Joseph A. Miller, MS

An estimated 200,000 adult patients suffer cardiac arrest in US hospitals each year, of which <20% survive to hospital discharge.[1, 2] Patient survival from in‐hospital cardiac arrest (IHCA), however, varies widely across hospitals, and may be partly attributed to differences in hospital practices.[3, 4, 5] Although there are data to support specific patient‐level practices in the hospital, such as delivery of electrical shock for ventricular fibrillation within 2 minutes of onset of the lethal rhythm,[6] little is known about in‐hospital systems‐level factors. Similar to patient‐level practices, some organizational and systems level practices are supported by international consensus and guideline recommendations.[7, 8] However, the adoption of these practices is poorly understood. As such, we sought to gain a better understanding of current US hospital practices with regard to IHCA and resuscitation with the hopes of identifying potential targets for improvement in quality and outcomes.

METHODS

We conducted a nationally representative mail survey between May 2011 and November 2011, targeting a stratified random sample of 1000 hospitals. We utilized the US Acute‐Care Hospitals (FY2008) database from the American Hospital Association to determine the total population of 3809 community hospitals (ie, nonfederal government, nonpsychiatric, and nonlong‐term care hospitals).[9] This included general medical and surgical, surgical, cancer, heart, orthopedic, and children's hospitals. These hospitals were stratified into tertiles by annual in‐patient days and teaching status (major, minor, nonteaching), from which our sample was randomly selected (Table 1). We identified each hospital's cardiopulmonary resuscitation (CPR) committee (sometimes known as code committee, code blue committee, or cardiac arrest committee) chair or chief medical/quality officer, to whom the paper‐based survey was addressed, with instructions to forward to the most appropriate person if someone other than the recipient. This study was evaluated by the University of Chicago institutional review board and deemed exempt from further review.

Figure 1

RESULTS

Responses were received from 439 hospitals (425 complete and 14 partially complete), yielding a response rate of 44%. One subject ID was removed from the survey and could not be identified, so it was excluded from any analyses. Hospital demographics were similar between responders and nonresponders (P=0.50) (Table 1). Respondents who filled out the surveys included chief medical/quality officers (n=143 [33%]), chairs of CPR committees (n=64 [15%]), members of CPR committees (n=29 [7%]), chiefs of staff (n=33 [8%]), resuscitation officers/nurses (n=27 [6%]), chief nursing officers (n=13 [3%]), and others (n=131 [30%]).

Table 2 summarizes structure, equipment, quality improvement, and pre‐ and postarrest practices across the hospitals. Of note, 77% of hospitals (n=334) reported having a predesignated, dedicated code team, and 66% (n=281) reported standardized defibrillator make and model throughout their hospital. However, less than one‐third of hospitals utilized any CPR assist technology (eg, CPR quality sensor or mechanical CPR device). The majority of hospitals reported having a rapid response team (RRT) (n=391 [91%]). Although a therapeutic hypothermia protocol for postarrest care was in place in over half of hospitals (n=252 [58%]), utilization of hypothermia for patients with return of spontaneous circulation was infrequent.

Hospitals reported that routine responders to IHCA events included respiratory therapists (n=414 [95%]), critical care nurses (n=406 [93%]), floor nurses (n=396 [90%]), attending physicians (n=392 [89%]), physician trainees (n=162 [37%]), and pharmacists (n=210 [48%]). Figure 1 shows the distribution of responders and team leaders by hospital type. Of the nonteaching hospitals, attending‐level physicians were likely to respond at 94% (265/281) and routinely lead the resuscitations at 84% (236/281), whereas, of major teaching hospitals, attending physicians were only likely to respond at 71% (30/42) and routinely lead at 19% (8/42).

Two‐thirds of the hospitals had a CPR committee (n=270 [66%]), and 196 (49%) had some staff time dedicated to resuscitation quality improvement. Hospitals with a specific committee dedicated to resuscitation and/or dedicated staff for resuscitation quality improvement were more likely to routinely track cardiac arrest data (odds ratio [OR]: 3.64, 95% confidence interval [CI]: 2.056.47 and OR: 2.02, 95% CI: 1.16‐3.54, respectively) and review the data (OR: 2.67, 95% CI: 1.45‐4.92 and OR: 2.18, 95% CI: 1.22‐3.89, respectively), after adjusting for teaching status and hospital size. These hospitals were also more likely to engage in simulation training and debriefing (Table 3).

Ninety percent (n=391) of respondents agreed that there is room for improvement in resuscitation practice at my hospital, and 70% (n=302) agreed that improved resuscitation would translate into improved patient outcomes. Overall, 78% (n=338) cited at least 1 barrier to improved resuscitation quality, of which the lack of adequate training (n=233 [54%]) and the lack of an appropriate champion (n=230 [53%]) were the most common. In subgroup analysis, nonteaching hospitals were significantly more likely to report the lack of a champion than their teaching counterparts (P=0.001) (Figure 2). In addition, we analyzed the data by hospitals that reported lack of a champion was not a barrier and compared them to those for whom it was, and found significantly higher adherence across all the measures in Table 2 supported by the 2010 guidelines, with the exception of real‐time feedback (data not shown).

Figure 2

DISCUSSION

In this nationally representative sample of hospitals, we found considerable variability in cardiac arrest and resuscitation structures and processes, suggesting potential areas to target for improvement. Some practices, including use of RRTs and defibrillator standardization, were fairly routine, whereas others, such as therapeutic hypothermia and CPR assist technology, were rarely utilized. Quality initiatives, such as data tracking and review, simulation training, and debriefing were variable.

Several factors likely contribute to the variable implementation of evidence‐based practices. Guidelines alone have been shown to have little impact on practice by physicians in general.[11] This is supported by the lack of correlation we found between the presence, absence or strength of specific American Heart Association (AHA) emergency cardiovascular care treatment recommendations and the percent of hospitals reporting performing that measure. It is possible that other factors, such as a lack of familiarity or agreement with those guidelines, or the presence of external barriers, may be contributing.[12, 13] Specifically, the importance of a clinical champion was supported by our finding that hospitals reporting lack of a champion as a barrier were less likely to be adherent with guidelines. However, because the study did not directly test the impact of a champion, we wanted to be careful to avoid overstating or editorializing our results.

Some of the variability may also be related to the resource intensiveness of the practice. Routine simulation training and debriefing interventions, for example, are time intensive and require trained personnel to institute. That may explain the correlation we noted between these practices and the presence of CPR committee and dedicated personnel. The use of dedicated personnel was rare in this study, with less than half of respondents reporting any dedicated staff and a median of 0.5 full‐time equivalents for those reporting positively. This is in stark contrast to the routine use of resuscitation officers (primarily nurses dedicated to overseeing resuscitation practices and education at the hospital) in the United Kingdom.[14] Such a resuscitation officer model adopted by US hospitals could improve the quality and intensity of resuscitation care approaches.

Particularly surprising was the high rate of respondents (70%) reporting that they do not utilize any CPR assist technology. In the patient who does not have an arterial line, use of quantitative capnography is the best measure of cardiac output during cardiac arrest, yet only one‐quarter of hospitals reported using it, with no discrepancy between hospital type or size. A recent summit of national resuscitation experts expounded on the AHA guidelines suggesting that end‐tidal carbon dioxide should be used in all arrests to guide the quality of CPR with a goal value of >20.[8] Similarly, CPR feedback devices have an even higher level of evidence recommendation in the 2010 AHA guidelines than capnography, yet only 4% of hospitals reported utilizing them. Although it is true that introducing these CPR assist technologies into a hospital would require some effort on the part of hospital leadership, it is important to recognize the potential role such devices might play in the larger context of a resuscitation quality program to optimize clinical outcomes from IHCA.

Several differences were noted between hospitals based on teaching status. Although all hospitals were more likely to rely on physicians to lead resuscitations, nonteaching hospitals were more likely to report routine leadership by nurses and pharmacists. Nonteaching hospitals were also less likely to have a CPR committee, even after adjusting for hospital size. In addition, these hospitals were also more likely to report the lack of a clinical champion as a barrier to quality improvement.

There were several limitations to this study. First, this was a descriptive survey that was not tied to outcomes. As such, we are unable to draw conclusions about which practices correlate with decreased incidence of cardiac arrest and improved survival. Second, this was an optional survey with a somewhat limited response rate. Even though the characteristics of the nonresponding hospitals were similar to the responding hospitals, we cannot rule out the possibility that a selection bias was introduced, which would likely overestimate adherence to the guidelines. Self‐reported responses may have introduced additional errors. Finally, the short interval between the release of the 2010 guidelines and the administration of the first survey may have contributed to the variability in implementation of some practices, but many of the recommendations had been previously included in the 2005 guidelines.

We conclude that there is wide variability between hospitals and within practices for resuscitation care. Future work should seek to understand which practices are associated with improved patient outcomes and how best to implement these practices in a more uniform fashion.

An estimated 200,000 adult patients suffer cardiac arrest in US hospitals each year, of which <20% survive to hospital discharge.[1, 2] Patient survival from in‐hospital cardiac arrest (IHCA), however, varies widely across hospitals, and may be partly attributed to differences in hospital practices.[3, 4, 5] Although there are data to support specific patient‐level practices in the hospital, such as delivery of electrical shock for ventricular fibrillation within 2 minutes of onset of the lethal rhythm,[6] little is known about in‐hospital systems‐level factors. Similar to patient‐level practices, some organizational and systems level practices are supported by international consensus and guideline recommendations.[7, 8] However, the adoption of these practices is poorly understood. As such, we sought to gain a better understanding of current US hospital practices with regard to IHCA and resuscitation with the hopes of identifying potential targets for improvement in quality and outcomes.

METHODS

We conducted a nationally representative mail survey between May 2011 and November 2011, targeting a stratified random sample of 1000 hospitals. We utilized the US Acute‐Care Hospitals (FY2008) database from the American Hospital Association to determine the total population of 3809 community hospitals (ie, nonfederal government, nonpsychiatric, and nonlong‐term care hospitals).[9] This included general medical and surgical, surgical, cancer, heart, orthopedic, and children's hospitals. These hospitals were stratified into tertiles by annual in‐patient days and teaching status (major, minor, nonteaching), from which our sample was randomly selected (Table 1). We identified each hospital's cardiopulmonary resuscitation (CPR) committee (sometimes known as code committee, code blue committee, or cardiac arrest committee) chair or chief medical/quality officer, to whom the paper‐based survey was addressed, with instructions to forward to the most appropriate person if someone other than the recipient. This study was evaluated by the University of Chicago institutional review board and deemed exempt from further review.

Figure 1

RESULTS

Responses were received from 439 hospitals (425 complete and 14 partially complete), yielding a response rate of 44%. One subject ID was removed from the survey and could not be identified, so it was excluded from any analyses. Hospital demographics were similar between responders and nonresponders (P=0.50) (Table 1). Respondents who filled out the surveys included chief medical/quality officers (n=143 [33%]), chairs of CPR committees (n=64 [15%]), members of CPR committees (n=29 [7%]), chiefs of staff (n=33 [8%]), resuscitation officers/nurses (n=27 [6%]), chief nursing officers (n=13 [3%]), and others (n=131 [30%]).

Table 2 summarizes structure, equipment, quality improvement, and pre‐ and postarrest practices across the hospitals. Of note, 77% of hospitals (n=334) reported having a predesignated, dedicated code team, and 66% (n=281) reported standardized defibrillator make and model throughout their hospital. However, less than one‐third of hospitals utilized any CPR assist technology (eg, CPR quality sensor or mechanical CPR device). The majority of hospitals reported having a rapid response team (RRT) (n=391 [91%]). Although a therapeutic hypothermia protocol for postarrest care was in place in over half of hospitals (n=252 [58%]), utilization of hypothermia for patients with return of spontaneous circulation was infrequent.

