of both evidence showing that respiratory cultures improve clinical outcomes and studies examining the yield of noninvasive collection methods. ^4,5 However, resistant pathogens lead to a risk of inadequate empiric therapy, which is associated with increased mortality. ⁶ Culture data may provide an opportunity for escalation or de-escalation of antibiotic coverage. IDSA recommendations for microbiologic sampling are thus aimed at increasing appropriate coverage and minimizing unnecessary antibiotic exposure.

While the yield and clinical utility of sputum culture in community-acquired pneumonia has been studied extensively, data examining the yield of sputum culture in HAP (non–ventilator-associated pneumonia [non-VAP]) are sparse. In 1 small single-center study, researchers demonstrated positive sputum cultures in 17/35 (48.6%) patients with radiographically confirmed cases of HAP, ⁷ while in another study, researchers demonstrated positive sputum cultures in 57/63 (90.5%). ⁸ We aimed to identify the frequency with which sputum cultures positively identify an organism, identify predictors of positive sputum cultures, and characterize the microbiology of sputum cultures in a large cohort of HAP cases.

We conducted a retrospective cohort study of patients admitted to a large academic medical center in Boston, Massachusetts, from January 2007 to July 2013. All patients ≥18 years of age were eligible for inclusion. We excluded outside hospital transfers, those with a length of hospitalization <48 hours, and psychiatric admissions.

The study was approved by the institutional review board at the Beth Israel Deaconess Medical Center and granted a waiver of informed consent. Data were collected from electronic databases and supplemented by chart review.

Hospital-Acquired Pneumonia

We defined HAP as pneumonia occurring at least 48 hours after admission, consistent with American Thoracic Society and IDSA criteria.³ To identify cases, we reviewed the charts of all admissions identified as having a discharge International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code for bacterial pneumonia (481, 482, 483, 485, 486, 507), indicated as not “present-on-admission.” We validated that the treating clinician had clinically diagnosed pneumonia and initiated antibiotics for this purpose by performing chart review. We reviewed the radiologist interpretation of radiographs surrounding the date of the clinical diagnosis of pneumonia to confirm the presence of a new opacity. Uncertain cases (with respect to either the presence of pneumonia or the timing of the diagnosis) were reviewed by a second member of the study team and, in the case of disagreement, adjudicated by a third member of the study team. Only the first clinically validated HAP per hospitalization was included in the analysis. To focus on HAP rather than VAP, we excluded hospitalizations in which the date of a procedure code for mechanical ventilation preceded the date of pneumonia diagnosis.

Microbiology

In our analysis, we used sputum samples obtained from expectorated or induced samples to evaluate the yield of noninvasive sputum sampling, as recommended by the IDSA. We included sputum samples collected ≥48 hours after admission and within 7 days of the clinical diagnosis of HAP. Sputum samples with >10 epithelial cells per high-power field (hpf) were considered to be contaminated. Among noncontaminated samples, positive sputum cultures were defined as those with a microbiologic diagnosis other than “oral flora,” while those with no growth or growth of oral flora or only yeast were considered to be negative. The hospital’s microbiology laboratory does not routinely provide species identification for Gram-negative rods (GNRs) growing on culture in the presence of growth of ≥3 other colony types. We considered such GNRs (not further speciated) to represent a positive culture result in our analysis given that colonization versus pathogenicity is a clinical distinction and, as such, these results may impact antibiotic choice.

Statistical Analysis

Data were analyzed by using SAS software, version 9.3. We used a 2-sided P value of <0.05 to indicate statistical significance for all comparisons. We used the χ² test and the nonparametric median test for unadjusted comparisons.

To identify predictors of a positive (versus negative or contaminated) sputum culture among patients with HAP, we used a generalized estimating equation model with a Poisson distribution error term, log link, and first-order autoregressive correlation structure to account for multiple sputum specimens per patient. We combined culture negative and contaminated samples to highlight the clinical utility of sputum culture in a real-world setting. Potential predictors chosen based on clinical grounds included all variables listed in Table 1. We defined comorbidities specified in Table 1 via ICD-9-CM secondary diagnosis codes and diagnosis related groups (DRGs) using Healthcare Cost and Utilization Project Comorbidity Software, version 3.7, based on the work of Elixhauser et al.^9,10; dialysis use was defined by an ICD-9-CM procedure code of 39.95; inpatient steroid use was defined by a hospital pharmacy charge for a systemic steroid in the 7 days preceding the sputum sample.

There were 230,635 hospitalizations of patients ≥18 years of age from January 2007 to July 2013. After excluding outside hospital transfers (n = 14,422), hospitalizations <48 hours in duration (n = 59,774), and psychiatric hospitalizations (n = 9887), there were 146,552 hospitalizations in the cohort.

To our knowledge, our study is the first to assess the predictors of positive sputum culture among patients with HAP (non-VAP) who had sputum samples obtained noninvasively, and this study is larger than prior studies in which researchers reported on sputum culture yield in HAP. Sputum samples were obtained in 29.4% cases of clinically diagnosed HAP. Although 87% of specimens obtained were culture-negative or contaminated, 13% yielded a bacterial organism. Although we do not report the antibiotic sensitivity patterns of the isolated organisms, the organisms identified frequently demonstrate antibiotic resistance, highlighting the potential for both antibiotic escalation and de-escalation based on sputum culture. In a multivariable model, presence of chronic lung disease and steroid use in the preceding week were both significantly associated with culture positivity.

The retrospective nature of the study raises the possibility of selection bias from systematic differences between the 29.4% of patients with HAP who had sputum collected and those who did not. Patients with sputum cultures were similar to patients without cultures in most measured characteristics, but we are unable to know what the yield of noninvasive sputum culture would have been had all patients with HAP been sampled. As such, our findings reflect the yield of sputum culture among patients with HAP for whom cultures were successfully obtained. It is not clear why only 29.4% of HAP patients received IDSA guideline-concordant care, but similar rates of culture use are reported elsewhere.⁷ While physician decision-making could have contributed to this finding, it is also possible that many sick, hospitalized patients are simply unable to produce sputum for analysis. In future studies, researchers should examine barriers to guideline-concordant care.

We considered a culture result of GNRs (not further speciated) as positive in our analysis because this result indicates growth of mixed bacterial types, the pathogenicity of which is a clinical determination. Physicians may request speciation and antibiotic sensitivities and, as such, these results have the potential to impact antibiotic choice. Had we considered such cultures to be negative or contaminated, the rate of culture positivity would have been only slightly reduced from 63/478 (13.2%) to 50/478 (10.5%).

The strengths of our study include the chart-based validation of administratively identified cases of pneumonia and a large cohort. There are also limitations. The single-center nature of the study has implications for pretest probability and generalizability. Additionally, in our study, we did not examine outcomes among patients treated empirically versus those treated based on sputum culture results. Finally, our reliance on administrative codes to identify cases of HAP for subsequent validation could have resulted in incomplete capture of HAP cases.

In conclusion, in our study, we provide an estimate of the diagnostic yield of sputum culture in a large cohort with chart-validated HAP, a description of HAP microbiology, and predictors of positive sputum culture. Thirteen percent of patients who had sputum culture testing received a microbiologic diagnosis. Because of the relative ease of obtaining a sputum sample and the microbiologic distribution in our study (representing a mix of commonly drug-resistant pathogens and more typical community-acquired pathogens), we suggest that sputum culture in HAP is a useful diagnostic tool with the potential to inform antibiotic escalation or de-escalation.

Acknowledgments

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. Dr. Marcantonio was funded by grant number K24AG035075 from the National Institute on Aging. The funding organizations had no involvement in any aspect of the study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Disclosure

No conflicts of interest apply for any of the authors.

Pneumonia is a major cause of hospitalization, mortality, and healthcare cost. ^1,2 The diagnosis involves clinical features plus radiographic evidence of infection. Hospital-acquired pneumonia (HAP) is defined by the Infectious Disease Society of America (IDSA) as a pneumonia that occurs ≥48 hours after admission and is not associated with mechanical ventilation. ³

IDSA recommendations suggest that patients with suspected HAP be treated based on results of noninvasively obtained sputum cultures rather than being treated empirically. ³ This recommendation is graded as weak with low-quality evidence based on a lack of both evidence showing that respiratory cultures improve clinical outcomes and studies examining the yield of noninvasive collection methods. ^4,5 However, resistant pathogens lead to a risk of inadequate empiric therapy, which is associated with increased mortality. ⁶ Culture data may provide an opportunity for escalation or de-escalation of antibiotic coverage. IDSA recommendations for microbiologic sampling are thus aimed at increasing appropriate coverage and minimizing unnecessary antibiotic exposure.

While the yield and clinical utility of sputum culture in community-acquired pneumonia has been studied extensively, data examining the yield of sputum culture in HAP (non–ventilator-associated pneumonia [non-VAP]) are sparse. In 1 small single-center study, researchers demonstrated positive sputum cultures in 17/35 (48.6%) patients with radiographically confirmed cases of HAP, ⁷ while in another study, researchers demonstrated positive sputum cultures in 57/63 (90.5%). ⁸ We aimed to identify the frequency with which sputum cultures positively identify an organism, identify predictors of positive sputum cultures, and characterize the microbiology of sputum cultures in a large cohort of HAP cases.

We conducted a retrospective cohort study of patients admitted to a large academic medical center in Boston, Massachusetts, from January 2007 to July 2013. All patients ≥18 years of age were eligible for inclusion. We excluded outside hospital transfers, those with a length of hospitalization <48 hours, and psychiatric admissions.

The study was approved by the institutional review board at the Beth Israel Deaconess Medical Center and granted a waiver of informed consent. Data were collected from electronic databases and supplemented by chart review.

Hospital-Acquired Pneumonia

We defined HAP as pneumonia occurring at least 48 hours after admission, consistent with American Thoracic Society and IDSA criteria.³ To identify cases, we reviewed the charts of all admissions identified as having a discharge International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code for bacterial pneumonia (481, 482, 483, 485, 486, 507), indicated as not “present-on-admission.” We validated that the treating clinician had clinically diagnosed pneumonia and initiated antibiotics for this purpose by performing chart review. We reviewed the radiologist interpretation of radiographs surrounding the date of the clinical diagnosis of pneumonia to confirm the presence of a new opacity. Uncertain cases (with respect to either the presence of pneumonia or the timing of the diagnosis) were reviewed by a second member of the study team and, in the case of disagreement, adjudicated by a third member of the study team. Only the first clinically validated HAP per hospitalization was included in the analysis. To focus on HAP rather than VAP, we excluded hospitalizations in which the date of a procedure code for mechanical ventilation preceded the date of pneumonia diagnosis.

Microbiology

In our analysis, we used sputum samples obtained from expectorated or induced samples to evaluate the yield of noninvasive sputum sampling, as recommended by the IDSA. We included sputum samples collected ≥48 hours after admission and within 7 days of the clinical diagnosis of HAP. Sputum samples with >10 epithelial cells per high-power field (hpf) were considered to be contaminated. Among noncontaminated samples, positive sputum cultures were defined as those with a microbiologic diagnosis other than “oral flora,” while those with no growth or growth of oral flora or only yeast were considered to be negative. The hospital’s microbiology laboratory does not routinely provide species identification for Gram-negative rods (GNRs) growing on culture in the presence of growth of ≥3 other colony types. We considered such GNRs (not further speciated) to represent a positive culture result in our analysis given that colonization versus pathogenicity is a clinical distinction and, as such, these results may impact antibiotic choice.

Statistical Analysis

Data were analyzed by using SAS software, version 9.3. We used a 2-sided P value of <0.05 to indicate statistical significance for all comparisons. We used the χ² test and the nonparametric median test for unadjusted comparisons.

To identify predictors of a positive (versus negative or contaminated) sputum culture among patients with HAP, we used a generalized estimating equation model with a Poisson distribution error term, log link, and first-order autoregressive correlation structure to account for multiple sputum specimens per patient. We combined culture negative and contaminated samples to highlight the clinical utility of sputum culture in a real-world setting. Potential predictors chosen based on clinical grounds included all variables listed in Table 1. We defined comorbidities specified in Table 1 via ICD-9-CM secondary diagnosis codes and diagnosis related groups (DRGs) using Healthcare Cost and Utilization Project Comorbidity Software, version 3.7, based on the work of Elixhauser et al.^9,10; dialysis use was defined by an ICD-9-CM procedure code of 39.95; inpatient steroid use was defined by a hospital pharmacy charge for a systemic steroid in the 7 days preceding the sputum sample.

There were 230,635 hospitalizations of patients ≥18 years of age from January 2007 to July 2013. After excluding outside hospital transfers (n = 14,422), hospitalizations <48 hours in duration (n = 59,774), and psychiatric hospitalizations (n = 9887), there were 146,552 hospitalizations in the cohort.

To our knowledge, our study is the first to assess the predictors of positive sputum culture among patients with HAP (non-VAP) who had sputum samples obtained noninvasively, and this study is larger than prior studies in which researchers reported on sputum culture yield in HAP. Sputum samples were obtained in 29.4% cases of clinically diagnosed HAP. Although 87% of specimens obtained were culture-negative or contaminated, 13% yielded a bacterial organism. Although we do not report the antibiotic sensitivity patterns of the isolated organisms, the organisms identified frequently demonstrate antibiotic resistance, highlighting the potential for both antibiotic escalation and de-escalation based on sputum culture. In a multivariable model, presence of chronic lung disease and steroid use in the preceding week were both significantly associated with culture positivity.

The retrospective nature of the study raises the possibility of selection bias from systematic differences between the 29.4% of patients with HAP who had sputum collected and those who did not. Patients with sputum cultures were similar to patients without cultures in most measured characteristics, but we are unable to know what the yield of noninvasive sputum culture would have been had all patients with HAP been sampled. As such, our findings reflect the yield of sputum culture among patients with HAP for whom cultures were successfully obtained. It is not clear why only 29.4% of HAP patients received IDSA guideline-concordant care, but similar rates of culture use are reported elsewhere.⁷ While physician decision-making could have contributed to this finding, it is also possible that many sick, hospitalized patients are simply unable to produce sputum for analysis. In future studies, researchers should examine barriers to guideline-concordant care.

We considered a culture result of GNRs (not further speciated) as positive in our analysis because this result indicates growth of mixed bacterial types, the pathogenicity of which is a clinical determination. Physicians may request speciation and antibiotic sensitivities and, as such, these results have the potential to impact antibiotic choice. Had we considered such cultures to be negative or contaminated, the rate of culture positivity would have been only slightly reduced from 63/478 (13.2%) to 50/478 (10.5%).

The strengths of our study include the chart-based validation of administratively identified cases of pneumonia and a large cohort. There are also limitations. The single-center nature of the study has implications for pretest probability and generalizability. Additionally, in our study, we did not examine outcomes among patients treated empirically versus those treated based on sputum culture results. Finally, our reliance on administrative codes to identify cases of HAP for subsequent validation could have resulted in incomplete capture of HAP cases.