Hospitals reported that routine responders to IHCA events included respiratory therapists (n=414 [95%]), critical care nurses (n=406 [93%]), floor nurses (n=396 [90%]), attending physicians (n=392 [89%]), physician trainees (n=162 [37%]), and pharmacists (n=210 [48%]). Figure 1 shows the distribution of responders and team leaders by hospital type. Of the nonteaching hospitals, attending‐level physicians were likely to respond at 94% (265/281) and routinely lead the resuscitations at 84% (236/281), whereas, of major teaching hospitals, attending physicians were only likely to respond at 71% (30/42) and routinely lead at 19% (8/42).

Two‐thirds of the hospitals had a CPR committee (n=270 [66%]), and 196 (49%) had some staff time dedicated to resuscitation quality improvement. Hospitals with a specific committee dedicated to resuscitation and/or dedicated staff for resuscitation quality improvement were more likely to routinely track cardiac arrest data (odds ratio [OR]: 3.64, 95% confidence interval [CI]: 2.056.47 and OR: 2.02, 95% CI: 1.16‐3.54, respectively) and review the data (OR: 2.67, 95% CI: 1.45‐4.92 and OR: 2.18, 95% CI: 1.22‐3.89, respectively), after adjusting for teaching status and hospital size. These hospitals were also more likely to engage in simulation training and debriefing (Table 3).

Ninety percent (n=391) of respondents agreed that there is room for improvement in resuscitation practice at my hospital, and 70% (n=302) agreed that improved resuscitation would translate into improved patient outcomes. Overall, 78% (n=338) cited at least 1 barrier to improved resuscitation quality, of which the lack of adequate training (n=233 [54%]) and the lack of an appropriate champion (n=230 [53%]) were the most common. In subgroup analysis, nonteaching hospitals were significantly more likely to report the lack of a champion than their teaching counterparts (P=0.001) (Figure 2). In addition, we analyzed the data by hospitals that reported lack of a champion was not a barrier and compared them to those for whom it was, and found significantly higher adherence across all the measures in Table 2 supported by the 2010 guidelines, with the exception of real‐time feedback (data not shown).

Figure 2

DISCUSSION

In this nationally representative sample of hospitals, we found considerable variability in cardiac arrest and resuscitation structures and processes, suggesting potential areas to target for improvement. Some practices, including use of RRTs and defibrillator standardization, were fairly routine, whereas others, such as therapeutic hypothermia and CPR assist technology, were rarely utilized. Quality initiatives, such as data tracking and review, simulation training, and debriefing were variable.

Several factors likely contribute to the variable implementation of evidence‐based practices. Guidelines alone have been shown to have little impact on practice by physicians in general.[11] This is supported by the lack of correlation we found between the presence, absence or strength of specific American Heart Association (AHA) emergency cardiovascular care treatment recommendations and the percent of hospitals reporting performing that measure. It is possible that other factors, such as a lack of familiarity or agreement with those guidelines, or the presence of external barriers, may be contributing.[12, 13] Specifically, the importance of a clinical champion was supported by our finding that hospitals reporting lack of a champion as a barrier were less likely to be adherent with guidelines. However, because the study did not directly test the impact of a champion, we wanted to be careful to avoid overstating or editorializing our results.

Some of the variability may also be related to the resource intensiveness of the practice. Routine simulation training and debriefing interventions, for example, are time intensive and require trained personnel to institute. That may explain the correlation we noted between these practices and the presence of CPR committee and dedicated personnel. The use of dedicated personnel was rare in this study, with less than half of respondents reporting any dedicated staff and a median of 0.5 full‐time equivalents for those reporting positively. This is in stark contrast to the routine use of resuscitation officers (primarily nurses dedicated to overseeing resuscitation practices and education at the hospital) in the United Kingdom.[14] Such a resuscitation officer model adopted by US hospitals could improve the quality and intensity of resuscitation care approaches.

Particularly surprising was the high rate of respondents (70%) reporting that they do not utilize any CPR assist technology. In the patient who does not have an arterial line, use of quantitative capnography is the best measure of cardiac output during cardiac arrest, yet only one‐quarter of hospitals reported using it, with no discrepancy between hospital type or size. A recent summit of national resuscitation experts expounded on the AHA guidelines suggesting that end‐tidal carbon dioxide should be used in all arrests to guide the quality of CPR with a goal value of >20.[8] Similarly, CPR feedback devices have an even higher level of evidence recommendation in the 2010 AHA guidelines than capnography, yet only 4% of hospitals reported utilizing them. Although it is true that introducing these CPR assist technologies into a hospital would require some effort on the part of hospital leadership, it is important to recognize the potential role such devices might play in the larger context of a resuscitation quality program to optimize clinical outcomes from IHCA.

Several differences were noted between hospitals based on teaching status. Although all hospitals were more likely to rely on physicians to lead resuscitations, nonteaching hospitals were more likely to report routine leadership by nurses and pharmacists. Nonteaching hospitals were also less likely to have a CPR committee, even after adjusting for hospital size. In addition, these hospitals were also more likely to report the lack of a clinical champion as a barrier to quality improvement.

There were several limitations to this study. First, this was a descriptive survey that was not tied to outcomes. As such, we are unable to draw conclusions about which practices correlate with decreased incidence of cardiac arrest and improved survival. Second, this was an optional survey with a somewhat limited response rate. Even though the characteristics of the nonresponding hospitals were similar to the responding hospitals, we cannot rule out the possibility that a selection bias was introduced, which would likely overestimate adherence to the guidelines. Self‐reported responses may have introduced additional errors. Finally, the short interval between the release of the 2010 guidelines and the administration of the first survey may have contributed to the variability in implementation of some practices, but many of the recommendations had been previously included in the 2005 guidelines.

We conclude that there is wide variability between hospitals and within practices for resuscitation care. Future work should seek to understand which practices are associated with improved patient outcomes and how best to implement these practices in a more uniform fashion.

An estimated 200,000 adult patients suffer cardiac arrest in US hospitals each year, of which <20% survive to hospital discharge.[1, 2] Patient survival from in‐hospital cardiac arrest (IHCA), however, varies widely across hospitals, and may be partly attributed to differences in hospital practices.[3, 4, 5] Although there are data to support specific patient‐level practices in the hospital, such as delivery of electrical shock for ventricular fibrillation within 2 minutes of onset of the lethal rhythm,[6] little is known about in‐hospital systems‐level factors. Similar to patient‐level practices, some organizational and systems level practices are supported by international consensus and guideline recommendations.[7, 8] However, the adoption of these practices is poorly understood. As such, we sought to gain a better understanding of current US hospital practices with regard to IHCA and resuscitation with the hopes of identifying potential targets for improvement in quality and outcomes.

METHODS

We conducted a nationally representative mail survey between May 2011 and November 2011, targeting a stratified random sample of 1000 hospitals. We utilized the US Acute‐Care Hospitals (FY2008) database from the American Hospital Association to determine the total population of 3809 community hospitals (ie, nonfederal government, nonpsychiatric, and nonlong‐term care hospitals).[9] This included general medical and surgical, surgical, cancer, heart, orthopedic, and children's hospitals. These hospitals were stratified into tertiles by annual in‐patient days and teaching status (major, minor, nonteaching), from which our sample was randomly selected (Table 1). We identified each hospital's cardiopulmonary resuscitation (CPR) committee (sometimes known as code committee, code blue committee, or cardiac arrest committee) chair or chief medical/quality officer, to whom the paper‐based survey was addressed, with instructions to forward to the most appropriate person if someone other than the recipient. This study was evaluated by the University of Chicago institutional review board and deemed exempt from further review.

Figure 1

RESULTS

Responses were received from 439 hospitals (425 complete and 14 partially complete), yielding a response rate of 44%. One subject ID was removed from the survey and could not be identified, so it was excluded from any analyses. Hospital demographics were similar between responders and nonresponders (P=0.50) (Table 1). Respondents who filled out the surveys included chief medical/quality officers (n=143 [33%]), chairs of CPR committees (n=64 [15%]), members of CPR committees (n=29 [7%]), chiefs of staff (n=33 [8%]), resuscitation officers/nurses (n=27 [6%]), chief nursing officers (n=13 [3%]), and others (n=131 [30%]).

Table 2 summarizes structure, equipment, quality improvement, and pre‐ and postarrest practices across the hospitals. Of note, 77% of hospitals (n=334) reported having a predesignated, dedicated code team, and 66% (n=281) reported standardized defibrillator make and model throughout their hospital. However, less than one‐third of hospitals utilized any CPR assist technology (eg, CPR quality sensor or mechanical CPR device). The majority of hospitals reported having a rapid response team (RRT) (n=391 [91%]). Although a therapeutic hypothermia protocol for postarrest care was in place in over half of hospitals (n=252 [58%]), utilization of hypothermia for patients with return of spontaneous circulation was infrequent.

Hospitals reported that routine responders to IHCA events included respiratory therapists (n=414 [95%]), critical care nurses (n=406 [93%]), floor nurses (n=396 [90%]), attending physicians (n=392 [89%]), physician trainees (n=162 [37%]), and pharmacists (n=210 [48%]). Figure 1 shows the distribution of responders and team leaders by hospital type. Of the nonteaching hospitals, attending‐level physicians were likely to respond at 94% (265/281) and routinely lead the resuscitations at 84% (236/281), whereas, of major teaching hospitals, attending physicians were only likely to respond at 71% (30/42) and routinely lead at 19% (8/42).

Two‐thirds of the hospitals had a CPR committee (n=270 [66%]), and 196 (49%) had some staff time dedicated to resuscitation quality improvement. Hospitals with a specific committee dedicated to resuscitation and/or dedicated staff for resuscitation quality improvement were more likely to routinely track cardiac arrest data (odds ratio [OR]: 3.64, 95% confidence interval [CI]: 2.056.47 and OR: 2.02, 95% CI: 1.16‐3.54, respectively) and review the data (OR: 2.67, 95% CI: 1.45‐4.92 and OR: 2.18, 95% CI: 1.22‐3.89, respectively), after adjusting for teaching status and hospital size. These hospitals were also more likely to engage in simulation training and debriefing (Table 3).

Ninety percent (n=391) of respondents agreed that there is room for improvement in resuscitation practice at my hospital, and 70% (n=302) agreed that improved resuscitation would translate into improved patient outcomes. Overall, 78% (n=338) cited at least 1 barrier to improved resuscitation quality, of which the lack of adequate training (n=233 [54%]) and the lack of an appropriate champion (n=230 [53%]) were the most common. In subgroup analysis, nonteaching hospitals were significantly more likely to report the lack of a champion than their teaching counterparts (P=0.001) (Figure 2). In addition, we analyzed the data by hospitals that reported lack of a champion was not a barrier and compared them to those for whom it was, and found significantly higher adherence across all the measures in Table 2 supported by the 2010 guidelines, with the exception of real‐time feedback (data not shown).

Figure 2

DISCUSSION

In this nationally representative sample of hospitals, we found considerable variability in cardiac arrest and resuscitation structures and processes, suggesting potential areas to target for improvement. Some practices, including use of RRTs and defibrillator standardization, were fairly routine, whereas others, such as therapeutic hypothermia and CPR assist technology, were rarely utilized. Quality initiatives, such as data tracking and review, simulation training, and debriefing were variable.