In conclusion, in our study, we provide an estimate of the diagnostic yield of sputum culture in a large cohort with chart-validated HAP, a description of HAP microbiology, and predictors of positive sputum culture. Thirteen percent of patients who had sputum culture testing received a microbiologic diagnosis. Because of the relative ease of obtaining a sputum sample and the microbiologic distribution in our study (representing a mix of commonly drug-resistant pathogens and more typical community-acquired pathogens), we suggest that sputum culture in HAP is a useful diagnostic tool with the potential to inform antibiotic escalation or de-escalation.

Acknowledgments

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. Dr. Marcantonio was funded by grant number K24AG035075 from the National Institute on Aging. The funding organizations had no involvement in any aspect of the study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Disclosure

No conflicts of interest apply for any of the authors.

Pneumonia is a major cause of hospitalization, mortality, and healthcare cost. ^1,2 The diagnosis involves clinical features plus radiographic evidence of infection. Hospital-acquired pneumonia (HAP) is defined by the Infectious Disease Society of America (IDSA) as a pneumonia that occurs ≥48 hours after admission and is not associated with mechanical ventilation. ³

IDSA recommendations suggest that patients with suspected HAP be treated based on results of noninvasively obtained sputum cultures rather than being treated empirically. ³ This recommendation is graded as weak with low-quality evidence based on a lack of both evidence showing that respiratory cultures improve clinical outcomes and studies examining the yield of noninvasive collection methods. ^4,5 However, resistant pathogens lead to a risk of inadequate empiric therapy, which is associated with increased mortality. ⁶ Culture data may provide an opportunity for escalation or de-escalation of antibiotic coverage. IDSA recommendations for microbiologic sampling are thus aimed at increasing appropriate coverage and minimizing unnecessary antibiotic exposure.

While the yield and clinical utility of sputum culture in community-acquired pneumonia has been studied extensively, data examining the yield of sputum culture in HAP (non–ventilator-associated pneumonia [non-VAP]) are sparse. In 1 small single-center study, researchers demonstrated positive sputum cultures in 17/35 (48.6%) patients with radiographically confirmed cases of HAP, ⁷ while in another study, researchers demonstrated positive sputum cultures in 57/63 (90.5%). ⁸ We aimed to identify the frequency with which sputum cultures positively identify an organism, identify predictors of positive sputum cultures, and characterize the microbiology of sputum cultures in a large cohort of HAP cases.

We conducted a retrospective cohort study of patients admitted to a large academic medical center in Boston, Massachusetts, from January 2007 to July 2013. All patients ≥18 years of age were eligible for inclusion. We excluded outside hospital transfers, those with a length of hospitalization <48 hours, and psychiatric admissions.

The study was approved by the institutional review board at the Beth Israel Deaconess Medical Center and granted a waiver of informed consent. Data were collected from electronic databases and supplemented by chart review.

Hospital-Acquired Pneumonia

We defined HAP as pneumonia occurring at least 48 hours after admission, consistent with American Thoracic Society and IDSA criteria.³ To identify cases, we reviewed the charts of all admissions identified as having a discharge International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code for bacterial pneumonia (481, 482, 483, 485, 486, 507), indicated as not “present-on-admission.” We validated that the treating clinician had clinically diagnosed pneumonia and initiated antibiotics for this purpose by performing chart review. We reviewed the radiologist interpretation of radiographs surrounding the date of the clinical diagnosis of pneumonia to confirm the presence of a new opacity. Uncertain cases (with respect to either the presence of pneumonia or the timing of the diagnosis) were reviewed by a second member of the study team and, in the case of disagreement, adjudicated by a third member of the study team. Only the first clinically validated HAP per hospitalization was included in the analysis. To focus on HAP rather than VAP, we excluded hospitalizations in which the date of a procedure code for mechanical ventilation preceded the date of pneumonia diagnosis.

Microbiology

In our analysis, we used sputum samples obtained from expectorated or induced samples to evaluate the yield of noninvasive sputum sampling, as recommended by the IDSA. We included sputum samples collected ≥48 hours after admission and within 7 days of the clinical diagnosis of HAP. Sputum samples with >10 epithelial cells per high-power field (hpf) were considered to be contaminated. Among noncontaminated samples, positive sputum cultures were defined as those with a microbiologic diagnosis other than “oral flora,” while those with no growth or growth of oral flora or only yeast were considered to be negative. The hospital’s microbiology laboratory does not routinely provide species identification for Gram-negative rods (GNRs) growing on culture in the presence of growth of ≥3 other colony types. We considered such GNRs (not further speciated) to represent a positive culture result in our analysis given that colonization versus pathogenicity is a clinical distinction and, as such, these results may impact antibiotic choice.

Statistical Analysis

Data were analyzed by using SAS software, version 9.3. We used a 2-sided P value of <0.05 to indicate statistical significance for all comparisons. We used the χ² test and the nonparametric median test for unadjusted comparisons.

To identify predictors of a positive (versus negative or contaminated) sputum culture among patients with HAP, we used a generalized estimating equation model with a Poisson distribution error term, log link, and first-order autoregressive correlation structure to account for multiple sputum specimens per patient. We combined culture negative and contaminated samples to highlight the clinical utility of sputum culture in a real-world setting. Potential predictors chosen based on clinical grounds included all variables listed in Table 1. We defined comorbidities specified in Table 1 via ICD-9-CM secondary diagnosis codes and diagnosis related groups (DRGs) using Healthcare Cost and Utilization Project Comorbidity Software, version 3.7, based on the work of Elixhauser et al.^9,10; dialysis use was defined by an ICD-9-CM procedure code of 39.95; inpatient steroid use was defined by a hospital pharmacy charge for a systemic steroid in the 7 days preceding the sputum sample.

There were 230,635 hospitalizations of patients ≥18 years of age from January 2007 to July 2013. After excluding outside hospital transfers (n = 14,422), hospitalizations <48 hours in duration (n = 59,774), and psychiatric hospitalizations (n = 9887), there were 146,552 hospitalizations in the cohort.

To our knowledge, our study is the first to assess the predictors of positive sputum culture among patients with HAP (non-VAP) who had sputum samples obtained noninvasively, and this study is larger than prior studies in which researchers reported on sputum culture yield in HAP. Sputum samples were obtained in 29.4% cases of clinically diagnosed HAP. Although 87% of specimens obtained were culture-negative or contaminated, 13% yielded a bacterial organism. Although we do not report the antibiotic sensitivity patterns of the isolated organisms, the organisms identified frequently demonstrate antibiotic resistance, highlighting the potential for both antibiotic escalation and de-escalation based on sputum culture. In a multivariable model, presence of chronic lung disease and steroid use in the preceding week were both significantly associated with culture positivity.

The retrospective nature of the study raises the possibility of selection bias from systematic differences between the 29.4% of patients with HAP who had sputum collected and those who did not. Patients with sputum cultures were similar to patients without cultures in most measured characteristics, but we are unable to know what the yield of noninvasive sputum culture would have been had all patients with HAP been sampled. As such, our findings reflect the yield of sputum culture among patients with HAP for whom cultures were successfully obtained. It is not clear why only 29.4% of HAP patients received IDSA guideline-concordant care, but similar rates of culture use are reported elsewhere.⁷ While physician decision-making could have contributed to this finding, it is also possible that many sick, hospitalized patients are simply unable to produce sputum for analysis. In future studies, researchers should examine barriers to guideline-concordant care.

We considered a culture result of GNRs (not further speciated) as positive in our analysis because this result indicates growth of mixed bacterial types, the pathogenicity of which is a clinical determination. Physicians may request speciation and antibiotic sensitivities and, as such, these results have the potential to impact antibiotic choice. Had we considered such cultures to be negative or contaminated, the rate of culture positivity would have been only slightly reduced from 63/478 (13.2%) to 50/478 (10.5%).

The strengths of our study include the chart-based validation of administratively identified cases of pneumonia and a large cohort. There are also limitations. The single-center nature of the study has implications for pretest probability and generalizability. Additionally, in our study, we did not examine outcomes among patients treated empirically versus those treated based on sputum culture results. Finally, our reliance on administrative codes to identify cases of HAP for subsequent validation could have resulted in incomplete capture of HAP cases.

In conclusion, in our study, we provide an estimate of the diagnostic yield of sputum culture in a large cohort with chart-validated HAP, a description of HAP microbiology, and predictors of positive sputum culture. Thirteen percent of patients who had sputum culture testing received a microbiologic diagnosis. Because of the relative ease of obtaining a sputum sample and the microbiologic distribution in our study (representing a mix of commonly drug-resistant pathogens and more typical community-acquired pathogens), we suggest that sputum culture in HAP is a useful diagnostic tool with the potential to inform antibiotic escalation or de-escalation.

Acknowledgments

Dr. Herzig was funded by grant number K23AG042459 from the National Institute on Aging. Dr. Marcantonio was funded by grant number K24AG035075 from the National Institute on Aging. The funding organizations had no involvement in any aspect of the study, including design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Disclosure

No conflicts of interest apply for any of the authors.

Ascites is the most common complication of cirrhosis and often leads to hospitalization. ¹ Paracentesis is recommended for all patients admitted with ascites and cirrhosis. ¹ Additionally, the Society of Hospital Medicine considers the ability to perform paracenteses a core competency for hospitalists. ² Although considered a safe procedure, major bleeding complications occur in 0.2% to 1.7% of paracenteses. ^3-7 Patients with cirrhosis form new abdominal wall vessels because of portal hypertension, and hemoperitoneum from the laceration of these vessels during paracentesis carries a high morbidity and mortality. ^6,8 Ultrasound guidance using a low-frequency ultrasound probe is currently standard practice for paracentesis and has been shown to reduce bleeding complications. ^9-11 However, the use of vascular ultrasound (high-frequency probe) is also recommended to identify blood vessels within the intended needle pathway to reduce bleeding, but no studies have been performed to demonstrate a benefit. ^3,11 This study aimed to evaluate whether this “2-probe technique” reduces paracentesis-related bleeding complications.

The procedure service at Cedars Sinai Medical Center (CSMC) in Los Angeles performs paracentesis regularly with ultrasound guidance. CSMC is a tertiary care, academic medical center with 861 licensed beds. We performed a pre- to postintervention study of consecutive patients (admitted and ambulatory) who underwent paracentesis done by 1 proceduralist (MJA) from the procedure service at CSMC from February 2010 through February 2016. From February 1, 2010, through August 2011, paracenteses were performed using only low-frequency, phased array ultrasound probes (preintervention group). From September 1, 2011, through February 2016, a 2-probe technique was used, whereby ultrasound interrogation of the abdomen using a low-frequency, phased array probe (to identify ascites) was supplemented with a second scan using a high-frequency, linear probe to identify vasculature within the planned needle path (postintervention group). As a standard part of quality assurance, CSMC documented all paracentesis-related complications from procedures performed by their center. Northwestern University investigators (JHB, EC, JF) independently evaluated these data to look at bleeding complications before and after the implementation of the 2-probe technique. The CSMC and Northwestern University institutional review boards approved this study.

Procedure Protocol

Each patient’s primary team or outpatient physician requested a consultation for paracentesis from the CSMC procedure service. All patient evaluations began with an abdominal ultrasound using the low-frequency probe to determine the presence of ascites and a potential window of access to the fluid. After September 1, 2011, the CSMC procedure service implemented the 2-probe technique to also evaluate the abdominal wall for the presence of vessels. Color flow Doppler ultrasound further helped to differentiate blood vessels as necessary. The optimal window was then marked on the abdominal wall, and the paracentesis was performed. Per the routine of the CSMC procedure service, antiplatelet or anticoagulant medications were not held for paracenteses.

Measurement

All data were collected prospectively at the time of the procedure, including the volume of fluid removed, the number of needle passes required, and whether the patient was on antiplatelet or anticoagulant medications (including warfarin, direct oral anticoagulants, thrombin inhibitors, heparin, or low molecular weight heparins). Patients were followed for complications for up to 24 hours after the procedure or until a clinical question of a complication was reconciled. Minor bleeding was defined as new serosanguinous fluid on repeat paracentesis not associated with hemodynamic changes, local bruising or bleeding at the site, or abdominal wall hematoma. Major bleeding was defined by the development of hemodynamic instability or by reaccumulation of fluid on ultrasound within 24 hours postparacentesis and one of the following: an associated hemoglobin drop of greater than 2 g/dl, blood seen on repeat paracentesis, blood density fluid on a computed tomography scan, or the lack of an alternative explanation. All data were recorded in a handheld database (HanDbase; DDH Software, Wellington, FL).

A query of the electronic medical record was performed to obtain patient demographics and relevant clinical information, including age, sex, body mass index, International Normalized Ratio (INR), partial thromboplastin time (PTT), platelet counts (10³/uL, hematocrit (%) and creatinine (mg/dl). Our query for laboratory data retrieved the closest laboratory entry up to 48 hours before the procedure.

Statistical Analysis

We used a χ² test, Student t test, or Kruskal-Wallis test to compare demographic and clinical characteristics of procedure patients between the 2 study groups (pre- and postintervention). Major and minor bleeding were compared between the 2 groups using the χ² test.¹² We used the χ² test instead of the Fisher’s exact test for several reasons. The usual rule is that the Fisher’s exact test is necessary when 1 or more expected outcome values are less than 5. However, McDonald argues that the χ² test should be used with large sample sizes (more than 1000) in lieu of the outcome-value-of-5 rule.¹² The Fisher’s exact test also assumes that the row and column totals are fixed. However, the outcomes in our study were not fixed because any patient could have a bleeding complication during each procedure. When row and column totals are not fixed, only 5% of the time will a P value be less than 0.05, and the Fisher’s exact test is too conservative.¹² We performed all statistical analyses using IBM SPSS Statistics Version 22 (IBM Corp, Armonk, NY).

Patient demographic and clinical information can be found in the Table. The proceduralist (MJA) performed a total of 5777 paracenteses (1000 preintervention, 4777 postintervention) on 1639 patients. Four hundred eighty-nine (10.2%) vascular anomalies were identified within the intended needle path in the postintervention group (Figure). More patients in the preintervention group were on aspirin (93 [9.3%] vs 230 [4.8%]; P < 0.001) and therapeutic intravenous anticoagulants (33 [3.3%] vs 89 [1.9%]; P = 0.004), while more patients in the postintervention group were on both an antiplatelet and oral anticoagulant (1 [0.1%] vs 38 [0.8%]; P = 0.015) and subcutaneous prophylactic anticoagulants (184 [18.4%] vs 1120 [23.4%]; P = 0.001) at the time of the procedure. There were no other differences between groups with antiplatelet or anticoagulant drugs. We found no difference in minor bleeding between pre- and postintervention groups. Major bleeding was lower after the 2-probe technique was implemented (3 [0.3%] vs 4 [0.08%]; P = 0.07). There were no between-group differences in INR, PTT, or platelet counts among major bleeders. One patient in the postintervention group had hemodynamic instability and dropped his hemoglobin by 3.8 g/dl at 7 hours after the procedure. This was unexplained, as the patient had no abdominal symptoms or findings on examination. The patient received several liters of fluid before ultimately dying, and the primary team considered sepsis as a possible cause, but no postmortem examination was performed. This was the only death attributed to a major bleeding complication. We included this patient in our analysis because the cause of his demise was not completely clear. However, excluding this patient would change the results from a trend to a statistically significant difference between groups (3 [0.3%] vs 3 [0.06%]; P = 0.03).