Several factors likely contribute to the variable implementation of evidence‐based practices. Guidelines alone have been shown to have little impact on practice by physicians in general.[11] This is supported by the lack of correlation we found between the presence, absence or strength of specific American Heart Association (AHA) emergency cardiovascular care treatment recommendations and the percent of hospitals reporting performing that measure. It is possible that other factors, such as a lack of familiarity or agreement with those guidelines, or the presence of external barriers, may be contributing.[12, 13] Specifically, the importance of a clinical champion was supported by our finding that hospitals reporting lack of a champion as a barrier were less likely to be adherent with guidelines. However, because the study did not directly test the impact of a champion, we wanted to be careful to avoid overstating or editorializing our results.

Some of the variability may also be related to the resource intensiveness of the practice. Routine simulation training and debriefing interventions, for example, are time intensive and require trained personnel to institute. That may explain the correlation we noted between these practices and the presence of CPR committee and dedicated personnel. The use of dedicated personnel was rare in this study, with less than half of respondents reporting any dedicated staff and a median of 0.5 full‐time equivalents for those reporting positively. This is in stark contrast to the routine use of resuscitation officers (primarily nurses dedicated to overseeing resuscitation practices and education at the hospital) in the United Kingdom.[14] Such a resuscitation officer model adopted by US hospitals could improve the quality and intensity of resuscitation care approaches.

Particularly surprising was the high rate of respondents (70%) reporting that they do not utilize any CPR assist technology. In the patient who does not have an arterial line, use of quantitative capnography is the best measure of cardiac output during cardiac arrest, yet only one‐quarter of hospitals reported using it, with no discrepancy between hospital type or size. A recent summit of national resuscitation experts expounded on the AHA guidelines suggesting that end‐tidal carbon dioxide should be used in all arrests to guide the quality of CPR with a goal value of >20.[8] Similarly, CPR feedback devices have an even higher level of evidence recommendation in the 2010 AHA guidelines than capnography, yet only 4% of hospitals reported utilizing them. Although it is true that introducing these CPR assist technologies into a hospital would require some effort on the part of hospital leadership, it is important to recognize the potential role such devices might play in the larger context of a resuscitation quality program to optimize clinical outcomes from IHCA.

Several differences were noted between hospitals based on teaching status. Although all hospitals were more likely to rely on physicians to lead resuscitations, nonteaching hospitals were more likely to report routine leadership by nurses and pharmacists. Nonteaching hospitals were also less likely to have a CPR committee, even after adjusting for hospital size. In addition, these hospitals were also more likely to report the lack of a clinical champion as a barrier to quality improvement.

There were several limitations to this study. First, this was a descriptive survey that was not tied to outcomes. As such, we are unable to draw conclusions about which practices correlate with decreased incidence of cardiac arrest and improved survival. Second, this was an optional survey with a somewhat limited response rate. Even though the characteristics of the nonresponding hospitals were similar to the responding hospitals, we cannot rule out the possibility that a selection bias was introduced, which would likely overestimate adherence to the guidelines. Self‐reported responses may have introduced additional errors. Finally, the short interval between the release of the 2010 guidelines and the administration of the first survey may have contributed to the variability in implementation of some practices, but many of the recommendations had been previously included in the 2005 guidelines.

We conclude that there is wide variability between hospitals and within practices for resuscitation care. Future work should seek to understand which practices are associated with improved patient outcomes and how best to implement these practices in a more uniform fashion.

With the continuing growth of the specialty of hospital medicine, the capabilities and performance of hospital medicine groups (HMGs) varies significantly. There are few guidelines that HMGs can reference as tools to guide self‐improvement. To address this deficiency, the Society of Hospital Medicine (SHM) Board of Directors authorized a process to identify the key principles and characteristics of an effective HMG.

METHODS

Final Approval

The final draft of the 10 principles and 47 characteristics was approved for publication at a meeting of the SHM Board of Directors in September 2013 (Figure 1).

Figure 1

RESULTS

A recurring issue that the workgroup addressed was the applicability of the characteristics from 1 practice setting to another. Confounding factors include the HMG's employment/organizational model (eg, hospital employed, academic, multispecialty group, private practice, and management company), its population served (eg, adult vs pediatric, more than 1 hospital), and the type of hospital served (eg, academic vs community, the hospital has more than 1 HMG). The workgroup has made an effort to assure that all 47 characteristics can be applied to every type of HMG.

In developing the 10 principles, the workgroup attempted to construct a list of the basic ingredients needed to build and sustain an effective HMG. These 10 principles stand on their own, independent of the 47 key characteristics, and include issues such as effective leadership, clinician engagement, adequate resources, management infrastructure, key hospitalist roles and responsibilities, alignment with the hospital, and the recruitment and retention of qualified hospitalists.

A more detailed version of the Key Principles and Characteristics of an Effective HMG is available in the online version of this article (see Supporting Information, Appendix, in the online version of this article). The online Appendix includes the rationales for each of the characteristics, guidance on how to provide feedback to the SHM on the framework, and the SHM's plan for further development of the key principles and characteristics.

DISCUSSION

To address the variability in capabilities and performance of HMGs, these principles and characteristics are designed to provide a framework for HMGs seeking to conduct self‐assessments and develop pathways for improvement.

Although there may be HMG arrangements that do not directly involve the hospital and its executive team, and therefore alternative approaches may make sense, for most HMGs hospitals are directly involved with the HMG as either an employer or a contractor. For that reason, the Key Principles and Characteristics of an Effective HMG is written for 2 audiences: the executive leadership of the hospital (most specifically the chief medical officer or a similar role) and the hospitalists in the HMG (most specifically the practice medical director). To address the key characteristics requires the active participation of both parties. For the hospital executives, the framework establishes expectations for the HMG. For the hospitalists, the framework provides guidance in the development of an improvement plan.

Hospital executives and hospitalists can use the key characteristics in a broad spectrum of ways. The easiest and least formalized approach would be to use the framework as the basis of an ongoing dialogue between the hospital leadership and the HMG. A more formal approach would be to use the framework to guide the planning and budgeting activities of the HMG. Finally, a hospital or health system can use the key principles and characteristics as a way to evaluate their affiliated HMG(s)for example, the HMG must address 80% of the 47 characteristics.

The Key Principles and Characteristics of an Effective HMG should be considered akin to the Core Competencies in Hospital Medicine previously published in the Journal of Hospital Medicine.[7] However, instead of focusing on the competencies of individual physicians, this framework focuses on the characteristics of hospitalist groups. Just as a physician or other healthcare provider is not expected to demonstrate competency for every element in the core competencies document, an HMG does not need to have all 47 characteristics to be effective. Effective hospitalists may have skills other than those listed in the Core Competencies in Hospital Medicine. Similarly, the 47 characteristics do not represent an exhaustive list of every desirable HMG attribute. In general, effective HMGs should possess most of the characteristics.

In applying the framework, the HMG should not simply attempt to evaluate each characteristic with a yes or no assessment. For HMGs responding yes, there may be a wide range of performancefrom meeting the bare minimum requirements to employing sophisticated, expansive measures to excel in the characteristic.

SHM encourages hospital leaders and HMG leaders to use these characteristics to perform an HMG self‐assessment and to develop a plan. The plan could address implementation of selected characteristics that are not currently being addressed by the HMG or the development of additional behaviors, tools, resources, and capabilities that more fully incorporate those characteristics for which the HMG meets only minimum requirements. In addition, the plan could address the impact that a larger organization (eg, health system, hospital, or employer) may have on a given characteristic.

As outlined above, the process used to develop the Key Principles and Characteristics of an Effective HMG was grounded in expert opinion and extensive review and feedback. HMGs that use the framework should recognize that others might have a different opinion. For example, characteristic 5.2 states, The HMG's compensation model aligns hospitalist incentives with the goals of the hospital and the goals of the hospitalist's employer (if different). There are likely to be experienced hospitalist leaders who believe that an effective HMG does not need to have an incentive compensation system. However, the consensus process employed to develop the key characteristics led to the conclusion that an effective HMG should have an incentive compensation system.

The publication of the Key Principles and Characteristics of an Effective HMG may lead to negative and/or unintended consequences. A self‐assessment by an HMG using this framework could require a significant level of effort on behalf of the HMG, whereas implementing remedial efforts to address the characteristics could require an investment of time and money that could take away from other important issues facing the HMG. Many HMGs may be held accountable for addressing these characteristics without the necessary financial support from their hospital or medical group. Finally, the publication of the document could create a backlash from members of the hospitalist community who do not think that the SHM should be in the business of defining what characterizes an effective HMG, rather that this definition should be left to the marketplace.

Despite these concerns, the leadership of the SHM expects that the publication of the Key Principles and Characteristics of an Effective HMG will lead to overall improvement in the capabilities and performance of HMGs.

CONCLUSIONS

The Key Principles and Characteristics of an Effective HMG have been designed to be aspirational, helping to raise the bar for the specialty of hospital medicine. These principles and characteristics could provide a framework for HMGs seeking to conduct self‐assessments, outlining a pathway for improvement, and better defining the central role of hospitalists in coordinating team‐based, patient‐centered care in the acute care setting.

With the continuing growth of the specialty of hospital medicine, the capabilities and performance of hospital medicine groups (HMGs) varies significantly. There are few guidelines that HMGs can reference as tools to guide self‐improvement. To address this deficiency, the Society of Hospital Medicine (SHM) Board of Directors authorized a process to identify the key principles and characteristics of an effective HMG.

METHODS

Final Approval

The final draft of the 10 principles and 47 characteristics was approved for publication at a meeting of the SHM Board of Directors in September 2013 (Figure 1).

Figure 1

RESULTS

A recurring issue that the workgroup addressed was the applicability of the characteristics from 1 practice setting to another. Confounding factors include the HMG's employment/organizational model (eg, hospital employed, academic, multispecialty group, private practice, and management company), its population served (eg, adult vs pediatric, more than 1 hospital), and the type of hospital served (eg, academic vs community, the hospital has more than 1 HMG). The workgroup has made an effort to assure that all 47 characteristics can be applied to every type of HMG.

In developing the 10 principles, the workgroup attempted to construct a list of the basic ingredients needed to build and sustain an effective HMG. These 10 principles stand on their own, independent of the 47 key characteristics, and include issues such as effective leadership, clinician engagement, adequate resources, management infrastructure, key hospitalist roles and responsibilities, alignment with the hospital, and the recruitment and retention of qualified hospitalists.

A more detailed version of the Key Principles and Characteristics of an Effective HMG is available in the online version of this article (see Supporting Information, Appendix, in the online version of this article). The online Appendix includes the rationales for each of the characteristics, guidance on how to provide feedback to the SHM on the framework, and the SHM's plan for further development of the key principles and characteristics.

DISCUSSION

To address the variability in capabilities and performance of HMGs, these principles and characteristics are designed to provide a framework for HMGs seeking to conduct self‐assessments and develop pathways for improvement.

Although there may be HMG arrangements that do not directly involve the hospital and its executive team, and therefore alternative approaches may make sense, for most HMGs hospitals are directly involved with the HMG as either an employer or a contractor. For that reason, the Key Principles and Characteristics of an Effective HMG is written for 2 audiences: the executive leadership of the hospital (most specifically the chief medical officer or a similar role) and the hospitalists in the HMG (most specifically the practice medical director). To address the key characteristics requires the active participation of both parties. For the hospital executives, the framework establishes expectations for the HMG. For the hospitalists, the framework provides guidance in the development of an improvement plan.

Hospital executives and hospitalists can use the key characteristics in a broad spectrum of ways. The easiest and least formalized approach would be to use the framework as the basis of an ongoing dialogue between the hospital leadership and the HMG. A more formal approach would be to use the framework to guide the planning and budgeting activities of the HMG. Finally, a hospital or health system can use the key principles and characteristics as a way to evaluate their affiliated HMG(s)for example, the HMG must address 80% of the 47 characteristics.