To our knowledge, we report the largest series of paracentesis prospectively evaluated for bleeding complications, and this is the first study to evaluate whether adding a vascular ultrasound (high-frequency probe) avoids major bleeding. In our series, up to 10% of patients had abnormal vessels seen with a vascular ultrasound that were within the original intended trajectory path of the needle. These vessels were also likely present yet invisible when ultrasound-guided paracentesis using only the standard, low-frequency probe was being performed. It is unknown whether these vessels are routinely traversed with the needle, nicked, or narrowly avoided during paracenteses performed using only a low-frequency probe.

Procedure-related bleeding may not be completely avoidable, despite using the vascular probe. Some authors have suggested that the mechanism of bleeding is more related to the rapid reduction in intraperitoneal pressure, which increases the gradient across vessel walls, resulting in rupture and bleeding.⁶ However, in our series, using vascular ultrasound also reduced major bleeding to numbers lower than those historically reported in the literature (0.2%).^3-4 Our preintervention number needed to harm was 333 procedures to cause 1 major bleed, compared to 1250 (or 1666 using the 3-patient bleeding analysis) in the postintervention group. In 2008, 150,000 Medicare beneficiaries underwent paracentesis.¹³ Using our study analysis, if vascular ultrasound was used on these patients, up to 360 major bleeds may have been prevented, along with a corresponding reduction in unnecessary morbidity and mortality.

Our study has several limitations. First, it was limited to 1 center with 1 very experienced proceduralist. Although it is possible that the reduction in major bleeding may have been due to the increasing experience of the proceduralist over time, we do not think that this is likely because he had already performed thousands of paracenteses over 9 years before the start of our study. Second, major bleeding was rare and therefore precluded a multivariate analysis to control for temporal trends that might have occurred in our pre- to poststudy design. Statistically significant demographic and clinical variable differences between groups were likely not clinically meaningful. Although more patients were on intravenous anticoagulants in the preintervention group, coagulopathy or low platelets do not increase the bleeding risk during paracenteses,^1,8 and there was no clinical difference in INR, PTT, or platelets between groups (Table). Third, it is possible that unmeasured characteristics contributed to more patient complications in the preintervention group. Finally, we were unable to evaluate length of stay and mortality differences between groups that might have been attributable to the procedure because of the low number of major bleeding complications and the inability to perform a multivariate analysis.

Our results suggest that using the 2-probe technique to predetermine the needle path before performing paracentesis might prevent major bleeding. Based on our findings, we believe that the addition of a vascular ultrasound during paracentesis should be considered by all hospitalists.

Acknowledgments

The authors acknowledge Drs. Douglas Vaughan and Kevin O’Leary for their support and encouragement of this work. They would also like to thank the Cedars-Sinai Enterprise Information Systems Department for assistance with their data query.

Disclosure

The authors have no relevant financial disclosures or conflicts of interest to report.

Ascites is the most common complication of cirrhosis and often leads to hospitalization. ¹ Paracentesis is recommended for all patients admitted with ascites and cirrhosis. ¹ Additionally, the Society of Hospital Medicine considers the ability to perform paracenteses a core competency for hospitalists. ² Although considered a safe procedure, major bleeding complications occur in 0.2% to 1.7% of paracenteses. ^3-7 Patients with cirrhosis form new abdominal wall vessels because of portal hypertension, and hemoperitoneum from the laceration of these vessels during paracentesis carries a high morbidity and mortality. ^6,8 Ultrasound guidance using a low-frequency ultrasound probe is currently standard practice for paracentesis and has been shown to reduce bleeding complications. ^9-11 However, the use of vascular ultrasound (high-frequency probe) is also recommended to identify blood vessels within the intended needle pathway to reduce bleeding, but no studies have been performed to demonstrate a benefit. ^3,11 This study aimed to evaluate whether this “2-probe technique” reduces paracentesis-related bleeding complications.

The procedure service at Cedars Sinai Medical Center (CSMC) in Los Angeles performs paracentesis regularly with ultrasound guidance. CSMC is a tertiary care, academic medical center with 861 licensed beds. We performed a pre- to postintervention study of consecutive patients (admitted and ambulatory) who underwent paracentesis done by 1 proceduralist (MJA) from the procedure service at CSMC from February 2010 through February 2016. From February 1, 2010, through August 2011, paracenteses were performed using only low-frequency, phased array ultrasound probes (preintervention group). From September 1, 2011, through February 2016, a 2-probe technique was used, whereby ultrasound interrogation of the abdomen using a low-frequency, phased array probe (to identify ascites) was supplemented with a second scan using a high-frequency, linear probe to identify vasculature within the planned needle path (postintervention group). As a standard part of quality assurance, CSMC documented all paracentesis-related complications from procedures performed by their center. Northwestern University investigators (JHB, EC, JF) independently evaluated these data to look at bleeding complications before and after the implementation of the 2-probe technique. The CSMC and Northwestern University institutional review boards approved this study.

Procedure Protocol

Each patient’s primary team or outpatient physician requested a consultation for paracentesis from the CSMC procedure service. All patient evaluations began with an abdominal ultrasound using the low-frequency probe to determine the presence of ascites and a potential window of access to the fluid. After September 1, 2011, the CSMC procedure service implemented the 2-probe technique to also evaluate the abdominal wall for the presence of vessels. Color flow Doppler ultrasound further helped to differentiate blood vessels as necessary. The optimal window was then marked on the abdominal wall, and the paracentesis was performed. Per the routine of the CSMC procedure service, antiplatelet or anticoagulant medications were not held for paracenteses.

Measurement

All data were collected prospectively at the time of the procedure, including the volume of fluid removed, the number of needle passes required, and whether the patient was on antiplatelet or anticoagulant medications (including warfarin, direct oral anticoagulants, thrombin inhibitors, heparin, or low molecular weight heparins). Patients were followed for complications for up to 24 hours after the procedure or until a clinical question of a complication was reconciled. Minor bleeding was defined as new serosanguinous fluid on repeat paracentesis not associated with hemodynamic changes, local bruising or bleeding at the site, or abdominal wall hematoma. Major bleeding was defined by the development of hemodynamic instability or by reaccumulation of fluid on ultrasound within 24 hours postparacentesis and one of the following: an associated hemoglobin drop of greater than 2 g/dl, blood seen on repeat paracentesis, blood density fluid on a computed tomography scan, or the lack of an alternative explanation. All data were recorded in a handheld database (HanDbase; DDH Software, Wellington, FL).

A query of the electronic medical record was performed to obtain patient demographics and relevant clinical information, including age, sex, body mass index, International Normalized Ratio (INR), partial thromboplastin time (PTT), platelet counts (10³/uL, hematocrit (%) and creatinine (mg/dl). Our query for laboratory data retrieved the closest laboratory entry up to 48 hours before the procedure.

Statistical Analysis

We used a χ² test, Student t test, or Kruskal-Wallis test to compare demographic and clinical characteristics of procedure patients between the 2 study groups (pre- and postintervention). Major and minor bleeding were compared between the 2 groups using the χ² test.¹² We used the χ² test instead of the Fisher’s exact test for several reasons. The usual rule is that the Fisher’s exact test is necessary when 1 or more expected outcome values are less than 5. However, McDonald argues that the χ² test should be used with large sample sizes (more than 1000) in lieu of the outcome-value-of-5 rule.¹² The Fisher’s exact test also assumes that the row and column totals are fixed. However, the outcomes in our study were not fixed because any patient could have a bleeding complication during each procedure. When row and column totals are not fixed, only 5% of the time will a P value be less than 0.05, and the Fisher’s exact test is too conservative.¹² We performed all statistical analyses using IBM SPSS Statistics Version 22 (IBM Corp, Armonk, NY).

Patient demographic and clinical information can be found in the Table. The proceduralist (MJA) performed a total of 5777 paracenteses (1000 preintervention, 4777 postintervention) on 1639 patients. Four hundred eighty-nine (10.2%) vascular anomalies were identified within the intended needle path in the postintervention group (Figure). More patients in the preintervention group were on aspirin (93 [9.3%] vs 230 [4.8%]; P < 0.001) and therapeutic intravenous anticoagulants (33 [3.3%] vs 89 [1.9%]; P = 0.004), while more patients in the postintervention group were on both an antiplatelet and oral anticoagulant (1 [0.1%] vs 38 [0.8%]; P = 0.015) and subcutaneous prophylactic anticoagulants (184 [18.4%] vs 1120 [23.4%]; P = 0.001) at the time of the procedure. There were no other differences between groups with antiplatelet or anticoagulant drugs. We found no difference in minor bleeding between pre- and postintervention groups. Major bleeding was lower after the 2-probe technique was implemented (3 [0.3%] vs 4 [0.08%]; P = 0.07). There were no between-group differences in INR, PTT, or platelet counts among major bleeders. One patient in the postintervention group had hemodynamic instability and dropped his hemoglobin by 3.8 g/dl at 7 hours after the procedure. This was unexplained, as the patient had no abdominal symptoms or findings on examination. The patient received several liters of fluid before ultimately dying, and the primary team considered sepsis as a possible cause, but no postmortem examination was performed. This was the only death attributed to a major bleeding complication. We included this patient in our analysis because the cause of his demise was not completely clear. However, excluding this patient would change the results from a trend to a statistically significant difference between groups (3 [0.3%] vs 3 [0.06%]; P = 0.03).

To our knowledge, we report the largest series of paracentesis prospectively evaluated for bleeding complications, and this is the first study to evaluate whether adding a vascular ultrasound (high-frequency probe) avoids major bleeding. In our series, up to 10% of patients had abnormal vessels seen with a vascular ultrasound that were within the original intended trajectory path of the needle. These vessels were also likely present yet invisible when ultrasound-guided paracentesis using only the standard, low-frequency probe was being performed. It is unknown whether these vessels are routinely traversed with the needle, nicked, or narrowly avoided during paracenteses performed using only a low-frequency probe.

Procedure-related bleeding may not be completely avoidable, despite using the vascular probe. Some authors have suggested that the mechanism of bleeding is more related to the rapid reduction in intraperitoneal pressure, which increases the gradient across vessel walls, resulting in rupture and bleeding.⁶ However, in our series, using vascular ultrasound also reduced major bleeding to numbers lower than those historically reported in the literature (0.2%).^3-4 Our preintervention number needed to harm was 333 procedures to cause 1 major bleed, compared to 1250 (or 1666 using the 3-patient bleeding analysis) in the postintervention group. In 2008, 150,000 Medicare beneficiaries underwent paracentesis.¹³ Using our study analysis, if vascular ultrasound was used on these patients, up to 360 major bleeds may have been prevented, along with a corresponding reduction in unnecessary morbidity and mortality.

Our study has several limitations. First, it was limited to 1 center with 1 very experienced proceduralist. Although it is possible that the reduction in major bleeding may have been due to the increasing experience of the proceduralist over time, we do not think that this is likely because he had already performed thousands of paracenteses over 9 years before the start of our study. Second, major bleeding was rare and therefore precluded a multivariate analysis to control for temporal trends that might have occurred in our pre- to poststudy design. Statistically significant demographic and clinical variable differences between groups were likely not clinically meaningful. Although more patients were on intravenous anticoagulants in the preintervention group, coagulopathy or low platelets do not increase the bleeding risk during paracenteses,^1,8 and there was no clinical difference in INR, PTT, or platelets between groups (Table). Third, it is possible that unmeasured characteristics contributed to more patient complications in the preintervention group. Finally, we were unable to evaluate length of stay and mortality differences between groups that might have been attributable to the procedure because of the low number of major bleeding complications and the inability to perform a multivariate analysis.

Our results suggest that using the 2-probe technique to predetermine the needle path before performing paracentesis might prevent major bleeding. Based on our findings, we believe that the addition of a vascular ultrasound during paracentesis should be considered by all hospitalists.

Acknowledgments

The authors acknowledge Drs. Douglas Vaughan and Kevin O’Leary for their support and encouragement of this work. They would also like to thank the Cedars-Sinai Enterprise Information Systems Department for assistance with their data query.

Disclosure

The authors have no relevant financial disclosures or conflicts of interest to report.

Ascites is the most common complication of cirrhosis and often leads to hospitalization. ¹ Paracentesis is recommended for all patients admitted with ascites and cirrhosis. ¹ Additionally, the Society of Hospital Medicine considers the ability to perform paracenteses a core competency for hospitalists. ² Although considered a safe procedure, major bleeding complications occur in 0.2% to 1.7% of paracenteses. ^3-7 Patients with cirrhosis form new abdominal wall vessels because of portal hypertension, and hemoperitoneum from the laceration of these vessels during paracentesis carries a high morbidity and mortality. ^6,8 Ultrasound guidance using a low-frequency ultrasound probe is currently standard practice for paracentesis and has been shown to reduce bleeding complications. ^9-11 However, the use of vascular ultrasound (high-frequency probe) is also recommended to identify blood vessels within the intended needle pathway to reduce bleeding, but no studies have been performed to demonstrate a benefit. ^3,11 This study aimed to evaluate whether this “2-probe technique” reduces paracentesis-related bleeding complications.

The procedure service at Cedars Sinai Medical Center (CSMC) in Los Angeles performs paracentesis regularly with ultrasound guidance. CSMC is a tertiary care, academic medical center with 861 licensed beds. We performed a pre- to postintervention study of consecutive patients (admitted and ambulatory) who underwent paracentesis done by 1 proceduralist (MJA) from the procedure service at CSMC from February 2010 through February 2016. From February 1, 2010, through August 2011, paracenteses were performed using only low-frequency, phased array ultrasound probes (preintervention group). From September 1, 2011, through February 2016, a 2-probe technique was used, whereby ultrasound interrogation of the abdomen using a low-frequency, phased array probe (to identify ascites) was supplemented with a second scan using a high-frequency, linear probe to identify vasculature within the planned needle path (postintervention group). As a standard part of quality assurance, CSMC documented all paracentesis-related complications from procedures performed by their center. Northwestern University investigators (JHB, EC, JF) independently evaluated these data to look at bleeding complications before and after the implementation of the 2-probe technique. The CSMC and Northwestern University institutional review boards approved this study.

Procedure Protocol

Each patient’s primary team or outpatient physician requested a consultation for paracentesis from the CSMC procedure service. All patient evaluations began with an abdominal ultrasound using the low-frequency probe to determine the presence of ascites and a potential window of access to the fluid. After September 1, 2011, the CSMC procedure service implemented the 2-probe technique to also evaluate the abdominal wall for the presence of vessels. Color flow Doppler ultrasound further helped to differentiate blood vessels as necessary. The optimal window was then marked on the abdominal wall, and the paracentesis was performed. Per the routine of the CSMC procedure service, antiplatelet or anticoagulant medications were not held for paracenteses.