The Key Principles and Characteristics of an Effective HMG should be considered akin to the Core Competencies in Hospital Medicine previously published in the Journal of Hospital Medicine.[7] However, instead of focusing on the competencies of individual physicians, this framework focuses on the characteristics of hospitalist groups. Just as a physician or other healthcare provider is not expected to demonstrate competency for every element in the core competencies document, an HMG does not need to have all 47 characteristics to be effective. Effective hospitalists may have skills other than those listed in the Core Competencies in Hospital Medicine. Similarly, the 47 characteristics do not represent an exhaustive list of every desirable HMG attribute. In general, effective HMGs should possess most of the characteristics.

In applying the framework, the HMG should not simply attempt to evaluate each characteristic with a yes or no assessment. For HMGs responding yes, there may be a wide range of performancefrom meeting the bare minimum requirements to employing sophisticated, expansive measures to excel in the characteristic.

SHM encourages hospital leaders and HMG leaders to use these characteristics to perform an HMG self‐assessment and to develop a plan. The plan could address implementation of selected characteristics that are not currently being addressed by the HMG or the development of additional behaviors, tools, resources, and capabilities that more fully incorporate those characteristics for which the HMG meets only minimum requirements. In addition, the plan could address the impact that a larger organization (eg, health system, hospital, or employer) may have on a given characteristic.

As outlined above, the process used to develop the Key Principles and Characteristics of an Effective HMG was grounded in expert opinion and extensive review and feedback. HMGs that use the framework should recognize that others might have a different opinion. For example, characteristic 5.2 states, The HMG's compensation model aligns hospitalist incentives with the goals of the hospital and the goals of the hospitalist's employer (if different). There are likely to be experienced hospitalist leaders who believe that an effective HMG does not need to have an incentive compensation system. However, the consensus process employed to develop the key characteristics led to the conclusion that an effective HMG should have an incentive compensation system.

The publication of the Key Principles and Characteristics of an Effective HMG may lead to negative and/or unintended consequences. A self‐assessment by an HMG using this framework could require a significant level of effort on behalf of the HMG, whereas implementing remedial efforts to address the characteristics could require an investment of time and money that could take away from other important issues facing the HMG. Many HMGs may be held accountable for addressing these characteristics without the necessary financial support from their hospital or medical group. Finally, the publication of the document could create a backlash from members of the hospitalist community who do not think that the SHM should be in the business of defining what characterizes an effective HMG, rather that this definition should be left to the marketplace.

Despite these concerns, the leadership of the SHM expects that the publication of the Key Principles and Characteristics of an Effective HMG will lead to overall improvement in the capabilities and performance of HMGs.

CONCLUSIONS

The Key Principles and Characteristics of an Effective HMG have been designed to be aspirational, helping to raise the bar for the specialty of hospital medicine. These principles and characteristics could provide a framework for HMGs seeking to conduct self‐assessments, outlining a pathway for improvement, and better defining the central role of hospitalists in coordinating team‐based, patient‐centered care in the acute care setting.

With the continuing growth of the specialty of hospital medicine, the capabilities and performance of hospital medicine groups (HMGs) varies significantly. There are few guidelines that HMGs can reference as tools to guide self‐improvement. To address this deficiency, the Society of Hospital Medicine (SHM) Board of Directors authorized a process to identify the key principles and characteristics of an effective HMG.

METHODS

Final Approval

The final draft of the 10 principles and 47 characteristics was approved for publication at a meeting of the SHM Board of Directors in September 2013 (Figure 1).

Figure 1

RESULTS

A recurring issue that the workgroup addressed was the applicability of the characteristics from 1 practice setting to another. Confounding factors include the HMG's employment/organizational model (eg, hospital employed, academic, multispecialty group, private practice, and management company), its population served (eg, adult vs pediatric, more than 1 hospital), and the type of hospital served (eg, academic vs community, the hospital has more than 1 HMG). The workgroup has made an effort to assure that all 47 characteristics can be applied to every type of HMG.

In developing the 10 principles, the workgroup attempted to construct a list of the basic ingredients needed to build and sustain an effective HMG. These 10 principles stand on their own, independent of the 47 key characteristics, and include issues such as effective leadership, clinician engagement, adequate resources, management infrastructure, key hospitalist roles and responsibilities, alignment with the hospital, and the recruitment and retention of qualified hospitalists.

A more detailed version of the Key Principles and Characteristics of an Effective HMG is available in the online version of this article (see Supporting Information, Appendix, in the online version of this article). The online Appendix includes the rationales for each of the characteristics, guidance on how to provide feedback to the SHM on the framework, and the SHM's plan for further development of the key principles and characteristics.

DISCUSSION

To address the variability in capabilities and performance of HMGs, these principles and characteristics are designed to provide a framework for HMGs seeking to conduct self‐assessments and develop pathways for improvement.

Although there may be HMG arrangements that do not directly involve the hospital and its executive team, and therefore alternative approaches may make sense, for most HMGs hospitals are directly involved with the HMG as either an employer or a contractor. For that reason, the Key Principles and Characteristics of an Effective HMG is written for 2 audiences: the executive leadership of the hospital (most specifically the chief medical officer or a similar role) and the hospitalists in the HMG (most specifically the practice medical director). To address the key characteristics requires the active participation of both parties. For the hospital executives, the framework establishes expectations for the HMG. For the hospitalists, the framework provides guidance in the development of an improvement plan.

Hospital executives and hospitalists can use the key characteristics in a broad spectrum of ways. The easiest and least formalized approach would be to use the framework as the basis of an ongoing dialogue between the hospital leadership and the HMG. A more formal approach would be to use the framework to guide the planning and budgeting activities of the HMG. Finally, a hospital or health system can use the key principles and characteristics as a way to evaluate their affiliated HMG(s)for example, the HMG must address 80% of the 47 characteristics.

The Key Principles and Characteristics of an Effective HMG should be considered akin to the Core Competencies in Hospital Medicine previously published in the Journal of Hospital Medicine.[7] However, instead of focusing on the competencies of individual physicians, this framework focuses on the characteristics of hospitalist groups. Just as a physician or other healthcare provider is not expected to demonstrate competency for every element in the core competencies document, an HMG does not need to have all 47 characteristics to be effective. Effective hospitalists may have skills other than those listed in the Core Competencies in Hospital Medicine. Similarly, the 47 characteristics do not represent an exhaustive list of every desirable HMG attribute. In general, effective HMGs should possess most of the characteristics.

In applying the framework, the HMG should not simply attempt to evaluate each characteristic with a yes or no assessment. For HMGs responding yes, there may be a wide range of performancefrom meeting the bare minimum requirements to employing sophisticated, expansive measures to excel in the characteristic.

SHM encourages hospital leaders and HMG leaders to use these characteristics to perform an HMG self‐assessment and to develop a plan. The plan could address implementation of selected characteristics that are not currently being addressed by the HMG or the development of additional behaviors, tools, resources, and capabilities that more fully incorporate those characteristics for which the HMG meets only minimum requirements. In addition, the plan could address the impact that a larger organization (eg, health system, hospital, or employer) may have on a given characteristic.

As outlined above, the process used to develop the Key Principles and Characteristics of an Effective HMG was grounded in expert opinion and extensive review and feedback. HMGs that use the framework should recognize that others might have a different opinion. For example, characteristic 5.2 states, The HMG's compensation model aligns hospitalist incentives with the goals of the hospital and the goals of the hospitalist's employer (if different). There are likely to be experienced hospitalist leaders who believe that an effective HMG does not need to have an incentive compensation system. However, the consensus process employed to develop the key characteristics led to the conclusion that an effective HMG should have an incentive compensation system.

The publication of the Key Principles and Characteristics of an Effective HMG may lead to negative and/or unintended consequences. A self‐assessment by an HMG using this framework could require a significant level of effort on behalf of the HMG, whereas implementing remedial efforts to address the characteristics could require an investment of time and money that could take away from other important issues facing the HMG. Many HMGs may be held accountable for addressing these characteristics without the necessary financial support from their hospital or medical group. Finally, the publication of the document could create a backlash from members of the hospitalist community who do not think that the SHM should be in the business of defining what characterizes an effective HMG, rather that this definition should be left to the marketplace.

Despite these concerns, the leadership of the SHM expects that the publication of the Key Principles and Characteristics of an Effective HMG will lead to overall improvement in the capabilities and performance of HMGs.

CONCLUSIONS

The Key Principles and Characteristics of an Effective HMG have been designed to be aspirational, helping to raise the bar for the specialty of hospital medicine. These principles and characteristics could provide a framework for HMGs seeking to conduct self‐assessments, outlining a pathway for improvement, and better defining the central role of hospitalists in coordinating team‐based, patient‐centered care in the acute care setting.

Person‐organization fit concerns the conditions and consequences of compatibility between people and the organizations for which they work.[1] Studies of other industries have demonstrated that person‐organization fit informs the way individuals join, perform in, and are retained by organizations.[2] Person‐job fit is a closely related subordinate concept that concerns the alignment of workers and their job in as much as workers have needs that their job supplies, or conversely, jobs have requirements that certain workers' abilities can help meet.[3] Explorations of job fit in physicians and their work have recently emerged in a few investigations published in medical journals.[4, 5, 6, 7, 8] Further expanding the understanding of fit between physicians and their employment is important, because the decline of solo practices and recent emphasis on team‐based care have led to a growing number of US physicians working in organizations.[9]

The movement of physicians into employed situations may continue if certain types of Accountable Care Organizations take root.[10] And physicians may be primed to join employer organizations based on current career priorities of individuals in American society. Surveys of medical residents entering the workforce reveal more physicians preferring the security of being employees than starting their own practices.[11] Given these trends, job fit will inform our understanding of how personal and job characteristics facilitate recruitment, performance, satisfaction, and longevity of physician employees.

BACKGROUND

Virtually all hospitalists work in organizationshospitalsand are employees of hospitals, medical schools, physician group practices, or management companies, and therefore invariably function within organizational structures and systems.[7] In spite of their rapid growth in numbers, many employers have faced difficulties recruiting and retaining enough hospitalists to fill their staffing needs. Consequently, the US hospitalist workforce today is characterized by high salaries, work load, and attrition rates.[12]

In this evolving unsaturated market, the attraction‐selection‐attrition framework[13] provides a theoretical construct that predicts that hospitalists and their employers would seek congruence of goals and values early in their relationship through a process of trial and error. This framework assumes that early interactions between workers and organizations serve as opportunities for them to understand if job fit is poor and dissociate or remain affiliated as long as job fit is mutually acceptable. Therefore, job switching on average is expected to increase job fit because workers and organizations gain a better understanding of their own goals and values and choose more wisely the next time.

Other theoretical frameworks, such as the job characteristic model,[14] suggest that over time as workers stay at the same job, they continue to maintain and improve job fit through various workplace‐ or self‐modification strategies. For example, seniority status may have privileges (eg, less undesirable call), or workers may create privileged niches through the acquisition of new skills and abilities over time. Hospitalists' tendency to diversify their work‐related activities by incorporating administrative and teaching responsibilities[15] may thus contribute to improving job fit. Additionally, as a measure of complementarity among people who work together, job fit may be influenced by the quality of relationships among hospitalists and their coworkers through their reorientation to the prevailing organizational climate[16, 17] and increasing socialization.[18] Finally, given that experiential learning is known to contribute to better hospitalist work performance,[19] job fit may affect productivity and clinical outcomes vis‐‐vis quality of work life.