Measurement

All data were collected prospectively at the time of the procedure, including the volume of fluid removed, the number of needle passes required, and whether the patient was on antiplatelet or anticoagulant medications (including warfarin, direct oral anticoagulants, thrombin inhibitors, heparin, or low molecular weight heparins). Patients were followed for complications for up to 24 hours after the procedure or until a clinical question of a complication was reconciled. Minor bleeding was defined as new serosanguinous fluid on repeat paracentesis not associated with hemodynamic changes, local bruising or bleeding at the site, or abdominal wall hematoma. Major bleeding was defined by the development of hemodynamic instability or by reaccumulation of fluid on ultrasound within 24 hours postparacentesis and one of the following: an associated hemoglobin drop of greater than 2 g/dl, blood seen on repeat paracentesis, blood density fluid on a computed tomography scan, or the lack of an alternative explanation. All data were recorded in a handheld database (HanDbase; DDH Software, Wellington, FL).

A query of the electronic medical record was performed to obtain patient demographics and relevant clinical information, including age, sex, body mass index, International Normalized Ratio (INR), partial thromboplastin time (PTT), platelet counts (10³/uL, hematocrit (%) and creatinine (mg/dl). Our query for laboratory data retrieved the closest laboratory entry up to 48 hours before the procedure.

Statistical Analysis

We used a χ² test, Student t test, or Kruskal-Wallis test to compare demographic and clinical characteristics of procedure patients between the 2 study groups (pre- and postintervention). Major and minor bleeding were compared between the 2 groups using the χ² test.¹² We used the χ² test instead of the Fisher’s exact test for several reasons. The usual rule is that the Fisher’s exact test is necessary when 1 or more expected outcome values are less than 5. However, McDonald argues that the χ² test should be used with large sample sizes (more than 1000) in lieu of the outcome-value-of-5 rule.¹² The Fisher’s exact test also assumes that the row and column totals are fixed. However, the outcomes in our study were not fixed because any patient could have a bleeding complication during each procedure. When row and column totals are not fixed, only 5% of the time will a P value be less than 0.05, and the Fisher’s exact test is too conservative.¹² We performed all statistical analyses using IBM SPSS Statistics Version 22 (IBM Corp, Armonk, NY).

Patient demographic and clinical information can be found in the Table. The proceduralist (MJA) performed a total of 5777 paracenteses (1000 preintervention, 4777 postintervention) on 1639 patients. Four hundred eighty-nine (10.2%) vascular anomalies were identified within the intended needle path in the postintervention group (Figure). More patients in the preintervention group were on aspirin (93 [9.3%] vs 230 [4.8%]; P < 0.001) and therapeutic intravenous anticoagulants (33 [3.3%] vs 89 [1.9%]; P = 0.004), while more patients in the postintervention group were on both an antiplatelet and oral anticoagulant (1 [0.1%] vs 38 [0.8%]; P = 0.015) and subcutaneous prophylactic anticoagulants (184 [18.4%] vs 1120 [23.4%]; P = 0.001) at the time of the procedure. There were no other differences between groups with antiplatelet or anticoagulant drugs. We found no difference in minor bleeding between pre- and postintervention groups. Major bleeding was lower after the 2-probe technique was implemented (3 [0.3%] vs 4 [0.08%]; P = 0.07). There were no between-group differences in INR, PTT, or platelet counts among major bleeders. One patient in the postintervention group had hemodynamic instability and dropped his hemoglobin by 3.8 g/dl at 7 hours after the procedure. This was unexplained, as the patient had no abdominal symptoms or findings on examination. The patient received several liters of fluid before ultimately dying, and the primary team considered sepsis as a possible cause, but no postmortem examination was performed. This was the only death attributed to a major bleeding complication. We included this patient in our analysis because the cause of his demise was not completely clear. However, excluding this patient would change the results from a trend to a statistically significant difference between groups (3 [0.3%] vs 3 [0.06%]; P = 0.03).

To our knowledge, we report the largest series of paracentesis prospectively evaluated for bleeding complications, and this is the first study to evaluate whether adding a vascular ultrasound (high-frequency probe) avoids major bleeding. In our series, up to 10% of patients had abnormal vessels seen with a vascular ultrasound that were within the original intended trajectory path of the needle. These vessels were also likely present yet invisible when ultrasound-guided paracentesis using only the standard, low-frequency probe was being performed. It is unknown whether these vessels are routinely traversed with the needle, nicked, or narrowly avoided during paracenteses performed using only a low-frequency probe.

Procedure-related bleeding may not be completely avoidable, despite using the vascular probe. Some authors have suggested that the mechanism of bleeding is more related to the rapid reduction in intraperitoneal pressure, which increases the gradient across vessel walls, resulting in rupture and bleeding.⁶ However, in our series, using vascular ultrasound also reduced major bleeding to numbers lower than those historically reported in the literature (0.2%).^3-4 Our preintervention number needed to harm was 333 procedures to cause 1 major bleed, compared to 1250 (or 1666 using the 3-patient bleeding analysis) in the postintervention group. In 2008, 150,000 Medicare beneficiaries underwent paracentesis.¹³ Using our study analysis, if vascular ultrasound was used on these patients, up to 360 major bleeds may have been prevented, along with a corresponding reduction in unnecessary morbidity and mortality.

Our study has several limitations. First, it was limited to 1 center with 1 very experienced proceduralist. Although it is possible that the reduction in major bleeding may have been due to the increasing experience of the proceduralist over time, we do not think that this is likely because he had already performed thousands of paracenteses over 9 years before the start of our study. Second, major bleeding was rare and therefore precluded a multivariate analysis to control for temporal trends that might have occurred in our pre- to poststudy design. Statistically significant demographic and clinical variable differences between groups were likely not clinically meaningful. Although more patients were on intravenous anticoagulants in the preintervention group, coagulopathy or low platelets do not increase the bleeding risk during paracenteses,^1,8 and there was no clinical difference in INR, PTT, or platelets between groups (Table). Third, it is possible that unmeasured characteristics contributed to more patient complications in the preintervention group. Finally, we were unable to evaluate length of stay and mortality differences between groups that might have been attributable to the procedure because of the low number of major bleeding complications and the inability to perform a multivariate analysis.

Our results suggest that using the 2-probe technique to predetermine the needle path before performing paracentesis might prevent major bleeding. Based on our findings, we believe that the addition of a vascular ultrasound during paracentesis should be considered by all hospitalists.

Acknowledgments

The authors acknowledge Drs. Douglas Vaughan and Kevin O’Leary for their support and encouragement of this work. They would also like to thank the Cedars-Sinai Enterprise Information Systems Department for assistance with their data query.

Disclosure

The authors have no relevant financial disclosures or conflicts of interest to report.

The value placed on bedside clinical observation in the decision-making process of a patient’s illness has been diminished by today’s armamentarium of sophisticated technology. Increasing reliance is now placed on the result of nonspecific tests in preference to bedside clinical judgement in the diagnostic and management process. While diagnostic investigations have undoubtedly provided great advancements in medical care, they come at time and financial costs. Physicians should therefore continue to be encouraged to make clinical decisions based on their bedside assessment.

With hospital overcrowding a significant problem within the healthcare system and the expectation that it will worsen with an ageing population, identifying factors that predict patient suitability for discharge has become an important focus for clinicians.^1,2 There exists a paucity of literature predicting discharge suitability of general surgical patients admitted through the emergency department (ED). Furthermore, despite the extensive research into the effectiveness of discharge planning,³ little research has been conducted to describe positive predictive indicators for discharge. Observations made during surgical rounds have led the authors to consider that individuals who are using a smartphone during their bedside assessment may be clinically well enough for discharge.

The aim of this study was to assess whether the clinical assessment of an acute surgical patient could be usefully augmented by the observation of the active use of smartphones (the smartphone sign) and whether this could be used as a surrogate marker to indicate a patient’s well-being and suitability for same-day discharge from the hospital in acute surgical patients.

Design and Setting

This was a prospective observational study performed over 2 periods at a tertiary hospital in South Australia, Australia. At our institution, acute surgical patients are admitted to the acute surgical unit (ASU) from the ED by junior surgical doctors. Patients are then reviewed by the on-call surgical consultant, who implements management plans or advises discharge on 2 occasions per day.

Participants

All patients admitted under the ASU were considered eligible for the study. Exclusion criteria included patients that (i) required immediate surgical intervention (defined as time of review to theatre of less than 4 hours) and (ii) had immediate admission to the intensive care unit.

Consultant surgeons are employed within a general surgical subspecialty, including upper gastrointestinal, hepatobiliary, breast and endocrine, and colorectal. All surgeons from each team partake in the general surgery on-call roster. Each surgeon was included at least once within the observation periods. Experience of consultant surgeons ranged from 5 years of postfellowship experience to surgeons with more than 30 years of experience, with the majority having more than 10 years of postfellowship experience.

Patients were stratified into 2 distinct cohorts upon consultant review: smartphone positive (spP) was defined as a patient who was using a smartphone or who had their phone on their bed; a patient was classified as smartphone negative (spN) if they did not fulfil these criteria. The presence or absence of a smartphone was recorded by the authors, who were present on consultant ward rounds but not involved in the decision-making process of patient care. In order to minimize bias, only 1 surgeon (PGD) was aware that the study was being conducted and all patients were blinded to the study. Additional information that was collected included patient demographics, requirement for surgery, and length of stay (LOS). A patient who was discharged on the same day as the consultant review was considered to be discharged on day 1, all other patients were considered to have LOS greater than 1 day. Requirement for surgery was defined as a patient who underwent a surgical procedure in an operating suite. Thirty-day unplanned readmission rates for all patients were examined. Readmission to another public hospital within the state was also included within the readmission data.

Observation Periods

An initial 4-week pilot study was conducted to assess for a possible association between spP and same-day discharge. A second 8-week study period was undertaken 1 year later accounting for the employment of the authors at the study’s institution. Unless stated, the results described are the accumulation of both study periods.

Statistical Analysis

As this is the first study of its kind, no prior estimates of numbers were known. After 2 weeks of data collection, data were analyzed in order to provide an estimate of the total number of patients required to provide a statistically valid result (α = 0.05; power = 0.80). Sample size was calculated to be 40 subjects. It was agreed that in order to make the study as robust as possible, data should be collected for the 2 observation periods.

Demographic data are presented as means with standard deviations (SDs) or frequencies with percentages. A 2-sample Student t test was used to compare the age of spP and spN patients. A χ² test and logistic regressions were used to assess the association between smartphone status and patient demographics, LOS, and requirement for surgery. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A P value of <0.05 was considered significant. All data were analyzed by using R 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria).

During the 2 observation periods, a total of 227 eligible surgical admissions were observed with complete data for 221 patients. Six patients were excluded as their smartphone status was not recorded. The study sample represents our population of interest within an ASU, and we had complete data for 97.4% of participants with a 100% follow-up. There was no significant effect of study between the 2 observation periods (χ² = 140.19; P = 0.10). The mean age of patients was 50.24 years. Further demographic data are presented in Table 1. Twenty-five (11.3%) patients were spP and 196 (88.7%) were spN. Fifty-two (23.5%) patients were discharged home on day 1, and 169 (76.5%) had admissions longer than 1 day (see Figure). Sixty (27%) patients underwent surgery during their admission. Twenty-two patients had unplanned readmissions; only 1 of these patients had been observed to be spP.

There was a statistically significant difference in ages between the spP and spN groups (t = 8.40; P < 0.0005), with the average age of spP patients being 31.84 years compared with 52.58 years for spN patients. There was no statistical difference between gender and smartphone status (χ² = 1.78; P = 0.18; Table 2).

For those patients discharged home on day 1, there was a statistically significant association with being spP (χ² = 14.55, P = 0.0001). Patients who were spP were 5.29 times more likely to be discharged on day 1 (95% CI, 2.24-12.84). Of the variables analyzed, only gender failed to demonstrate an effect on discharge home on day 1 (Table 3). Overall, the presence of a smartphone was found to have a sensitivity of 56.0% (95% CI, 34.93-75.60) and a specificity of 80.6% (95% CI, 74.37-85.90) in regard to same-day discharge. However, it was found to have a negative predictive value of 93.49% (95% CI, 88.65-96.71).

When examining readmission rates, only 4% of spP patients were readmitted versus 10.7% of spN patients. Accounting for variables, spP patients were 4 times less likely to be readmitted, though this was not statistically significant (OR 4.02; 95% CI, 0.43-37.2; P = 0.22). Furthermore, when examining only those patients discharged on day 1, smartphone status was not a predictor of readmission (OR 0.94; 95% CI, 0.06-15.2; P = 0 .97).

To mitigate the effect of age, analysis was conducted excluding those aged over 55 years (the previous retirement age in Australia), leaving 131 patients for analysis. The average age of spP patients was 31.8 years (SD 10.0) compared with 36.7 years (SD 10.9) for spN patients, representing a significant difference (t = 2.14; P = 0.04); 51.1% of patients were male, 19.1% of patients were spP, 26.0% of patients proceeded to an operation, the oldest spP was 51 years, and 29.0% of patients were discharged home on day 1. There was no difference in gender and smartphone status (χ² = 0.33; P = 0.6). When analyzing those discharged on day 1, again spP patients were more likely to be discharged home (χ² = 9.4; P = 0.002), and spP patients were 3.6 times more likely to be discharged home on day 1.

There were 4 spP patients who underwent an operation. Two patients had an incision and drainage of a perianal abscess, 1 patient underwent a laparotomy for an internal hernia after recently undergoing a Roux-en-Y gastric bypass at another hospital, and the final patient underwent a laparoscopic appendicectomy. One of these patients was still discharged home on day 1.

As J. A. Lindsay⁴ said, “For one mistake made for not knowing, ten mistakes are made for not looking.” At medical school, we are taught the finer techniques of the physical examination in order to support our diagnosis made from the history. It is not until we are experienced clinicians do we develop the clinical acumen and ability to tell an unwell patient from a well patient at a glance—colloquially known as the “end of the bed” assessment. In the pretechnology era, a well patient could frequently be seen reading their book, eg, the “novel-sign.” With the advent of the smartphone and electronic devices upon which novels can be read, statuses updated, and locations “checked into” (ie, the modern “vital signs”), the book sign may be a thing of the past. However, the ability for the clinician to assess a patient’s wellness is still crucial, and the value of any additional “physical signs” need to be estimated.

We observed a cohort of patients through a busy ASU in a tertiary hospital in South Australia, Australia. Acute surgical patients admitted to the hospital who were observed to be on their phones upon consultant review were more than 5 times likely to be discharged that same day. To the best of our knowledge, this is the first study to prospectively collect data to assess a frequently used but unevaluated clinical observation.

The use of a smartphone can tell us a lot about an individual’s physiology. We can assume the individual’s airway and breathing are adequate, allowing enough oxygen to reach the lungs and subsequently circulate. The individual is usually sitting up in bed and thus has an adequate blood pressure and blood oxygenation that can maintain cerebral perfusion. They have the cognitive and cerebral processing in place to function the device, and we can examine their cerebellar function by looking for fine-motor movements.

Mobile phone ownership is pervasive within Australia,⁵ with a conservative estimated 85.7% of the population (20.57 million people of a total population of approximately 24 million) owning a mobile phone and an estimated 50% to 79% of mobile phone ownership being of a smartphone.^6,7 This ownership is not just limited to the young, with 74% of Australians over 65 owning or using a mobile phone.⁸ Despite this high phone ownership among those over 65, it is still significantly less than their younger counterparts and may be one reason for the absence of spP in those older than 51 years. A key point in the study is that overall phone ownership was not known, and, thus, it is not possible to determine the proportion of spN patients who were negative because they did not own a phone. However, based on general population data, the incidence of spP patients was well below that seen in the community (11.3%)⁵ and even when excluding those over 55, the percentage of spP patients only rose to 19.1%. Unsurprisingly, increasing age was associated with a decreased likelihood of being spP (P < 0.0005), as younger people are more likely to own a phone.⁸ There was no association with gender (P = 0.18). There are a number of explanations that may explain the lower than expected percentage of spP patients, including the inability for the patient to gather their possessions during a medical emergency, patients storing their phones prior to doctor review (72%-85% of Australians report talking on phones in public places to be rude or intrusive⁵), but more importantly, that our hypothesis that patients were too unwell to use their device appears to hold true.

There are potential alternate reasons other than smartphone status that may account for patients being discharged home on day 1. While there was no association seen with gender, the need for an operation prolonged a patient’s stay (OR 1.64; 95% CI, 0.046-0.46), and there was a trend seen with increasing age (OR 0.98; 95% CI, 0.96-1.00). Neither of these 2 demographics are unsurprising: increasing age is associated with increasing medical comorbidities and thus complexity; even the simplest of operations require a postprocedure observation period, automatically increasing their LOS. Additionally, measured demographics are limited and there may be further unmeasured reasons that account for earlier discharge.

The other key component to this study is the value of the physical examination, albeit only assessing 1 component: the general inspection. In their review of the value of the physical examination of the cardiovascular system, Elder et al. highlight an important point: in traditional teaching, the value of a physical sign is compared with a diagnostic reference, typically imaging or an invasive test.⁹ They argue that this definition undervalues the physical examination and list other values aside from accuracy including accessibility, contribution to clinical care beyond diagnoses, cost effectiveness, patients’ safety, patients’ perceptions, and pedagogic value; and they argue that the physical examination should always be considered in regard to the clinical context—in this case, the newly admitted general surgical patient.

The assessment of the presence or absence of a smartphone is readily performed upon general inspection and is easily visible; general inspection of the patient and failure to observe the clinical sign when present are 2 of the greatest errors associated with physical examination.¹⁰ Furthermore, given its unique status as a physical sign, the authors’ opinion and experience is that it is readily teachable. McGee states, “…a fundamental lesson [in regards to teaching] is that the diagnosis of many clinical problems, despite modern testing, still depends primarily on what the clinician sees, hears, and feels.”¹¹ In their article, Paley et al. found that more than 80% of patients admitted from the ED under internal medicine could be accurately diagnosed based largely on history and examination alone and concluded that basic clinical skills are sufficient for achieving an accurate diagnosis in most cases.¹² Although Paley et al. were assisted with basic tests (such as electrocardiogram and basic haematological and biochemistry results), the point of clinical skills is not lost. Furthermore, this assessment was made in a group of patients generally considered to be complex in contrast to the “standard” appendicitis or cholecystitis patient that makes up a significant proportion of general surgical patients.

There are a number of limitations to this study, however, including smartphones that may have been missed during the observational period. Potential confounding variables such as socioeconomic status and the overall smartphone ownership of our subjects were not known. We did not ask all admitted patients whether they owned a phone or whether they had a phone in their possession. Knowledge of those who owned phones but were not in possession of them could strengthen our argument that spN patients were not using their phone because they were unwell, rather than just not having access to it.

However, this study has a number of strengths, including a large sample size and data that were prospectively collected by a method and in a setting that was the same for all participants. Clear and appropriate definitions were used, which minimizes misclassification bias. Participants and decision makers were blinded to the study, and potentially confounding variables such as age and sex were accounted for.

Assessing the suitability for discharge from the hospital is a decision encountered by hospital-based clinicians every day. These skills are not taught, but are rather learned as a junior doctor acquires experience. It is unlikely that protocols will be developed to aid identification of potential discharges from an acute surgical ward; acute surgical conditions are too varied and dynamic to be able to pool all data. We continue to rely on our own and fellow colleagues’ (doctors, nurses, and other staff) input and assessment. However, our study has shown that it is possible to identify and quantify clinical findings that are already regularly used, albeit potentially subconsciously, to assess suitability for discharge. We have shown in this large, prospectively collected observational study that if a surgical patient is seen using their electronic device, they are more likely to be safe to go home. Thus, surgeons can reliably use this observation as a trigger to consider discharging the patient following a more thorough assessment.

While these observations might appear to be rather a simplistic way of trying to quantify whether or not a patient is fit for discharge, any clues that hint towards a patient’s well-being should be taken into account when making an overall assessment. The active use of a smartphone is one such measure.

Acknowledgments

The authors thank Emma Knight and Nancy Briggs from the Data Management & Analysis Centre, Discipline of Public Health, University of Adelaide.

Disclosure

No author nor the institution received any payment or services from a third party for any aspect of the submitted work and report no conflict of interest. There are no reported financial relationships with any entities by any of the authors. There are no patents pending based upon this publication. There are no relationships or activities that readers could perceive to have influenced, or give the appearance of influencing, the submitted work. The corresponding author is not in receipt of a research scholarship. The paper is not based on a previous communication.

The value placed on bedside clinical observation in the decision-making process of a patient’s illness has been diminished by today’s armamentarium of sophisticated technology. Increasing reliance is now placed on the result of nonspecific tests in preference to bedside clinical judgement in the diagnostic and management process. While diagnostic investigations have undoubtedly provided great advancements in medical care, they come at time and financial costs. Physicians should therefore continue to be encouraged to make clinical decisions based on their bedside assessment.

With hospital overcrowding a significant problem within the healthcare system and the expectation that it will worsen with an ageing population, identifying factors that predict patient suitability for discharge has become an important focus for clinicians.^1,2 There exists a paucity of literature predicting discharge suitability of general surgical patients admitted through the emergency department (ED). Furthermore, despite the extensive research into the effectiveness of discharge planning,³ little research has been conducted to describe positive predictive indicators for discharge. Observations made during surgical rounds have led the authors to consider that individuals who are using a smartphone during their bedside assessment may be clinically well enough for discharge.

The aim of this study was to assess whether the clinical assessment of an acute surgical patient could be usefully augmented by the observation of the active use of smartphones (the smartphone sign) and whether this could be used as a surrogate marker to indicate a patient’s well-being and suitability for same-day discharge from the hospital in acute surgical patients.

Design and Setting

This was a prospective observational study performed over 2 periods at a tertiary hospital in South Australia, Australia. At our institution, acute surgical patients are admitted to the acute surgical unit (ASU) from the ED by junior surgical doctors. Patients are then reviewed by the on-call surgical consultant, who implements management plans or advises discharge on 2 occasions per day.

Participants

All patients admitted under the ASU were considered eligible for the study. Exclusion criteria included patients that (i) required immediate surgical intervention (defined as time of review to theatre of less than 4 hours) and (ii) had immediate admission to the intensive care unit.

Consultant surgeons are employed within a general surgical subspecialty, including upper gastrointestinal, hepatobiliary, breast and endocrine, and colorectal. All surgeons from each team partake in the general surgery on-call roster. Each surgeon was included at least once within the observation periods. Experience of consultant surgeons ranged from 5 years of postfellowship experience to surgeons with more than 30 years of experience, with the majority having more than 10 years of postfellowship experience.

Patients were stratified into 2 distinct cohorts upon consultant review: smartphone positive (spP) was defined as a patient who was using a smartphone or who had their phone on their bed; a patient was classified as smartphone negative (spN) if they did not fulfil these criteria. The presence or absence of a smartphone was recorded by the authors, who were present on consultant ward rounds but not involved in the decision-making process of patient care. In order to minimize bias, only 1 surgeon (PGD) was aware that the study was being conducted and all patients were blinded to the study. Additional information that was collected included patient demographics, requirement for surgery, and length of stay (LOS). A patient who was discharged on the same day as the consultant review was considered to be discharged on day 1, all other patients were considered to have LOS greater than 1 day. Requirement for surgery was defined as a patient who underwent a surgical procedure in an operating suite. Thirty-day unplanned readmission rates for all patients were examined. Readmission to another public hospital within the state was also included within the readmission data.

Observation Periods

An initial 4-week pilot study was conducted to assess for a possible association between spP and same-day discharge. A second 8-week study period was undertaken 1 year later accounting for the employment of the authors at the study’s institution. Unless stated, the results described are the accumulation of both study periods.

Statistical Analysis

As this is the first study of its kind, no prior estimates of numbers were known. After 2 weeks of data collection, data were analyzed in order to provide an estimate of the total number of patients required to provide a statistically valid result (α = 0.05; power = 0.80). Sample size was calculated to be 40 subjects. It was agreed that in order to make the study as robust as possible, data should be collected for the 2 observation periods.

Demographic data are presented as means with standard deviations (SDs) or frequencies with percentages. A 2-sample Student t test was used to compare the age of spP and spN patients. A χ² test and logistic regressions were used to assess the association between smartphone status and patient demographics, LOS, and requirement for surgery. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A P value of <0.05 was considered significant. All data were analyzed by using R 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria).

During the 2 observation periods, a total of 227 eligible surgical admissions were observed with complete data for 221 patients. Six patients were excluded as their smartphone status was not recorded. The study sample represents our population of interest within an ASU, and we had complete data for 97.4% of participants with a 100% follow-up. There was no significant effect of study between the 2 observation periods (χ² = 140.19; P = 0.10). The mean age of patients was 50.24 years. Further demographic data are presented in Table 1. Twenty-five (11.3%) patients were spP and 196 (88.7%) were spN. Fifty-two (23.5%) patients were discharged home on day 1, and 169 (76.5%) had admissions longer than 1 day (see Figure). Sixty (27%) patients underwent surgery during their admission. Twenty-two patients had unplanned readmissions; only 1 of these patients had been observed to be spP.

There was a statistically significant difference in ages between the spP and spN groups (t = 8.40; P < 0.0005), with the average age of spP patients being 31.84 years compared with 52.58 years for spN patients. There was no statistical difference between gender and smartphone status (χ² = 1.78; P = 0.18; Table 2).

For those patients discharged home on day 1, there was a statistically significant association with being spP (χ² = 14.55, P = 0.0001). Patients who were spP were 5.29 times more likely to be discharged on day 1 (95% CI, 2.24-12.84). Of the variables analyzed, only gender failed to demonstrate an effect on discharge home on day 1 (Table 3). Overall, the presence of a smartphone was found to have a sensitivity of 56.0% (95% CI, 34.93-75.60) and a specificity of 80.6% (95% CI, 74.37-85.90) in regard to same-day discharge. However, it was found to have a negative predictive value of 93.49% (95% CI, 88.65-96.71).

When examining readmission rates, only 4% of spP patients were readmitted versus 10.7% of spN patients. Accounting for variables, spP patients were 4 times less likely to be readmitted, though this was not statistically significant (OR 4.02; 95% CI, 0.43-37.2; P = 0.22). Furthermore, when examining only those patients discharged on day 1, smartphone status was not a predictor of readmission (OR 0.94; 95% CI, 0.06-15.2; P = 0 .97).

To mitigate the effect of age, analysis was conducted excluding those aged over 55 years (the previous retirement age in Australia), leaving 131 patients for analysis. The average age of spP patients was 31.8 years (SD 10.0) compared with 36.7 years (SD 10.9) for spN patients, representing a significant difference (t = 2.14; P = 0.04); 51.1% of patients were male, 19.1% of patients were spP, 26.0% of patients proceeded to an operation, the oldest spP was 51 years, and 29.0% of patients were discharged home on day 1. There was no difference in gender and smartphone status (χ² = 0.33; P = 0.6). When analyzing those discharged on day 1, again spP patients were more likely to be discharged home (χ² = 9.4; P = 0.002), and spP patients were 3.6 times more likely to be discharged home on day 1.

There were 4 spP patients who underwent an operation. Two patients had an incision and drainage of a perianal abscess, 1 patient underwent a laparotomy for an internal hernia after recently undergoing a Roux-en-Y gastric bypass at another hospital, and the final patient underwent a laparoscopic appendicectomy. One of these patients was still discharged home on day 1.

As J. A. Lindsay⁴ said, “For one mistake made for not knowing, ten mistakes are made for not looking.” At medical school, we are taught the finer techniques of the physical examination in order to support our diagnosis made from the history. It is not until we are experienced clinicians do we develop the clinical acumen and ability to tell an unwell patient from a well patient at a glance—colloquially known as the “end of the bed” assessment. In the pretechnology era, a well patient could frequently be seen reading their book, eg, the “novel-sign.” With the advent of the smartphone and electronic devices upon which novels can be read, statuses updated, and locations “checked into” (ie, the modern “vital signs”), the book sign may be a thing of the past. However, the ability for the clinician to assess a patient’s wellness is still crucial, and the value of any additional “physical signs” need to be estimated.

We observed a cohort of patients through a busy ASU in a tertiary hospital in South Australia, Australia. Acute surgical patients admitted to the hospital who were observed to be on their phones upon consultant review were more than 5 times likely to be discharged that same day. To the best of our knowledge, this is the first study to prospectively collect data to assess a frequently used but unevaluated clinical observation.

The use of a smartphone can tell us a lot about an individual’s physiology. We can assume the individual’s airway and breathing are adequate, allowing enough oxygen to reach the lungs and subsequently circulate. The individual is usually sitting up in bed and thus has an adequate blood pressure and blood oxygenation that can maintain cerebral perfusion. They have the cognitive and cerebral processing in place to function the device, and we can examine their cerebellar function by looking for fine-motor movements.

Mobile phone ownership is pervasive within Australia,⁵ with a conservative estimated 85.7% of the population (20.57 million people of a total population of approximately 24 million) owning a mobile phone and an estimated 50% to 79% of mobile phone ownership being of a smartphone.^6,7 This ownership is not just limited to the young, with 74% of Australians over 65 owning or using a mobile phone.⁸ Despite this high phone ownership among those over 65, it is still significantly less than their younger counterparts and may be one reason for the absence of spP in those older than 51 years. A key point in the study is that overall phone ownership was not known, and, thus, it is not possible to determine the proportion of spN patients who were negative because they did not own a phone. However, based on general population data, the incidence of spP patients was well below that seen in the community (11.3%)⁵ and even when excluding those over 55, the percentage of spP patients only rose to 19.1%. Unsurprisingly, increasing age was associated with a decreased likelihood of being spP (P < 0.0005), as younger people are more likely to own a phone.⁸ There was no association with gender (P = 0.18). There are a number of explanations that may explain the lower than expected percentage of spP patients, including the inability for the patient to gather their possessions during a medical emergency, patients storing their phones prior to doctor review (72%-85% of Australians report talking on phones in public places to be rude or intrusive⁵), but more importantly, that our hypothesis that patients were too unwell to use their device appears to hold true.