To test the validity of these assumptions in a sample of hospitalists, we critically appraised the following 4 hypotheses:

• Hypothesis 1 (H1): Job attrition and reselection improves job fit among hospitalists entering the job market.
• Hypothesis 2 (H2): Better job fit is achieved through hospitalists engaging a variety of personal skills and abilities.
• Hypothesis 3 (H3): Job fit increases with hospitalists' job duration together with socialization and internalization of organizational values.
• Hypothesis 4 (H4): Job fit is correlated with hospitalists' quality of work life.

METHODS

Analysis was performed on data from the 2009 to 2010 Hospital Medicine Physician Worklife Survey. The sample frame included nonmembers and members of Society of Hospital Medicine (SHM). Details about sampling strategy, data collection, and data quality are available in previous publications.[7, 20] The 118‐item survey instrument, including 9 demographic items and 24 practice and job characteristic items, was administered by mail. Examples of information solicited through these items included respondents' practice model, the number of hospitalist jobs they have held, and the specific kinds of clinical and nonclinical activities they performed as part of their current job.

We used a reliable but broad and generic measure of self‐perceived person‐job fit.[21] The survey items of the 5‐point Likert‐type scale anchored between strongly disagree and strongly agree were: I feel that my work utilizes my full abilities, I feel competent and fully able to handle my job, my job gives me the chance to do the things I feel I do best, I feel that my job and I are well‐matched, I feel I have adequate preparation for the job I now hold. The quality of hospitalists' relationships with physician colleagues, staff, and patients as well as job satisfaction was measured using scales adapted from the Physician Worklife Study.[22] Organizational climate was measured using an adapted scale from the Minimizing Error, Maximizing Outcome study incorporating 3 items from the cohesiveness subscale, 4 items from the organizational trust subscale, and 1 item from the quality emphasis subscale that were most pertinent to hospitalists' relationship with their organizations.[23] Intent to leave practice or reduce work hours was measured using 5 items from the Multi‐Center Hospitalist Survey Project.[24] Frequency of participation in suboptimal patient care was measured by adapting 3 items from the suboptimal reported practice subscale and 2 items from the suboptimal patient care subscale developed by Shanafelt et al.[25] Stress and job burnout were assessed using validated measures.[26, 27] Detailed descriptions of the response rate calculation and imputation of missing item data are available in previous publications.[7, 20]

Mean, variance, range, and skew were used to characterize the responses to the job fit survey scale. A table of respondent characteristics was constructed. A visual representation of job fit by individual hospitalist year in current practice was created, first, by plotting a locally weighted scatterplot smoothing curve to examine the shape of the general relationship, and second, by fitting a similarly contoured functional polynomial curve with 95% confidence intervals (CI) to a plot of the mean and interquartile range of job fit for each year in current practice. Spearman partial correlations were calculated for job fit and each of the 5 items addressing likelihood of leaving practice or reducing workload adjusted for gender to control for the higher proportion of women who plan to work part time. Median (interquartile range) job fit was calculated for categories defined by the number of job changes and compared with the reference category (no job change) using the nonparametric rank sum test for comparing non‐normally distributed data. Multivariate logistic regression models were used to calculate the odds ratio (OR) of participating in each of several clinical and nonclinical hospitalist activities between respondents whose job fit score was optimal (5 on a 5‐point scale) and less than optimal controlling for covariates that influence the likelihood of participating in these activities (years in current practice, practice model, and specialty training). A Spearman correlation matrix was created to assess interscale correlations among organizational parameters (years in current practice, job fit, organizational climate, and relationship with colleagues, staff, and patients). Finally, a separate Spearman correlation matrix was created to assess the interscale correlations among individual worker parameters (job fit, suboptimal patient care, job burnout, stress, and job satisfaction). Statistical significance was defined as P value <0.05, and all analyses were performed on Stata 11.0 (StataCorp, College Station, TX). The Northwestern University institutional review board approved this study.

RESULTS

Respondents included 816 hospitalists belonging to around 700 unique organizations. The adjusted response rate from the stratified sample was 26%. Respondents and nonrespondents were similar with regard to geographic region and model of practice, but respondents were more likely to be members of the SHM than nonrespondents. Panel A of Table 1 shows the demographic characteristics of the respondents. The mean age was 44.3 years, and about one‐third were women. The average hospitalist had about 7 years of experience in the specialty and about 5 years with their current hospitalist job. The majority were trained in internal medicine or one of its subspecialties, whereas pediatricians, family physicians, and physicians with other training made up the remainder.

Job fit was highly skewed toward optimum fit, with a mean of 4.3 on a scale of 1 to 5, with a narrow standard deviation of 0.7. The poorest job fit was reported by 0.3%, whereas optimal fit was reported by 21% of respondents. Job fit plotted against years in current practice had a logarithmic appearance typical of learning curves (Figure 1). An inflection point was visualized at around 2 years. For the purposes of this article, we refer to hospitalists' experience in the first 2 years of a job as an assimilation period, which is marked by a steep increase in job fit early when rapid learning or attrition took place. The years beyond the inflection point are characterized as an advancement period, when a more attenuated rise in job fit was experienced with time. The Spearman correlation between job fit and years in practice during the advancement period was 0.145 (n = 678, P < 0.001). Panel B of Table 1 displays the characteristics of respondents separately for the assimilation and advancement cohorts. Assimilation hospitalists in our sample had a mean age of 41.9 years and mean on‐the‐job experience of 4.3 years, reflecting that many hospitalists in the first 2 years of a job have made at least 1 job change in the past.

Figure 1

To show the effects of attrition and reselection, we first evaluated the proposition that hospitalists experience attrition (ie, intend to leave their jobs) in response to poor fit. Table 2 shows the correlations between job fit and the self‐reported intent to leave practice or reduce workload separately for the assimilation and advancement periods. For hospitalists in the assimilation period, job fit was negatively correlated with intent to leave current practice within 2 years and to leave hospital medicine within 5 years (P = 0.010 and 0.043, respectively). Hospitalists with <2 years in their current job, therefore, tended to consider attrition but not workload reduction to deal with poor job fit. On the other hand, hospitalists in the advancement period considered both attrition and workload reduction strategies in response to poor fit (all P < 0.001).

In Table 3, we further compared the median job fit across categories for the number of job switches. The median job fit during the assimilation period of hospitalists who had made 1 job change was slightly but statistically higher than the job fit of their counterparts who never left their first job (4.4 vs 4.0, P = 0.046). This suggests that job switching by hospitalists early in their jobs is associated with improved job fit (H1). However, the fit during the assimilation period of hospitalists who switched jobs twice or more was statistically no different from the fit of those in their first jobs, suggesting that the effect of the attrition‐reselection strategy is weak or inconsistent. The job fit for advancement period hospitalists was also different across the job change and no‐change categories. However, in the case of hospitalists later in their jobs, the median job fit was slightly but statistically lower among those who made job changes, revealing the potential drop in job fit that occurs when a hospitalist already established in his or her job starts over again in a new setting.

We hypothesized that hospitalists who achieved high job fit within a particular job were more likely to have engaged in activities that utilize a wider spectrum of their abilities. As shown in Table 4, hospitalists in the highest quartile of job fit were associated with a general trend toward higher odds of participating in a variety of common clinical and nonclinical hospitalist activities, but only the odds ratio associated with teaching achieved statistical significance (OR: 1.53, 95% CI: 1.01‐2.31) (H2).

Socialization with peers and the gradual sharing of values within organizations are hypothesized mechanisms for increasing job fit with time. We found that the number of years in current practice was positively correlated with job fit (Spearman coefficient R = 0.149, P < 0.001), organizational climate (R = 0.128, P < 0.001), and relationship with nonphysician staff (R = 0.102, P < 0.01). The association between years in practice and relationship with physician colleagues were weaker (R = 0.079, P < 0.05). Consistent with the episodic nature of patients' encounters with hospitalists, the measure of patient relationships was not significantly associated with length of time in job. In addition, we found substantial correlations among job fit, organizational climate, and all the relational measures (all R > 0.280, P < 0.001), indicating that hospitalists increasingly share the values of their organizations over time (H3).

Finally, we also hypothesized that poor job fit is associated with poor performance and quality of work life. Strong correlations with job fit were noted for stress (R = 0.307, P < 0.001), job burnout (R = 0.360, P < 0.001), and job satisfaction (R = 0.570, P < 0.001). Job fit (R = 0.147, P < 0.001), job burnout (R = 0.236, P < 0.001), stress (R = 0.305, P < 0.001), and job satisfaction (R = 0.224, P < 0.001) were all significantly correlated with the frequency of participating in suboptimal care (H4).

DISCUSSION

In this exploratory analysis, we validated in the hospitalist workforce several assumptions about person‐job fit that have been observed in workers of other industries. We observed attrition‐reselection (ie, job switching) as a strategy used by physicians to achieve better fit early in their job tenure, whereas job modification appeared to be more effective than attrition‐reselection among physicians already established in their jobs. We provided weak but plausible evidence that physicians with optimal job fit had a tendency to participate in activities (eg, teaching) that engage a wider set of interests and abilities. We also demonstrated the growth in hospitalists sharing the values of their organization through the time‐dependent associations among organizational climate, relational measures, and job fit. Finally, we found that physicians with suboptimal job fit were more likely to report poor performance in their work compared to those indicating optimal fit.

Our previous analysis of data from the Hospital Medicine Physician Worklife Survey exposed the widely variable work characteristics of hospitalist jobs in the US market and the equally variable preferences and priorities of individual hospitalists in selecting their work setting.[7] The implication of our present study is that hospitalists achieve the high levels of observed job fit using various strategies that aid their alignment with their employment. One of these strategies involves time, but physician longevity in practice may be both a determinant and product of good job fit. Although early job attrition may be necessary for fitting the right hospitalists to the right jobs, employers may appreciate the importance of retaining experienced hospitalists not only for cost and performance considerations but also for the growth of social capital in organizations consisting of enduring individuals. As our data suggest that hospitalists grow with their jobs, physicians may experience better fit with jobs that flexibly couple their work demands with benefits that address their individual work‐life needs over time. Another implication of this study is that job fit is a useful and predictive measure of job selection, performance, and retention. In light of studies that expose the limitations of job satisfaction as a measure influenced more by workers' dispositional affect (ie, their temperament and outlook) than their compatibility with their jobs,[28] job fit may add a functional dimension to traditional employee feedback measures.

There are limitations to this analysis. The most notable is the low survey response rate. Two reasons contributed to the fairly low rate of return. First, the original sampling frame included many outdated addresses and names of individuals who did not meet inclusion criteria. Although all sampled individuals who would have been excluded from the study could not be identified, we calculated our response rate without accounting for the proportion of potential ineligibles in the denominator population [Response Rate 2 (RR2) according to standards of the American Association of Public Opinion Research].[29] Second, the response rates of physician surveys have seen a steady decline over the years.[30] Respondents to our survey may be older and more experienced than US hospitalists in general. Although concerns about bias from under‐reporting cannot be fully addressed, we believe that the study sample is adequate for this preliminary study intended to translate the evidence of observed phenomena from the nonphysician to the physician workforces. The suboptimal response characteristics (high skew and low variability) of the generic person‐job fit survey scale used in this study indicate that a reliable survey instrument specifically designed to measure physician‐job fit need to be constructed de novo and validated for any future study. Although we performed simple analyses to support our assertions, few of our subanalyses may be underpowered, contributing to overinterpretation of the data. Additional empirical work is also necessary to assess the generalizability of this study's claims in other medical and surgical specialties. Such studies would also allow measurement of the sensitivity and specificity of physicians' self‐identification of poor job fit. Finally, additional investigations of this time‐dependent construct are more appropriately performed using a longitudinal study design to overcome the limitations inherent in this cross‐sectional analysis. Our conclusions about the time‐dependent features of job fit may be explained by other characteristics such as generational and cultural differences among hospitalists with varying experience.