There are potential alternate reasons other than smartphone status that may account for patients being discharged home on day 1. While there was no association seen with gender, the need for an operation prolonged a patient’s stay (OR 1.64; 95% CI, 0.046-0.46), and there was a trend seen with increasing age (OR 0.98; 95% CI, 0.96-1.00). Neither of these 2 demographics are unsurprising: increasing age is associated with increasing medical comorbidities and thus complexity; even the simplest of operations require a postprocedure observation period, automatically increasing their LOS. Additionally, measured demographics are limited and there may be further unmeasured reasons that account for earlier discharge.

The other key component to this study is the value of the physical examination, albeit only assessing 1 component: the general inspection. In their review of the value of the physical examination of the cardiovascular system, Elder et al. highlight an important point: in traditional teaching, the value of a physical sign is compared with a diagnostic reference, typically imaging or an invasive test.⁹ They argue that this definition undervalues the physical examination and list other values aside from accuracy including accessibility, contribution to clinical care beyond diagnoses, cost effectiveness, patients’ safety, patients’ perceptions, and pedagogic value; and they argue that the physical examination should always be considered in regard to the clinical context—in this case, the newly admitted general surgical patient.

The assessment of the presence or absence of a smartphone is readily performed upon general inspection and is easily visible; general inspection of the patient and failure to observe the clinical sign when present are 2 of the greatest errors associated with physical examination.¹⁰ Furthermore, given its unique status as a physical sign, the authors’ opinion and experience is that it is readily teachable. McGee states, “…a fundamental lesson [in regards to teaching] is that the diagnosis of many clinical problems, despite modern testing, still depends primarily on what the clinician sees, hears, and feels.”¹¹ In their article, Paley et al. found that more than 80% of patients admitted from the ED under internal medicine could be accurately diagnosed based largely on history and examination alone and concluded that basic clinical skills are sufficient for achieving an accurate diagnosis in most cases.¹² Although Paley et al. were assisted with basic tests (such as electrocardiogram and basic haematological and biochemistry results), the point of clinical skills is not lost. Furthermore, this assessment was made in a group of patients generally considered to be complex in contrast to the “standard” appendicitis or cholecystitis patient that makes up a significant proportion of general surgical patients.

There are a number of limitations to this study, however, including smartphones that may have been missed during the observational period. Potential confounding variables such as socioeconomic status and the overall smartphone ownership of our subjects were not known. We did not ask all admitted patients whether they owned a phone or whether they had a phone in their possession. Knowledge of those who owned phones but were not in possession of them could strengthen our argument that spN patients were not using their phone because they were unwell, rather than just not having access to it.

However, this study has a number of strengths, including a large sample size and data that were prospectively collected by a method and in a setting that was the same for all participants. Clear and appropriate definitions were used, which minimizes misclassification bias. Participants and decision makers were blinded to the study, and potentially confounding variables such as age and sex were accounted for.

Assessing the suitability for discharge from the hospital is a decision encountered by hospital-based clinicians every day. These skills are not taught, but are rather learned as a junior doctor acquires experience. It is unlikely that protocols will be developed to aid identification of potential discharges from an acute surgical ward; acute surgical conditions are too varied and dynamic to be able to pool all data. We continue to rely on our own and fellow colleagues’ (doctors, nurses, and other staff) input and assessment. However, our study has shown that it is possible to identify and quantify clinical findings that are already regularly used, albeit potentially subconsciously, to assess suitability for discharge. We have shown in this large, prospectively collected observational study that if a surgical patient is seen using their electronic device, they are more likely to be safe to go home. Thus, surgeons can reliably use this observation as a trigger to consider discharging the patient following a more thorough assessment.

While these observations might appear to be rather a simplistic way of trying to quantify whether or not a patient is fit for discharge, any clues that hint towards a patient’s well-being should be taken into account when making an overall assessment. The active use of a smartphone is one such measure.

Acknowledgments

The authors thank Emma Knight and Nancy Briggs from the Data Management & Analysis Centre, Discipline of Public Health, University of Adelaide.

Disclosure

No author nor the institution received any payment or services from a third party for any aspect of the submitted work and report no conflict of interest. There are no reported financial relationships with any entities by any of the authors. There are no patents pending based upon this publication. There are no relationships or activities that readers could perceive to have influenced, or give the appearance of influencing, the submitted work. The corresponding author is not in receipt of a research scholarship. The paper is not based on a previous communication.

The value placed on bedside clinical observation in the decision-making process of a patient’s illness has been diminished by today’s armamentarium of sophisticated technology. Increasing reliance is now placed on the result of nonspecific tests in preference to bedside clinical judgement in the diagnostic and management process. While diagnostic investigations have undoubtedly provided great advancements in medical care, they come at time and financial costs. Physicians should therefore continue to be encouraged to make clinical decisions based on their bedside assessment.

With hospital overcrowding a significant problem within the healthcare system and the expectation that it will worsen with an ageing population, identifying factors that predict patient suitability for discharge has become an important focus for clinicians.^1,2 There exists a paucity of literature predicting discharge suitability of general surgical patients admitted through the emergency department (ED). Furthermore, despite the extensive research into the effectiveness of discharge planning,³ little research has been conducted to describe positive predictive indicators for discharge. Observations made during surgical rounds have led the authors to consider that individuals who are using a smartphone during their bedside assessment may be clinically well enough for discharge.

The aim of this study was to assess whether the clinical assessment of an acute surgical patient could be usefully augmented by the observation of the active use of smartphones (the smartphone sign) and whether this could be used as a surrogate marker to indicate a patient’s well-being and suitability for same-day discharge from the hospital in acute surgical patients.

Design and Setting

This was a prospective observational study performed over 2 periods at a tertiary hospital in South Australia, Australia. At our institution, acute surgical patients are admitted to the acute surgical unit (ASU) from the ED by junior surgical doctors. Patients are then reviewed by the on-call surgical consultant, who implements management plans or advises discharge on 2 occasions per day.

Participants

All patients admitted under the ASU were considered eligible for the study. Exclusion criteria included patients that (i) required immediate surgical intervention (defined as time of review to theatre of less than 4 hours) and (ii) had immediate admission to the intensive care unit.

Consultant surgeons are employed within a general surgical subspecialty, including upper gastrointestinal, hepatobiliary, breast and endocrine, and colorectal. All surgeons from each team partake in the general surgery on-call roster. Each surgeon was included at least once within the observation periods. Experience of consultant surgeons ranged from 5 years of postfellowship experience to surgeons with more than 30 years of experience, with the majority having more than 10 years of postfellowship experience.

Patients were stratified into 2 distinct cohorts upon consultant review: smartphone positive (spP) was defined as a patient who was using a smartphone or who had their phone on their bed; a patient was classified as smartphone negative (spN) if they did not fulfil these criteria. The presence or absence of a smartphone was recorded by the authors, who were present on consultant ward rounds but not involved in the decision-making process of patient care. In order to minimize bias, only 1 surgeon (PGD) was aware that the study was being conducted and all patients were blinded to the study. Additional information that was collected included patient demographics, requirement for surgery, and length of stay (LOS). A patient who was discharged on the same day as the consultant review was considered to be discharged on day 1, all other patients were considered to have LOS greater than 1 day. Requirement for surgery was defined as a patient who underwent a surgical procedure in an operating suite. Thirty-day unplanned readmission rates for all patients were examined. Readmission to another public hospital within the state was also included within the readmission data.

Observation Periods

An initial 4-week pilot study was conducted to assess for a possible association between spP and same-day discharge. A second 8-week study period was undertaken 1 year later accounting for the employment of the authors at the study’s institution. Unless stated, the results described are the accumulation of both study periods.

Statistical Analysis

As this is the first study of its kind, no prior estimates of numbers were known. After 2 weeks of data collection, data were analyzed in order to provide an estimate of the total number of patients required to provide a statistically valid result (α = 0.05; power = 0.80). Sample size was calculated to be 40 subjects. It was agreed that in order to make the study as robust as possible, data should be collected for the 2 observation periods.

Demographic data are presented as means with standard deviations (SDs) or frequencies with percentages. A 2-sample Student t test was used to compare the age of spP and spN patients. A χ² test and logistic regressions were used to assess the association between smartphone status and patient demographics, LOS, and requirement for surgery. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A P value of <0.05 was considered significant. All data were analyzed by using R 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria).

During the 2 observation periods, a total of 227 eligible surgical admissions were observed with complete data for 221 patients. Six patients were excluded as their smartphone status was not recorded. The study sample represents our population of interest within an ASU, and we had complete data for 97.4% of participants with a 100% follow-up. There was no significant effect of study between the 2 observation periods (χ² = 140.19; P = 0.10). The mean age of patients was 50.24 years. Further demographic data are presented in Table 1. Twenty-five (11.3%) patients were spP and 196 (88.7%) were spN. Fifty-two (23.5%) patients were discharged home on day 1, and 169 (76.5%) had admissions longer than 1 day (see Figure). Sixty (27%) patients underwent surgery during their admission. Twenty-two patients had unplanned readmissions; only 1 of these patients had been observed to be spP.

There was a statistically significant difference in ages between the spP and spN groups (t = 8.40; P < 0.0005), with the average age of spP patients being 31.84 years compared with 52.58 years for spN patients. There was no statistical difference between gender and smartphone status (χ² = 1.78; P = 0.18; Table 2).

For those patients discharged home on day 1, there was a statistically significant association with being spP (χ² = 14.55, P = 0.0001). Patients who were spP were 5.29 times more likely to be discharged on day 1 (95% CI, 2.24-12.84). Of the variables analyzed, only gender failed to demonstrate an effect on discharge home on day 1 (Table 3). Overall, the presence of a smartphone was found to have a sensitivity of 56.0% (95% CI, 34.93-75.60) and a specificity of 80.6% (95% CI, 74.37-85.90) in regard to same-day discharge. However, it was found to have a negative predictive value of 93.49% (95% CI, 88.65-96.71).

When examining readmission rates, only 4% of spP patients were readmitted versus 10.7% of spN patients. Accounting for variables, spP patients were 4 times less likely to be readmitted, though this was not statistically significant (OR 4.02; 95% CI, 0.43-37.2; P = 0.22). Furthermore, when examining only those patients discharged on day 1, smartphone status was not a predictor of readmission (OR 0.94; 95% CI, 0.06-15.2; P = 0 .97).

To mitigate the effect of age, analysis was conducted excluding those aged over 55 years (the previous retirement age in Australia), leaving 131 patients for analysis. The average age of spP patients was 31.8 years (SD 10.0) compared with 36.7 years (SD 10.9) for spN patients, representing a significant difference (t = 2.14; P = 0.04); 51.1% of patients were male, 19.1% of patients were spP, 26.0% of patients proceeded to an operation, the oldest spP was 51 years, and 29.0% of patients were discharged home on day 1. There was no difference in gender and smartphone status (χ² = 0.33; P = 0.6). When analyzing those discharged on day 1, again spP patients were more likely to be discharged home (χ² = 9.4; P = 0.002), and spP patients were 3.6 times more likely to be discharged home on day 1.

There were 4 spP patients who underwent an operation. Two patients had an incision and drainage of a perianal abscess, 1 patient underwent a laparotomy for an internal hernia after recently undergoing a Roux-en-Y gastric bypass at another hospital, and the final patient underwent a laparoscopic appendicectomy. One of these patients was still discharged home on day 1.

As J. A. Lindsay⁴ said, “For one mistake made for not knowing, ten mistakes are made for not looking.” At medical school, we are taught the finer techniques of the physical examination in order to support our diagnosis made from the history. It is not until we are experienced clinicians do we develop the clinical acumen and ability to tell an unwell patient from a well patient at a glance—colloquially known as the “end of the bed” assessment. In the pretechnology era, a well patient could frequently be seen reading their book, eg, the “novel-sign.” With the advent of the smartphone and electronic devices upon which novels can be read, statuses updated, and locations “checked into” (ie, the modern “vital signs”), the book sign may be a thing of the past. However, the ability for the clinician to assess a patient’s wellness is still crucial, and the value of any additional “physical signs” need to be estimated.

We observed a cohort of patients through a busy ASU in a tertiary hospital in South Australia, Australia. Acute surgical patients admitted to the hospital who were observed to be on their phones upon consultant review were more than 5 times likely to be discharged that same day. To the best of our knowledge, this is the first study to prospectively collect data to assess a frequently used but unevaluated clinical observation.

The use of a smartphone can tell us a lot about an individual’s physiology. We can assume the individual’s airway and breathing are adequate, allowing enough oxygen to reach the lungs and subsequently circulate. The individual is usually sitting up in bed and thus has an adequate blood pressure and blood oxygenation that can maintain cerebral perfusion. They have the cognitive and cerebral processing in place to function the device, and we can examine their cerebellar function by looking for fine-motor movements.

Mobile phone ownership is pervasive within Australia,⁵ with a conservative estimated 85.7% of the population (20.57 million people of a total population of approximately 24 million) owning a mobile phone and an estimated 50% to 79% of mobile phone ownership being of a smartphone.^6,7 This ownership is not just limited to the young, with 74% of Australians over 65 owning or using a mobile phone.⁸ Despite this high phone ownership among those over 65, it is still significantly less than their younger counterparts and may be one reason for the absence of spP in those older than 51 years. A key point in the study is that overall phone ownership was not known, and, thus, it is not possible to determine the proportion of spN patients who were negative because they did not own a phone. However, based on general population data, the incidence of spP patients was well below that seen in the community (11.3%)⁵ and even when excluding those over 55, the percentage of spP patients only rose to 19.1%. Unsurprisingly, increasing age was associated with a decreased likelihood of being spP (P < 0.0005), as younger people are more likely to own a phone.⁸ There was no association with gender (P = 0.18). There are a number of explanations that may explain the lower than expected percentage of spP patients, including the inability for the patient to gather their possessions during a medical emergency, patients storing their phones prior to doctor review (72%-85% of Australians report talking on phones in public places to be rude or intrusive⁵), but more importantly, that our hypothesis that patients were too unwell to use their device appears to hold true.

There are potential alternate reasons other than smartphone status that may account for patients being discharged home on day 1. While there was no association seen with gender, the need for an operation prolonged a patient’s stay (OR 1.64; 95% CI, 0.046-0.46), and there was a trend seen with increasing age (OR 0.98; 95% CI, 0.96-1.00). Neither of these 2 demographics are unsurprising: increasing age is associated with increasing medical comorbidities and thus complexity; even the simplest of operations require a postprocedure observation period, automatically increasing their LOS. Additionally, measured demographics are limited and there may be further unmeasured reasons that account for earlier discharge.

The other key component to this study is the value of the physical examination, albeit only assessing 1 component: the general inspection. In their review of the value of the physical examination of the cardiovascular system, Elder et al. highlight an important point: in traditional teaching, the value of a physical sign is compared with a diagnostic reference, typically imaging or an invasive test.⁹ They argue that this definition undervalues the physical examination and list other values aside from accuracy including accessibility, contribution to clinical care beyond diagnoses, cost effectiveness, patients’ safety, patients’ perceptions, and pedagogic value; and they argue that the physical examination should always be considered in regard to the clinical context—in this case, the newly admitted general surgical patient.

The assessment of the presence or absence of a smartphone is readily performed upon general inspection and is easily visible; general inspection of the patient and failure to observe the clinical sign when present are 2 of the greatest errors associated with physical examination.¹⁰ Furthermore, given its unique status as a physical sign, the authors’ opinion and experience is that it is readily teachable. McGee states, “…a fundamental lesson [in regards to teaching] is that the diagnosis of many clinical problems, despite modern testing, still depends primarily on what the clinician sees, hears, and feels.”¹¹ In their article, Paley et al. found that more than 80% of patients admitted from the ED under internal medicine could be accurately diagnosed based largely on history and examination alone and concluded that basic clinical skills are sufficient for achieving an accurate diagnosis in most cases.¹² Although Paley et al. were assisted with basic tests (such as electrocardiogram and basic haematological and biochemistry results), the point of clinical skills is not lost. Furthermore, this assessment was made in a group of patients generally considered to be complex in contrast to the “standard” appendicitis or cholecystitis patient that makes up a significant proportion of general surgical patients.

There are a number of limitations to this study, however, including smartphones that may have been missed during the observational period. Potential confounding variables such as socioeconomic status and the overall smartphone ownership of our subjects were not known. We did not ask all admitted patients whether they owned a phone or whether they had a phone in their possession. Knowledge of those who owned phones but were not in possession of them could strengthen our argument that spN patients were not using their phone because they were unwell, rather than just not having access to it.

However, this study has a number of strengths, including a large sample size and data that were prospectively collected by a method and in a setting that was the same for all participants. Clear and appropriate definitions were used, which minimizes misclassification bias. Participants and decision makers were blinded to the study, and potentially confounding variables such as age and sex were accounted for.

Assessing the suitability for discharge from the hospital is a decision encountered by hospital-based clinicians every day. These skills are not taught, but are rather learned as a junior doctor acquires experience. It is unlikely that protocols will be developed to aid identification of potential discharges from an acute surgical ward; acute surgical conditions are too varied and dynamic to be able to pool all data. We continue to rely on our own and fellow colleagues’ (doctors, nurses, and other staff) input and assessment. However, our study has shown that it is possible to identify and quantify clinical findings that are already regularly used, albeit potentially subconsciously, to assess suitability for discharge. We have shown in this large, prospectively collected observational study that if a surgical patient is seen using their electronic device, they are more likely to be safe to go home. Thus, surgeons can reliably use this observation as a trigger to consider discharging the patient following a more thorough assessment.

While these observations might appear to be rather a simplistic way of trying to quantify whether or not a patient is fit for discharge, any clues that hint towards a patient’s well-being should be taken into account when making an overall assessment. The active use of a smartphone is one such measure.

Acknowledgments

The authors thank Emma Knight and Nancy Briggs from the Data Management & Analysis Centre, Discipline of Public Health, University of Adelaide.

Disclosure

No author nor the institution received any payment or services from a third party for any aspect of the submitted work and report no conflict of interest. There are no reported financial relationships with any entities by any of the authors. There are no patents pending based upon this publication. There are no relationships or activities that readers could perceive to have influenced, or give the appearance of influencing, the submitted work. The corresponding author is not in receipt of a research scholarship. The paper is not based on a previous communication.

Annual healthcare costs in the United States are over $3 trillion and are garnering significant national attention.¹ The United States spends approximately 2.5 times more per capita on healthcare when compared to other developed nations.² One source of unnecessary cost in healthcare is defensive medicine. Defensive medicine has been defined by Congress as occurring “when doctors order tests, procedures, or visits, or avoid certain high-risk patients or procedures, primarily (but not necessarily) because of concern about malpractice liability.”³

Though difficult to assess, in 1 study, defensive medicine was estimated to cost $45 billion annually.⁴ While general agreement exists that physicians practice defensive medicine, the extent of defensive practices and the subsequent impact on healthcare costs remain unclear. This is especially true for a group of clinicians that is rapidly increasing in number: hospitalists. Currently, there are more than 50,000 hospitalists in the United States,⁵ yet the prevalence of defensive medicine in this relatively new specialty is unknown. Inpatient care is complex and time constraints can impede establishing an optimal therapeutic relationship with the patient, potentially raising liability fears. We therefore sought to quantify hospitalist physician estimates of the cost of defensive medicine and assess correlates of their estimates. As being sued might spur defensive behaviors, we also assessed how many hospitalists reported being sued and whether this was associated with their estimates of defensive medicine.

Survey Questionnaire

In a previously published survey-based analysis, we reported on physician practice and overuse for 2 common scenarios in hospital medicine: preoperative evaluation and management of uncomplicated syncope.⁶ After responding to the vignettes, each physician was asked to provide demographic and employment information and malpractice history. In addition, they were asked the following: In your best estimation, what percentage of healthcare-related resources (eg, hospital admissions, diagnostic testing, treatment) are spent purely because of defensive medicine concerns? __________% resources

Survey Sample & Administration

The survey was sent to a sample of 1753 hospitalists, randomly identified through the Society of Hospital Medicine’s (SHM) database of members and annual meeting attendees. It is estimated that almost 30% of practicing hospitalists in the United States are members of the SHM.⁵ A full description of the sampling methodology was previously published.⁶ Selected hospitalists were mailed surveys, a $20 financial incentive, and subsequent reminders between June and October 2011.

The study was exempted from institutional review board review by the University of Michigan and the VA Ann Arbor Healthcare System.

Variables

The primary outcome of interest was the response to the “% resources” estimated to be spent on defensive medicine. This was analyzed as a continuous variable. Independent variables included the following: VA employment, malpractice insurance payer, employer, history of malpractice lawsuit, sex, race, and years practicing as a physician.

Statistical Analysis

Analyses were conducted using SAS, version 9.4 (SAS Institute). Descriptive statistics were first calculated for all variables. Next, bivariable comparisons between the outcome variables and other variables of interest were performed. Multivariable comparisons were made using linear regression for the outcome of estimated resources spent on defensive medicine. A P value of < 0.05 was considered statistically significant.

Of the 1753 surveys mailed, 253 were excluded due to incorrect addresses or because the recipients were not practicing hospitalists. A total of 1020 were completed and returned, yielding a 68% response rate (1020 out of 1500 eligible). The hospitalist respondents were in practice for an average of 11 years (range 1-40 years). Respondents represented all 50 states and had a diverse background of experience and demographic characteristics, which has been previously described.⁶

Resources Estimated Spent on Defensive Medicine

Hospitalists reported, on average, that they believed defensive medicine accounted for 37.5% (standard deviation, 20.2%) of all healthcare spending. Results from the multivariable regression are presented in the Table. Hospitalists affiliated with a VA hospital reported 5.5% less in resources spent on defensive medicine than those not affiliated with a VA hospital (32.2% VA vs 37.7% non-VA, P = 0.025). For every 10 years in practice, the estimate of resources spent on defensive medicine decreased by 3% (P = 0.003). Those who were male (36.4% male vs 39.4% female, P = 0.023) and non-Hispanic white (32.5% non-Hispanic white vs 44.7% other, P ≤ 0.001) also estimated less resources spent on defensive medicine. We did not find an association between a hospitalist reporting being sued and their perception of resources spent on defensive medicine.  

Risk of Being Sued

Over a quarter of our sample (25.6%) reported having been sued at least once for medical malpractice. The proportion of hospitalists that reported a history of being sued generally increased with more years of practice (Figure). For those who had been in practice for at least 20 years, more than half (55%) had been sued at least once during their career.

In a national survey, hospitalists estimated that almost 40% of all healthcare-related resources are spent purely because of defensive medicine concerns. This estimate was affected by personal demographic and employment factors. Our second major finding is that over one-quarter of a large random sample of hospitalist physicians reported being sued for malpractice.

Hospitalist perceptions of defensive medicine varied significantly based on employment at a VA hospital, with VA-affiliated hospitalists reporting less estimated spending on defensive medicine. This effect may reflect a less litigious environment within the VA, even though physicians practicing within the VA can be reported to the National Practitioner Data Bank.⁷ The different environment may be due to the VA’s patient mix (VA patients tend to be poorer, older, sicker, and have more mental illness)⁸; however, it could also be due to its de facto practice of a form of enterprise liability, in which, by law, the VA assumes responsibility for negligence, sheltering its physicians from direct liability.

We also found that the higher the number of years a hospitalist reported practicing, the lower the perception of resources being spent on defensive medicine. The reason for this finding is unclear. There has been a recent focus on high-value care and overspending, and perhaps younger hospitalists are more aware of these initiatives and thus have higher estimates. Additionally, non-Hispanic white male respondents estimated a lower amount spent on defensive medicine compared with other respondents. This is consistent with previous studies of risk perception which have noted a “white male effect” in which white males generally perceive a wide range of risks to be lower than female and non-white individuals, likely due to sociopolitical factors.⁹ Here, the white male effect is particularly interesting, considering that male physicians are almost 2.5 times as likely as female physicians to report being sued.¹⁰

Similar to prior studies,¹¹ there was no association with personal liability claim experience and perceived resources spent on defensive medicine. It is unclear why personal experience of being sued does not appear to be associated with perceptions of defensive medicine practice. It is possible that the fear of being sued is worse than the actual experience or that physicians believe that lawsuits are either random events or inevitable and, as a result, do not change their practice patterns.

The lifetime risk of being named in a malpractice suit is substantial for hospitalists: in our study, over half of hospitalists in practice for 20 years or more reported they had been sued. This corresponds with the projection made by Jena and colleagues,¹² which estimated that 55% of internal medicine physicians will be sued by the age of 45, a number just slightly higher than the average for all physicians.

Our study has important limitations. Our sample was of hospitalists and therefore may not be reflective of other medical specialties. Second, due to the nature of the study design, the responses to spending on defensive medicine may not represent actual practice. Third, we did not confirm details such as place of employment or history of lawsuit, and this may be subject to recall bias. However, physicians are unlikely to forget having been sued. Finally, this survey is observational and cross-sectional. Our data imply association rather than causation. Without longitudinal data, it is impossible to know if years of practice correlate with perceived defensive medicine spending due to a generational effect or a longitudinal effect (such as more confidence in diagnostic skills with more years of practice).

Despite these limitations, our survey has important policy implications. First, we found that defensive medicine is perceived by hospitalists to be costly. Although physicians likely overestimated the cost (37.5%, or an estimated $1 trillion is far higher than previous estimates of approximately 2% of all healthcare spending),⁴ it also demonstrates the extent to which physicians feel as though the medical care that is provided may be unnecessary. Second, at least a quarter of hospitalist physicians have been sued, and the risk of being named as a defendant in a lawsuit increases the longer they have been in clinical practice.

Given these findings, policies aimed to reduce the practice of defensive medicine may help the rising costs of healthcare. Reducing defensive medicine requires decreasing physician fears of liability and related reporting. Traditional tort reforms (with the exception of damage caps) have not been proven to do this. And damage caps can be inequitable, hard to pass, and even found to be unconstitutional in some states.¹³ However, other reform options hold promise in reducing liability fears, including enterprise liability, safe harbor legislation, and health courts.¹³ Finally, shared decision-making models may also provide a method to reduce defensive fears as well.⁶

Acknowledgments

The authors thank the Society of Hospital Medicine, Dr. Scott Flanders, Andrew Hickner, and David Ratz for their assistance with this project.

Disclosure

The authors received financial support from the Blue Cross Blue Shield of Michigan Foundation, the Department of Veterans Affairs Health Services Research and Development Center for Clinical Management Research, the University of Michigan Specialist-Hospitalist Allied Research Program, and the Ann Arbor University of Michigan VA Patient Safety Enhancement Program.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of Blue Cross Blue Shield of Michigan Foundation, the Department of Veterans Affairs, or the Society of Hospital Medicine.

Annual healthcare costs in the United States are over $3 trillion and are garnering significant national attention.¹ The United States spends approximately 2.5 times more per capita on healthcare when compared to other developed nations.² One source of unnecessary cost in healthcare is defensive medicine. Defensive medicine has been defined by Congress as occurring “when doctors order tests, procedures, or visits, or avoid certain high-risk patients or procedures, primarily (but not necessarily) because of concern about malpractice liability.”³

Though difficult to assess, in 1 study, defensive medicine was estimated to cost $45 billion annually.⁴ While general agreement exists that physicians practice defensive medicine, the extent of defensive practices and the subsequent impact on healthcare costs remain unclear. This is especially true for a group of clinicians that is rapidly increasing in number: hospitalists. Currently, there are more than 50,000 hospitalists in the United States,⁵ yet the prevalence of defensive medicine in this relatively new specialty is unknown. Inpatient care is complex and time constraints can impede establishing an optimal therapeutic relationship with the patient, potentially raising liability fears. We therefore sought to quantify hospitalist physician estimates of the cost of defensive medicine and assess correlates of their estimates. As being sued might spur defensive behaviors, we also assessed how many hospitalists reported being sued and whether this was associated with their estimates of defensive medicine.

Survey Questionnaire

In a previously published survey-based analysis, we reported on physician practice and overuse for 2 common scenarios in hospital medicine: preoperative evaluation and management of uncomplicated syncope.⁶ After responding to the vignettes, each physician was asked to provide demographic and employment information and malpractice history. In addition, they were asked the following: In your best estimation, what percentage of healthcare-related resources (eg, hospital admissions, diagnostic testing, treatment) are spent purely because of defensive medicine concerns? __________% resources

Survey Sample & Administration

The survey was sent to a sample of 1753 hospitalists, randomly identified through the Society of Hospital Medicine’s (SHM) database of members and annual meeting attendees. It is estimated that almost 30% of practicing hospitalists in the United States are members of the SHM.⁵ A full description of the sampling methodology was previously published.⁶ Selected hospitalists were mailed surveys, a $20 financial incentive, and subsequent reminders between June and October 2011.

The study was exempted from institutional review board review by the University of Michigan and the VA Ann Arbor Healthcare System.

Variables

The primary outcome of interest was the response to the “% resources” estimated to be spent on defensive medicine. This was analyzed as a continuous variable. Independent variables included the following: VA employment, malpractice insurance payer, employer, history of malpractice lawsuit, sex, race, and years practicing as a physician.