As the US healthcare system reorganizes to bolster accountability,[31] we anticipate increasing interdependence between physicians and their employer organizations. Ultimately, the desired accountability in healthcare is likely to be obtained if physicians function not only as passive and interchangeable employees but as active stakeholders in the achievement of each organization's goals. A methodology for assessing the alignment of physicians and their jobs will continue to be important along the way.

Person‐organization fit concerns the conditions and consequences of compatibility between people and the organizations for which they work.[1] Studies of other industries have demonstrated that person‐organization fit informs the way individuals join, perform in, and are retained by organizations.[2] Person‐job fit is a closely related subordinate concept that concerns the alignment of workers and their job in as much as workers have needs that their job supplies, or conversely, jobs have requirements that certain workers' abilities can help meet.[3] Explorations of job fit in physicians and their work have recently emerged in a few investigations published in medical journals.[4, 5, 6, 7, 8] Further expanding the understanding of fit between physicians and their employment is important, because the decline of solo practices and recent emphasis on team‐based care have led to a growing number of US physicians working in organizations.[9]

The movement of physicians into employed situations may continue if certain types of Accountable Care Organizations take root.[10] And physicians may be primed to join employer organizations based on current career priorities of individuals in American society. Surveys of medical residents entering the workforce reveal more physicians preferring the security of being employees than starting their own practices.[11] Given these trends, job fit will inform our understanding of how personal and job characteristics facilitate recruitment, performance, satisfaction, and longevity of physician employees.

BACKGROUND

Virtually all hospitalists work in organizationshospitalsand are employees of hospitals, medical schools, physician group practices, or management companies, and therefore invariably function within organizational structures and systems.[7] In spite of their rapid growth in numbers, many employers have faced difficulties recruiting and retaining enough hospitalists to fill their staffing needs. Consequently, the US hospitalist workforce today is characterized by high salaries, work load, and attrition rates.[12]

In this evolving unsaturated market, the attraction‐selection‐attrition framework[13] provides a theoretical construct that predicts that hospitalists and their employers would seek congruence of goals and values early in their relationship through a process of trial and error. This framework assumes that early interactions between workers and organizations serve as opportunities for them to understand if job fit is poor and dissociate or remain affiliated as long as job fit is mutually acceptable. Therefore, job switching on average is expected to increase job fit because workers and organizations gain a better understanding of their own goals and values and choose more wisely the next time.

Other theoretical frameworks, such as the job characteristic model,[14] suggest that over time as workers stay at the same job, they continue to maintain and improve job fit through various workplace‐ or self‐modification strategies. For example, seniority status may have privileges (eg, less undesirable call), or workers may create privileged niches through the acquisition of new skills and abilities over time. Hospitalists' tendency to diversify their work‐related activities by incorporating administrative and teaching responsibilities[15] may thus contribute to improving job fit. Additionally, as a measure of complementarity among people who work together, job fit may be influenced by the quality of relationships among hospitalists and their coworkers through their reorientation to the prevailing organizational climate[16, 17] and increasing socialization.[18] Finally, given that experiential learning is known to contribute to better hospitalist work performance,[19] job fit may affect productivity and clinical outcomes vis‐‐vis quality of work life.

To test the validity of these assumptions in a sample of hospitalists, we critically appraised the following 4 hypotheses:

• Hypothesis 1 (H1): Job attrition and reselection improves job fit among hospitalists entering the job market.
• Hypothesis 2 (H2): Better job fit is achieved through hospitalists engaging a variety of personal skills and abilities.
• Hypothesis 3 (H3): Job fit increases with hospitalists' job duration together with socialization and internalization of organizational values.
• Hypothesis 4 (H4): Job fit is correlated with hospitalists' quality of work life.

METHODS

Analysis was performed on data from the 2009 to 2010 Hospital Medicine Physician Worklife Survey. The sample frame included nonmembers and members of Society of Hospital Medicine (SHM). Details about sampling strategy, data collection, and data quality are available in previous publications.[7, 20] The 118‐item survey instrument, including 9 demographic items and 24 practice and job characteristic items, was administered by mail. Examples of information solicited through these items included respondents' practice model, the number of hospitalist jobs they have held, and the specific kinds of clinical and nonclinical activities they performed as part of their current job.

We used a reliable but broad and generic measure of self‐perceived person‐job fit.[21] The survey items of the 5‐point Likert‐type scale anchored between strongly disagree and strongly agree were: I feel that my work utilizes my full abilities, I feel competent and fully able to handle my job, my job gives me the chance to do the things I feel I do best, I feel that my job and I are well‐matched, I feel I have adequate preparation for the job I now hold. The quality of hospitalists' relationships with physician colleagues, staff, and patients as well as job satisfaction was measured using scales adapted from the Physician Worklife Study.[22] Organizational climate was measured using an adapted scale from the Minimizing Error, Maximizing Outcome study incorporating 3 items from the cohesiveness subscale, 4 items from the organizational trust subscale, and 1 item from the quality emphasis subscale that were most pertinent to hospitalists' relationship with their organizations.[23] Intent to leave practice or reduce work hours was measured using 5 items from the Multi‐Center Hospitalist Survey Project.[24] Frequency of participation in suboptimal patient care was measured by adapting 3 items from the suboptimal reported practice subscale and 2 items from the suboptimal patient care subscale developed by Shanafelt et al.[25] Stress and job burnout were assessed using validated measures.[26, 27] Detailed descriptions of the response rate calculation and imputation of missing item data are available in previous publications.[7, 20]

Mean, variance, range, and skew were used to characterize the responses to the job fit survey scale. A table of respondent characteristics was constructed. A visual representation of job fit by individual hospitalist year in current practice was created, first, by plotting a locally weighted scatterplot smoothing curve to examine the shape of the general relationship, and second, by fitting a similarly contoured functional polynomial curve with 95% confidence intervals (CI) to a plot of the mean and interquartile range of job fit for each year in current practice. Spearman partial correlations were calculated for job fit and each of the 5 items addressing likelihood of leaving practice or reducing workload adjusted for gender to control for the higher proportion of women who plan to work part time. Median (interquartile range) job fit was calculated for categories defined by the number of job changes and compared with the reference category (no job change) using the nonparametric rank sum test for comparing non‐normally distributed data. Multivariate logistic regression models were used to calculate the odds ratio (OR) of participating in each of several clinical and nonclinical hospitalist activities between respondents whose job fit score was optimal (5 on a 5‐point scale) and less than optimal controlling for covariates that influence the likelihood of participating in these activities (years in current practice, practice model, and specialty training). A Spearman correlation matrix was created to assess interscale correlations among organizational parameters (years in current practice, job fit, organizational climate, and relationship with colleagues, staff, and patients). Finally, a separate Spearman correlation matrix was created to assess the interscale correlations among individual worker parameters (job fit, suboptimal patient care, job burnout, stress, and job satisfaction). Statistical significance was defined as P value <0.05, and all analyses were performed on Stata 11.0 (StataCorp, College Station, TX). The Northwestern University institutional review board approved this study.

RESULTS

Respondents included 816 hospitalists belonging to around 700 unique organizations. The adjusted response rate from the stratified sample was 26%. Respondents and nonrespondents were similar with regard to geographic region and model of practice, but respondents were more likely to be members of the SHM than nonrespondents. Panel A of Table 1 shows the demographic characteristics of the respondents. The mean age was 44.3 years, and about one‐third were women. The average hospitalist had about 7 years of experience in the specialty and about 5 years with their current hospitalist job. The majority were trained in internal medicine or one of its subspecialties, whereas pediatricians, family physicians, and physicians with other training made up the remainder.

Job fit was highly skewed toward optimum fit, with a mean of 4.3 on a scale of 1 to 5, with a narrow standard deviation of 0.7. The poorest job fit was reported by 0.3%, whereas optimal fit was reported by 21% of respondents. Job fit plotted against years in current practice had a logarithmic appearance typical of learning curves (Figure 1). An inflection point was visualized at around 2 years. For the purposes of this article, we refer to hospitalists' experience in the first 2 years of a job as an assimilation period, which is marked by a steep increase in job fit early when rapid learning or attrition took place. The years beyond the inflection point are characterized as an advancement period, when a more attenuated rise in job fit was experienced with time. The Spearman correlation between job fit and years in practice during the advancement period was 0.145 (n = 678, P < 0.001). Panel B of Table 1 displays the characteristics of respondents separately for the assimilation and advancement cohorts. Assimilation hospitalists in our sample had a mean age of 41.9 years and mean on‐the‐job experience of 4.3 years, reflecting that many hospitalists in the first 2 years of a job have made at least 1 job change in the past.

Figure 1

To show the effects of attrition and reselection, we first evaluated the proposition that hospitalists experience attrition (ie, intend to leave their jobs) in response to poor fit. Table 2 shows the correlations between job fit and the self‐reported intent to leave practice or reduce workload separately for the assimilation and advancement periods. For hospitalists in the assimilation period, job fit was negatively correlated with intent to leave current practice within 2 years and to leave hospital medicine within 5 years (P = 0.010 and 0.043, respectively). Hospitalists with <2 years in their current job, therefore, tended to consider attrition but not workload reduction to deal with poor job fit. On the other hand, hospitalists in the advancement period considered both attrition and workload reduction strategies in response to poor fit (all P < 0.001).

In Table 3, we further compared the median job fit across categories for the number of job switches. The median job fit during the assimilation period of hospitalists who had made 1 job change was slightly but statistically higher than the job fit of their counterparts who never left their first job (4.4 vs 4.0, P = 0.046). This suggests that job switching by hospitalists early in their jobs is associated with improved job fit (H1). However, the fit during the assimilation period of hospitalists who switched jobs twice or more was statistically no different from the fit of those in their first jobs, suggesting that the effect of the attrition‐reselection strategy is weak or inconsistent. The job fit for advancement period hospitalists was also different across the job change and no‐change categories. However, in the case of hospitalists later in their jobs, the median job fit was slightly but statistically lower among those who made job changes, revealing the potential drop in job fit that occurs when a hospitalist already established in his or her job starts over again in a new setting.

We hypothesized that hospitalists who achieved high job fit within a particular job were more likely to have engaged in activities that utilize a wider spectrum of their abilities. As shown in Table 4, hospitalists in the highest quartile of job fit were associated with a general trend toward higher odds of participating in a variety of common clinical and nonclinical hospitalist activities, but only the odds ratio associated with teaching achieved statistical significance (OR: 1.53, 95% CI: 1.01‐2.31) (H2).

Socialization with peers and the gradual sharing of values within organizations are hypothesized mechanisms for increasing job fit with time. We found that the number of years in current practice was positively correlated with job fit (Spearman coefficient R = 0.149, P < 0.001), organizational climate (R = 0.128, P < 0.001), and relationship with nonphysician staff (R = 0.102, P < 0.01). The association between years in practice and relationship with physician colleagues were weaker (R = 0.079, P < 0.05). Consistent with the episodic nature of patients' encounters with hospitalists, the measure of patient relationships was not significantly associated with length of time in job. In addition, we found substantial correlations among job fit, organizational climate, and all the relational measures (all R > 0.280, P < 0.001), indicating that hospitalists increasingly share the values of their organizations over time (H3).