Statistical Analysis

Analyses were conducted using SAS, version 9.4 (SAS Institute). Descriptive statistics were first calculated for all variables. Next, bivariable comparisons between the outcome variables and other variables of interest were performed. Multivariable comparisons were made using linear regression for the outcome of estimated resources spent on defensive medicine. A P value of < 0.05 was considered statistically significant.

Of the 1753 surveys mailed, 253 were excluded due to incorrect addresses or because the recipients were not practicing hospitalists. A total of 1020 were completed and returned, yielding a 68% response rate (1020 out of 1500 eligible). The hospitalist respondents were in practice for an average of 11 years (range 1-40 years). Respondents represented all 50 states and had a diverse background of experience and demographic characteristics, which has been previously described.⁶

Resources Estimated Spent on Defensive Medicine

Hospitalists reported, on average, that they believed defensive medicine accounted for 37.5% (standard deviation, 20.2%) of all healthcare spending. Results from the multivariable regression are presented in the Table. Hospitalists affiliated with a VA hospital reported 5.5% less in resources spent on defensive medicine than those not affiliated with a VA hospital (32.2% VA vs 37.7% non-VA, P = 0.025). For every 10 years in practice, the estimate of resources spent on defensive medicine decreased by 3% (P = 0.003). Those who were male (36.4% male vs 39.4% female, P = 0.023) and non-Hispanic white (32.5% non-Hispanic white vs 44.7% other, P ≤ 0.001) also estimated less resources spent on defensive medicine. We did not find an association between a hospitalist reporting being sued and their perception of resources spent on defensive medicine.  

Risk of Being Sued

Over a quarter of our sample (25.6%) reported having been sued at least once for medical malpractice. The proportion of hospitalists that reported a history of being sued generally increased with more years of practice (Figure). For those who had been in practice for at least 20 years, more than half (55%) had been sued at least once during their career.

In a national survey, hospitalists estimated that almost 40% of all healthcare-related resources are spent purely because of defensive medicine concerns. This estimate was affected by personal demographic and employment factors. Our second major finding is that over one-quarter of a large random sample of hospitalist physicians reported being sued for malpractice.

Hospitalist perceptions of defensive medicine varied significantly based on employment at a VA hospital, with VA-affiliated hospitalists reporting less estimated spending on defensive medicine. This effect may reflect a less litigious environment within the VA, even though physicians practicing within the VA can be reported to the National Practitioner Data Bank.⁷ The different environment may be due to the VA’s patient mix (VA patients tend to be poorer, older, sicker, and have more mental illness)⁸; however, it could also be due to its de facto practice of a form of enterprise liability, in which, by law, the VA assumes responsibility for negligence, sheltering its physicians from direct liability.

We also found that the higher the number of years a hospitalist reported practicing, the lower the perception of resources being spent on defensive medicine. The reason for this finding is unclear. There has been a recent focus on high-value care and overspending, and perhaps younger hospitalists are more aware of these initiatives and thus have higher estimates. Additionally, non-Hispanic white male respondents estimated a lower amount spent on defensive medicine compared with other respondents. This is consistent with previous studies of risk perception which have noted a “white male effect” in which white males generally perceive a wide range of risks to be lower than female and non-white individuals, likely due to sociopolitical factors.⁹ Here, the white male effect is particularly interesting, considering that male physicians are almost 2.5 times as likely as female physicians to report being sued.¹⁰

Similar to prior studies,¹¹ there was no association with personal liability claim experience and perceived resources spent on defensive medicine. It is unclear why personal experience of being sued does not appear to be associated with perceptions of defensive medicine practice. It is possible that the fear of being sued is worse than the actual experience or that physicians believe that lawsuits are either random events or inevitable and, as a result, do not change their practice patterns.

The lifetime risk of being named in a malpractice suit is substantial for hospitalists: in our study, over half of hospitalists in practice for 20 years or more reported they had been sued. This corresponds with the projection made by Jena and colleagues,¹² which estimated that 55% of internal medicine physicians will be sued by the age of 45, a number just slightly higher than the average for all physicians.

Our study has important limitations. Our sample was of hospitalists and therefore may not be reflective of other medical specialties. Second, due to the nature of the study design, the responses to spending on defensive medicine may not represent actual practice. Third, we did not confirm details such as place of employment or history of lawsuit, and this may be subject to recall bias. However, physicians are unlikely to forget having been sued. Finally, this survey is observational and cross-sectional. Our data imply association rather than causation. Without longitudinal data, it is impossible to know if years of practice correlate with perceived defensive medicine spending due to a generational effect or a longitudinal effect (such as more confidence in diagnostic skills with more years of practice).

Despite these limitations, our survey has important policy implications. First, we found that defensive medicine is perceived by hospitalists to be costly. Although physicians likely overestimated the cost (37.5%, or an estimated $1 trillion is far higher than previous estimates of approximately 2% of all healthcare spending),⁴ it also demonstrates the extent to which physicians feel as though the medical care that is provided may be unnecessary. Second, at least a quarter of hospitalist physicians have been sued, and the risk of being named as a defendant in a lawsuit increases the longer they have been in clinical practice.

Given these findings, policies aimed to reduce the practice of defensive medicine may help the rising costs of healthcare. Reducing defensive medicine requires decreasing physician fears of liability and related reporting. Traditional tort reforms (with the exception of damage caps) have not been proven to do this. And damage caps can be inequitable, hard to pass, and even found to be unconstitutional in some states.¹³ However, other reform options hold promise in reducing liability fears, including enterprise liability, safe harbor legislation, and health courts.¹³ Finally, shared decision-making models may also provide a method to reduce defensive fears as well.⁶

Acknowledgments

The authors thank the Society of Hospital Medicine, Dr. Scott Flanders, Andrew Hickner, and David Ratz for their assistance with this project.

Disclosure

The authors received financial support from the Blue Cross Blue Shield of Michigan Foundation, the Department of Veterans Affairs Health Services Research and Development Center for Clinical Management Research, the University of Michigan Specialist-Hospitalist Allied Research Program, and the Ann Arbor University of Michigan VA Patient Safety Enhancement Program.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of Blue Cross Blue Shield of Michigan Foundation, the Department of Veterans Affairs, or the Society of Hospital Medicine.

Annual healthcare costs in the United States are over $3 trillion and are garnering significant national attention.¹ The United States spends approximately 2.5 times more per capita on healthcare when compared to other developed nations.² One source of unnecessary cost in healthcare is defensive medicine. Defensive medicine has been defined by Congress as occurring “when doctors order tests, procedures, or visits, or avoid certain high-risk patients or procedures, primarily (but not necessarily) because of concern about malpractice liability.”³

Though difficult to assess, in 1 study, defensive medicine was estimated to cost $45 billion annually.⁴ While general agreement exists that physicians practice defensive medicine, the extent of defensive practices and the subsequent impact on healthcare costs remain unclear. This is especially true for a group of clinicians that is rapidly increasing in number: hospitalists. Currently, there are more than 50,000 hospitalists in the United States,⁵ yet the prevalence of defensive medicine in this relatively new specialty is unknown. Inpatient care is complex and time constraints can impede establishing an optimal therapeutic relationship with the patient, potentially raising liability fears. We therefore sought to quantify hospitalist physician estimates of the cost of defensive medicine and assess correlates of their estimates. As being sued might spur defensive behaviors, we also assessed how many hospitalists reported being sued and whether this was associated with their estimates of defensive medicine.

Survey Questionnaire

In a previously published survey-based analysis, we reported on physician practice and overuse for 2 common scenarios in hospital medicine: preoperative evaluation and management of uncomplicated syncope.⁶ After responding to the vignettes, each physician was asked to provide demographic and employment information and malpractice history. In addition, they were asked the following: In your best estimation, what percentage of healthcare-related resources (eg, hospital admissions, diagnostic testing, treatment) are spent purely because of defensive medicine concerns? __________% resources

Survey Sample & Administration

The survey was sent to a sample of 1753 hospitalists, randomly identified through the Society of Hospital Medicine’s (SHM) database of members and annual meeting attendees. It is estimated that almost 30% of practicing hospitalists in the United States are members of the SHM.⁵ A full description of the sampling methodology was previously published.⁶ Selected hospitalists were mailed surveys, a $20 financial incentive, and subsequent reminders between June and October 2011.

The study was exempted from institutional review board review by the University of Michigan and the VA Ann Arbor Healthcare System.

Variables

The primary outcome of interest was the response to the “% resources” estimated to be spent on defensive medicine. This was analyzed as a continuous variable. Independent variables included the following: VA employment, malpractice insurance payer, employer, history of malpractice lawsuit, sex, race, and years practicing as a physician.

Statistical Analysis

Analyses were conducted using SAS, version 9.4 (SAS Institute). Descriptive statistics were first calculated for all variables. Next, bivariable comparisons between the outcome variables and other variables of interest were performed. Multivariable comparisons were made using linear regression for the outcome of estimated resources spent on defensive medicine. A P value of < 0.05 was considered statistically significant.

Of the 1753 surveys mailed, 253 were excluded due to incorrect addresses or because the recipients were not practicing hospitalists. A total of 1020 were completed and returned, yielding a 68% response rate (1020 out of 1500 eligible). The hospitalist respondents were in practice for an average of 11 years (range 1-40 years). Respondents represented all 50 states and had a diverse background of experience and demographic characteristics, which has been previously described.⁶

Resources Estimated Spent on Defensive Medicine

Hospitalists reported, on average, that they believed defensive medicine accounted for 37.5% (standard deviation, 20.2%) of all healthcare spending. Results from the multivariable regression are presented in the Table. Hospitalists affiliated with a VA hospital reported 5.5% less in resources spent on defensive medicine than those not affiliated with a VA hospital (32.2% VA vs 37.7% non-VA, P = 0.025). For every 10 years in practice, the estimate of resources spent on defensive medicine decreased by 3% (P = 0.003). Those who were male (36.4% male vs 39.4% female, P = 0.023) and non-Hispanic white (32.5% non-Hispanic white vs 44.7% other, P ≤ 0.001) also estimated less resources spent on defensive medicine. We did not find an association between a hospitalist reporting being sued and their perception of resources spent on defensive medicine.  

Risk of Being Sued

Over a quarter of our sample (25.6%) reported having been sued at least once for medical malpractice. The proportion of hospitalists that reported a history of being sued generally increased with more years of practice (Figure). For those who had been in practice for at least 20 years, more than half (55%) had been sued at least once during their career.

In a national survey, hospitalists estimated that almost 40% of all healthcare-related resources are spent purely because of defensive medicine concerns. This estimate was affected by personal demographic and employment factors. Our second major finding is that over one-quarter of a large random sample of hospitalist physicians reported being sued for malpractice.

Hospitalist perceptions of defensive medicine varied significantly based on employment at a VA hospital, with VA-affiliated hospitalists reporting less estimated spending on defensive medicine. This effect may reflect a less litigious environment within the VA, even though physicians practicing within the VA can be reported to the National Practitioner Data Bank.⁷ The different environment may be due to the VA’s patient mix (VA patients tend to be poorer, older, sicker, and have more mental illness)⁸; however, it could also be due to its de facto practice of a form of enterprise liability, in which, by law, the VA assumes responsibility for negligence, sheltering its physicians from direct liability.

We also found that the higher the number of years a hospitalist reported practicing, the lower the perception of resources being spent on defensive medicine. The reason for this finding is unclear. There has been a recent focus on high-value care and overspending, and perhaps younger hospitalists are more aware of these initiatives and thus have higher estimates. Additionally, non-Hispanic white male respondents estimated a lower amount spent on defensive medicine compared with other respondents. This is consistent with previous studies of risk perception which have noted a “white male effect” in which white males generally perceive a wide range of risks to be lower than female and non-white individuals, likely due to sociopolitical factors.⁹ Here, the white male effect is particularly interesting, considering that male physicians are almost 2.5 times as likely as female physicians to report being sued.¹⁰

Similar to prior studies,¹¹ there was no association with personal liability claim experience and perceived resources spent on defensive medicine. It is unclear why personal experience of being sued does not appear to be associated with perceptions of defensive medicine practice. It is possible that the fear of being sued is worse than the actual experience or that physicians believe that lawsuits are either random events or inevitable and, as a result, do not change their practice patterns.

The lifetime risk of being named in a malpractice suit is substantial for hospitalists: in our study, over half of hospitalists in practice for 20 years or more reported they had been sued. This corresponds with the projection made by Jena and colleagues,¹² which estimated that 55% of internal medicine physicians will be sued by the age of 45, a number just slightly higher than the average for all physicians.

Our study has important limitations. Our sample was of hospitalists and therefore may not be reflective of other medical specialties. Second, due to the nature of the study design, the responses to spending on defensive medicine may not represent actual practice. Third, we did not confirm details such as place of employment or history of lawsuit, and this may be subject to recall bias. However, physicians are unlikely to forget having been sued. Finally, this survey is observational and cross-sectional. Our data imply association rather than causation. Without longitudinal data, it is impossible to know if years of practice correlate with perceived defensive medicine spending due to a generational effect or a longitudinal effect (such as more confidence in diagnostic skills with more years of practice).

Despite these limitations, our survey has important policy implications. First, we found that defensive medicine is perceived by hospitalists to be costly. Although physicians likely overestimated the cost (37.5%, or an estimated $1 trillion is far higher than previous estimates of approximately 2% of all healthcare spending),⁴ it also demonstrates the extent to which physicians feel as though the medical care that is provided may be unnecessary. Second, at least a quarter of hospitalist physicians have been sued, and the risk of being named as a defendant in a lawsuit increases the longer they have been in clinical practice.

Given these findings, policies aimed to reduce the practice of defensive medicine may help the rising costs of healthcare. Reducing defensive medicine requires decreasing physician fears of liability and related reporting. Traditional tort reforms (with the exception of damage caps) have not been proven to do this. And damage caps can be inequitable, hard to pass, and even found to be unconstitutional in some states.¹³ However, other reform options hold promise in reducing liability fears, including enterprise liability, safe harbor legislation, and health courts.¹³ Finally, shared decision-making models may also provide a method to reduce defensive fears as well.⁶

Acknowledgments

The authors thank the Society of Hospital Medicine, Dr. Scott Flanders, Andrew Hickner, and David Ratz for their assistance with this project.

Disclosure

The authors received financial support from the Blue Cross Blue Shield of Michigan Foundation, the Department of Veterans Affairs Health Services Research and Development Center for Clinical Management Research, the University of Michigan Specialist-Hospitalist Allied Research Program, and the Ann Arbor University of Michigan VA Patient Safety Enhancement Program.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of Blue Cross Blue Shield of Michigan Foundation, the Department of Veterans Affairs, or the Society of Hospital Medicine.

Click here to access the 2018 Directory of VA and DoD Facilities Digital Edition

Table of Contents

• Department of Defense Health Care Facilities

Click here to access the 2018 Directory of VA and DoD Facilities Digital Edition

Table of Contents

• Department of Defense Health Care Facilities

Click here to access the 2018 Directory of VA and DoD Facilities Digital Edition

Table of Contents

• Department of Defense Health Care Facilities