Finally, we also hypothesized that poor job fit is associated with poor performance and quality of work life. Strong correlations with job fit were noted for stress (R = 0.307, P < 0.001), job burnout (R = 0.360, P < 0.001), and job satisfaction (R = 0.570, P < 0.001). Job fit (R = 0.147, P < 0.001), job burnout (R = 0.236, P < 0.001), stress (R = 0.305, P < 0.001), and job satisfaction (R = 0.224, P < 0.001) were all significantly correlated with the frequency of participating in suboptimal care (H4).

DISCUSSION

In this exploratory analysis, we validated in the hospitalist workforce several assumptions about person‐job fit that have been observed in workers of other industries. We observed attrition‐reselection (ie, job switching) as a strategy used by physicians to achieve better fit early in their job tenure, whereas job modification appeared to be more effective than attrition‐reselection among physicians already established in their jobs. We provided weak but plausible evidence that physicians with optimal job fit had a tendency to participate in activities (eg, teaching) that engage a wider set of interests and abilities. We also demonstrated the growth in hospitalists sharing the values of their organization through the time‐dependent associations among organizational climate, relational measures, and job fit. Finally, we found that physicians with suboptimal job fit were more likely to report poor performance in their work compared to those indicating optimal fit.

Our previous analysis of data from the Hospital Medicine Physician Worklife Survey exposed the widely variable work characteristics of hospitalist jobs in the US market and the equally variable preferences and priorities of individual hospitalists in selecting their work setting.[7] The implication of our present study is that hospitalists achieve the high levels of observed job fit using various strategies that aid their alignment with their employment. One of these strategies involves time, but physician longevity in practice may be both a determinant and product of good job fit. Although early job attrition may be necessary for fitting the right hospitalists to the right jobs, employers may appreciate the importance of retaining experienced hospitalists not only for cost and performance considerations but also for the growth of social capital in organizations consisting of enduring individuals. As our data suggest that hospitalists grow with their jobs, physicians may experience better fit with jobs that flexibly couple their work demands with benefits that address their individual work‐life needs over time. Another implication of this study is that job fit is a useful and predictive measure of job selection, performance, and retention. In light of studies that expose the limitations of job satisfaction as a measure influenced more by workers' dispositional affect (ie, their temperament and outlook) than their compatibility with their jobs,[28] job fit may add a functional dimension to traditional employee feedback measures.

There are limitations to this analysis. The most notable is the low survey response rate. Two reasons contributed to the fairly low rate of return. First, the original sampling frame included many outdated addresses and names of individuals who did not meet inclusion criteria. Although all sampled individuals who would have been excluded from the study could not be identified, we calculated our response rate without accounting for the proportion of potential ineligibles in the denominator population [Response Rate 2 (RR2) according to standards of the American Association of Public Opinion Research].[29] Second, the response rates of physician surveys have seen a steady decline over the years.[30] Respondents to our survey may be older and more experienced than US hospitalists in general. Although concerns about bias from under‐reporting cannot be fully addressed, we believe that the study sample is adequate for this preliminary study intended to translate the evidence of observed phenomena from the nonphysician to the physician workforces. The suboptimal response characteristics (high skew and low variability) of the generic person‐job fit survey scale used in this study indicate that a reliable survey instrument specifically designed to measure physician‐job fit need to be constructed de novo and validated for any future study. Although we performed simple analyses to support our assertions, few of our subanalyses may be underpowered, contributing to overinterpretation of the data. Additional empirical work is also necessary to assess the generalizability of this study's claims in other medical and surgical specialties. Such studies would also allow measurement of the sensitivity and specificity of physicians' self‐identification of poor job fit. Finally, additional investigations of this time‐dependent construct are more appropriately performed using a longitudinal study design to overcome the limitations inherent in this cross‐sectional analysis. Our conclusions about the time‐dependent features of job fit may be explained by other characteristics such as generational and cultural differences among hospitalists with varying experience.

As the US healthcare system reorganizes to bolster accountability,[31] we anticipate increasing interdependence between physicians and their employer organizations. Ultimately, the desired accountability in healthcare is likely to be obtained if physicians function not only as passive and interchangeable employees but as active stakeholders in the achievement of each organization's goals. A methodology for assessing the alignment of physicians and their jobs will continue to be important along the way.

Person‐organization fit concerns the conditions and consequences of compatibility between people and the organizations for which they work.[1] Studies of other industries have demonstrated that person‐organization fit informs the way individuals join, perform in, and are retained by organizations.[2] Person‐job fit is a closely related subordinate concept that concerns the alignment of workers and their job in as much as workers have needs that their job supplies, or conversely, jobs have requirements that certain workers' abilities can help meet.[3] Explorations of job fit in physicians and their work have recently emerged in a few investigations published in medical journals.[4, 5, 6, 7, 8] Further expanding the understanding of fit between physicians and their employment is important, because the decline of solo practices and recent emphasis on team‐based care have led to a growing number of US physicians working in organizations.[9]

The movement of physicians into employed situations may continue if certain types of Accountable Care Organizations take root.[10] And physicians may be primed to join employer organizations based on current career priorities of individuals in American society. Surveys of medical residents entering the workforce reveal more physicians preferring the security of being employees than starting their own practices.[11] Given these trends, job fit will inform our understanding of how personal and job characteristics facilitate recruitment, performance, satisfaction, and longevity of physician employees.

BACKGROUND

Virtually all hospitalists work in organizationshospitalsand are employees of hospitals, medical schools, physician group practices, or management companies, and therefore invariably function within organizational structures and systems.[7] In spite of their rapid growth in numbers, many employers have faced difficulties recruiting and retaining enough hospitalists to fill their staffing needs. Consequently, the US hospitalist workforce today is characterized by high salaries, work load, and attrition rates.[12]

In this evolving unsaturated market, the attraction‐selection‐attrition framework[13] provides a theoretical construct that predicts that hospitalists and their employers would seek congruence of goals and values early in their relationship through a process of trial and error. This framework assumes that early interactions between workers and organizations serve as opportunities for them to understand if job fit is poor and dissociate or remain affiliated as long as job fit is mutually acceptable. Therefore, job switching on average is expected to increase job fit because workers and organizations gain a better understanding of their own goals and values and choose more wisely the next time.

Other theoretical frameworks, such as the job characteristic model,[14] suggest that over time as workers stay at the same job, they continue to maintain and improve job fit through various workplace‐ or self‐modification strategies. For example, seniority status may have privileges (eg, less undesirable call), or workers may create privileged niches through the acquisition of new skills and abilities over time. Hospitalists' tendency to diversify their work‐related activities by incorporating administrative and teaching responsibilities[15] may thus contribute to improving job fit. Additionally, as a measure of complementarity among people who work together, job fit may be influenced by the quality of relationships among hospitalists and their coworkers through their reorientation to the prevailing organizational climate[16, 17] and increasing socialization.[18] Finally, given that experiential learning is known to contribute to better hospitalist work performance,[19] job fit may affect productivity and clinical outcomes vis‐‐vis quality of work life.

To test the validity of these assumptions in a sample of hospitalists, we critically appraised the following 4 hypotheses:

• Hypothesis 1 (H1): Job attrition and reselection improves job fit among hospitalists entering the job market.
• Hypothesis 2 (H2): Better job fit is achieved through hospitalists engaging a variety of personal skills and abilities.
• Hypothesis 3 (H3): Job fit increases with hospitalists' job duration together with socialization and internalization of organizational values.
• Hypothesis 4 (H4): Job fit is correlated with hospitalists' quality of work life.

METHODS

Analysis was performed on data from the 2009 to 2010 Hospital Medicine Physician Worklife Survey. The sample frame included nonmembers and members of Society of Hospital Medicine (SHM). Details about sampling strategy, data collection, and data quality are available in previous publications.[7, 20] The 118‐item survey instrument, including 9 demographic items and 24 practice and job characteristic items, was administered by mail. Examples of information solicited through these items included respondents' practice model, the number of hospitalist jobs they have held, and the specific kinds of clinical and nonclinical activities they performed as part of their current job.

We used a reliable but broad and generic measure of self‐perceived person‐job fit.[21] The survey items of the 5‐point Likert‐type scale anchored between strongly disagree and strongly agree were: I feel that my work utilizes my full abilities, I feel competent and fully able to handle my job, my job gives me the chance to do the things I feel I do best, I feel that my job and I are well‐matched, I feel I have adequate preparation for the job I now hold. The quality of hospitalists' relationships with physician colleagues, staff, and patients as well as job satisfaction was measured using scales adapted from the Physician Worklife Study.[22] Organizational climate was measured using an adapted scale from the Minimizing Error, Maximizing Outcome study incorporating 3 items from the cohesiveness subscale, 4 items from the organizational trust subscale, and 1 item from the quality emphasis subscale that were most pertinent to hospitalists' relationship with their organizations.[23] Intent to leave practice or reduce work hours was measured using 5 items from the Multi‐Center Hospitalist Survey Project.[24] Frequency of participation in suboptimal patient care was measured by adapting 3 items from the suboptimal reported practice subscale and 2 items from the suboptimal patient care subscale developed by Shanafelt et al.[25] Stress and job burnout were assessed using validated measures.[26, 27] Detailed descriptions of the response rate calculation and imputation of missing item data are available in previous publications.[7, 20]

Mean, variance, range, and skew were used to characterize the responses to the job fit survey scale. A table of respondent characteristics was constructed. A visual representation of job fit by individual hospitalist year in current practice was created, first, by plotting a locally weighted scatterplot smoothing curve to examine the shape of the general relationship, and second, by fitting a similarly contoured functional polynomial curve with 95% confidence intervals (CI) to a plot of the mean and interquartile range of job fit for each year in current practice. Spearman partial correlations were calculated for job fit and each of the 5 items addressing likelihood of leaving practice or reducing workload adjusted for gender to control for the higher proportion of women who plan to work part time. Median (interquartile range) job fit was calculated for categories defined by the number of job changes and compared with the reference category (no job change) using the nonparametric rank sum test for comparing non‐normally distributed data. Multivariate logistic regression models were used to calculate the odds ratio (OR) of participating in each of several clinical and nonclinical hospitalist activities between respondents whose job fit score was optimal (5 on a 5‐point scale) and less than optimal controlling for covariates that influence the likelihood of participating in these activities (years in current practice, practice model, and specialty training). A Spearman correlation matrix was created to assess interscale correlations among organizational parameters (years in current practice, job fit, organizational climate, and relationship with colleagues, staff, and patients). Finally, a separate Spearman correlation matrix was created to assess the interscale correlations among individual worker parameters (job fit, suboptimal patient care, job burnout, stress, and job satisfaction). Statistical significance was defined as P value <0.05, and all analyses were performed on Stata 11.0 (StataCorp, College Station, TX). The Northwestern University institutional review board approved this study.

RESULTS

Respondents included 816 hospitalists belonging to around 700 unique organizations. The adjusted response rate from the stratified sample was 26%. Respondents and nonrespondents were similar with regard to geographic region and model of practice, but respondents were more likely to be members of the SHM than nonrespondents. Panel A of Table 1 shows the demographic characteristics of the respondents. The mean age was 44.3 years, and about one‐third were women. The average hospitalist had about 7 years of experience in the specialty and about 5 years with their current hospitalist job. The majority were trained in internal medicine or one of its subspecialties, whereas pediatricians, family physicians, and physicians with other training made up the remainder.

Job fit was highly skewed toward optimum fit, with a mean of 4.3 on a scale of 1 to 5, with a narrow standard deviation of 0.7. The poorest job fit was reported by 0.3%, whereas optimal fit was reported by 21% of respondents. Job fit plotted against years in current practice had a logarithmic appearance typical of learning curves (Figure 1). An inflection point was visualized at around 2 years. For the purposes of this article, we refer to hospitalists' experience in the first 2 years of a job as an assimilation period, which is marked by a steep increase in job fit early when rapid learning or attrition took place. The years beyond the inflection point are characterized as an advancement period, when a more attenuated rise in job fit was experienced with time. The Spearman correlation between job fit and years in practice during the advancement period was 0.145 (n = 678, P < 0.001). Panel B of Table 1 displays the characteristics of respondents separately for the assimilation and advancement cohorts. Assimilation hospitalists in our sample had a mean age of 41.9 years and mean on‐the‐job experience of 4.3 years, reflecting that many hospitalists in the first 2 years of a job have made at least 1 job change in the past.

Figure 1

To show the effects of attrition and reselection, we first evaluated the proposition that hospitalists experience attrition (ie, intend to leave their jobs) in response to poor fit. Table 2 shows the correlations between job fit and the self‐reported intent to leave practice or reduce workload separately for the assimilation and advancement periods. For hospitalists in the assimilation period, job fit was negatively correlated with intent to leave current practice within 2 years and to leave hospital medicine within 5 years (P = 0.010 and 0.043, respectively). Hospitalists with <2 years in their current job, therefore, tended to consider attrition but not workload reduction to deal with poor job fit. On the other hand, hospitalists in the advancement period considered both attrition and workload reduction strategies in response to poor fit (all P < 0.001).

In Table 3, we further compared the median job fit across categories for the number of job switches. The median job fit during the assimilation period of hospitalists who had made 1 job change was slightly but statistically higher than the job fit of their counterparts who never left their first job (4.4 vs 4.0, P = 0.046). This suggests that job switching by hospitalists early in their jobs is associated with improved job fit (H1). However, the fit during the assimilation period of hospitalists who switched jobs twice or more was statistically no different from the fit of those in their first jobs, suggesting that the effect of the attrition‐reselection strategy is weak or inconsistent. The job fit for advancement period hospitalists was also different across the job change and no‐change categories. However, in the case of hospitalists later in their jobs, the median job fit was slightly but statistically lower among those who made job changes, revealing the potential drop in job fit that occurs when a hospitalist already established in his or her job starts over again in a new setting.

We hypothesized that hospitalists who achieved high job fit within a particular job were more likely to have engaged in activities that utilize a wider spectrum of their abilities. As shown in Table 4, hospitalists in the highest quartile of job fit were associated with a general trend toward higher odds of participating in a variety of common clinical and nonclinical hospitalist activities, but only the odds ratio associated with teaching achieved statistical significance (OR: 1.53, 95% CI: 1.01‐2.31) (H2).

Socialization with peers and the gradual sharing of values within organizations are hypothesized mechanisms for increasing job fit with time. We found that the number of years in current practice was positively correlated with job fit (Spearman coefficient R = 0.149, P < 0.001), organizational climate (R = 0.128, P < 0.001), and relationship with nonphysician staff (R = 0.102, P < 0.01). The association between years in practice and relationship with physician colleagues were weaker (R = 0.079, P < 0.05). Consistent with the episodic nature of patients' encounters with hospitalists, the measure of patient relationships was not significantly associated with length of time in job. In addition, we found substantial correlations among job fit, organizational climate, and all the relational measures (all R > 0.280, P < 0.001), indicating that hospitalists increasingly share the values of their organizations over time (H3).

Finally, we also hypothesized that poor job fit is associated with poor performance and quality of work life. Strong correlations with job fit were noted for stress (R = 0.307, P < 0.001), job burnout (R = 0.360, P < 0.001), and job satisfaction (R = 0.570, P < 0.001). Job fit (R = 0.147, P < 0.001), job burnout (R = 0.236, P < 0.001), stress (R = 0.305, P < 0.001), and job satisfaction (R = 0.224, P < 0.001) were all significantly correlated with the frequency of participating in suboptimal care (H4).

DISCUSSION

In this exploratory analysis, we validated in the hospitalist workforce several assumptions about person‐job fit that have been observed in workers of other industries. We observed attrition‐reselection (ie, job switching) as a strategy used by physicians to achieve better fit early in their job tenure, whereas job modification appeared to be more effective than attrition‐reselection among physicians already established in their jobs. We provided weak but plausible evidence that physicians with optimal job fit had a tendency to participate in activities (eg, teaching) that engage a wider set of interests and abilities. We also demonstrated the growth in hospitalists sharing the values of their organization through the time‐dependent associations among organizational climate, relational measures, and job fit. Finally, we found that physicians with suboptimal job fit were more likely to report poor performance in their work compared to those indicating optimal fit.

Our previous analysis of data from the Hospital Medicine Physician Worklife Survey exposed the widely variable work characteristics of hospitalist jobs in the US market and the equally variable preferences and priorities of individual hospitalists in selecting their work setting.[7] The implication of our present study is that hospitalists achieve the high levels of observed job fit using various strategies that aid their alignment with their employment. One of these strategies involves time, but physician longevity in practice may be both a determinant and product of good job fit. Although early job attrition may be necessary for fitting the right hospitalists to the right jobs, employers may appreciate the importance of retaining experienced hospitalists not only for cost and performance considerations but also for the growth of social capital in organizations consisting of enduring individuals. As our data suggest that hospitalists grow with their jobs, physicians may experience better fit with jobs that flexibly couple their work demands with benefits that address their individual work‐life needs over time. Another implication of this study is that job fit is a useful and predictive measure of job selection, performance, and retention. In light of studies that expose the limitations of job satisfaction as a measure influenced more by workers' dispositional affect (ie, their temperament and outlook) than their compatibility with their jobs,[28] job fit may add a functional dimension to traditional employee feedback measures.

There are limitations to this analysis. The most notable is the low survey response rate. Two reasons contributed to the fairly low rate of return. First, the original sampling frame included many outdated addresses and names of individuals who did not meet inclusion criteria. Although all sampled individuals who would have been excluded from the study could not be identified, we calculated our response rate without accounting for the proportion of potential ineligibles in the denominator population [Response Rate 2 (RR2) according to standards of the American Association of Public Opinion Research].[29] Second, the response rates of physician surveys have seen a steady decline over the years.[30] Respondents to our survey may be older and more experienced than US hospitalists in general. Although concerns about bias from under‐reporting cannot be fully addressed, we believe that the study sample is adequate for this preliminary study intended to translate the evidence of observed phenomena from the nonphysician to the physician workforces. The suboptimal response characteristics (high skew and low variability) of the generic person‐job fit survey scale used in this study indicate that a reliable survey instrument specifically designed to measure physician‐job fit need to be constructed de novo and validated for any future study. Although we performed simple analyses to support our assertions, few of our subanalyses may be underpowered, contributing to overinterpretation of the data. Additional empirical work is also necessary to assess the generalizability of this study's claims in other medical and surgical specialties. Such studies would also allow measurement of the sensitivity and specificity of physicians' self‐identification of poor job fit. Finally, additional investigations of this time‐dependent construct are more appropriately performed using a longitudinal study design to overcome the limitations inherent in this cross‐sectional analysis. Our conclusions about the time‐dependent features of job fit may be explained by other characteristics such as generational and cultural differences among hospitalists with varying experience.

As the US healthcare system reorganizes to bolster accountability,[31] we anticipate increasing interdependence between physicians and their employer organizations. Ultimately, the desired accountability in healthcare is likely to be obtained if physicians function not only as passive and interchangeable employees but as active stakeholders in the achievement of each organization's goals. A methodology for assessing the alignment of physicians and their jobs will continue to be important along the way.

Over the past 15 years, there has been dramatic growth in the number of hospitalist physicians in the United States and in the number of hospitals served by them.13 Hospitals are motivated to hire experienced hospitalists to staff their inpatient services,4 with goals that include obtaining cost‐savings and higher quality.59 The rapid growth of Hospital Medicine saw multiple types of hospital practice models emerge with differing job characteristics, clinical duties, workload, and compensation schemes.10 The extent of the variability of hospitalist jobs across practice models is not known.

Intensifying recruitment efforts and the concomitant increase in compensation for hospitalists over the last decade suggest that demand for hospitalists is strong and sustained.11 As a result, today's cohort of hospitalists has a wide range of choices of types of jobs, practice models, and locations. The diversity of available hospitalist jobs is characterized, for example, by setting (community hospital vs academic hospital), employer (hospital vs private practice), job duties (the amount and type of clinical work, and other administrative, teaching, or research duties), and intensity (work hours and duties to maximize income or lifestyle). How these choices relate to job satisfaction and burnout are also unknown.

The Society of Hospital Medicine (SHM) has administered surveys to hospitalist group leaders biennially since 2003.1215 These surveys, however, do not address issues related to individual hospitalist worklife, recruitment, and retention. In 2005, SHM convened a Career Satisfaction Task Force that designed and executed a national survey of hospitalists in 2009‐2010. The objective of this study is to evaluate how job characteristics vary by practice model, and the association of these characteristics and practice models with job satisfaction and burnout.

METHODS

RESULTS

Response Rate

Of the 5389 originally sampled addresses, 1868 were undeliverable. Addresses were further excluded if they appeared in duplicate or were outdated. This yielded a total of 3105 eligible surveyees in the sample. As illustrated in Figure 1, 841 responded to the mailed survey and 5 responded to the Web‐based survey. After rejecting 67 non‐hospitalist respondents and 3 duplicate surveys, a total of 776 surveys were included in the final analysis. The adjusted response rate was 25.6% (776/3035). Members of SHM were more likely to return the survey than nonmembers. The adjusted response rate from hospitalists affiliated with the 3 sponsoring institutions was 6% (40/662). Because these respondents were more likely to be non‐members of SHM, we opted to analyze the responses from the sponsor hospitalists together with the sampled hospitalists. The demographics of the resulting pool of 816 respondents affiliated with over 650 unique hospitalist groups were representative of the original survey frame. We analyzed data from 794 of these who responded to the item indicating their hospitalist practice model. Demographic characteristics of responders and nonresponders to the practice model survey item were similar.

Figure 1

Components of Job Satisfaction

Hospitalists' rankings of the most important factors for job satisfaction revealed differences across models (Figure 2). Overall, hospitalists were most likely to consider optimal workload and compensation as important factors for job satisfaction from a list of 13 considerations. Local groups and academics were least likely to rank optimal workload as a top factor, and local group hospitalists were more likely to rank optimal autonomy than those of other models. Academic hospitalists had less concern for substantial pay, and more concern for the variety of tasks they perform and recognition by leaders, than other hospitalists.

Figure 2

Job Satisfaction and Burnout Risk

Differences in the ratings of 4 of the 11 satisfaction and job characteristic domains were found across the practice models (Figure 3). Multispecialty group hospitalists were less satisfied with autonomy and their relationship with patients than other practice models, and along with multistate groups, reported the highest perceived workload. Organizational fairness was rated much higher by local group hospitalists than other practice models. Despite these differences in work patterns and satisfaction, there were no differences found in level of global job satisfaction, specialty satisfaction, or burnout across the practice models. Overall, 62% of respondents reported high job satisfaction (4 on a 1 to 5 scale), and 30% indicated burnout symptoms.

Figure 3

DISCUSSION

In our sample of US hospitalists, we found major differences in work patterns and compensation across hospitalist practice models, but no differences in job satisfaction, specialty satisfaction, and burnout. In particular, differences across these models included variations in hospitalist workload, hours, pay, and distribution of work activities. We found that hospitalists perform a variety of clinical and nonclinical tasks, for many of which there are not standard reimbursement mechanisms. We also found that features of a job that individual hospitalists considered most important vary by practice model.

Previous analysis of this data explored the overall state of hospitalist satisfaction.16 The present analysis offers a glimpse into hospitalists' systems‐orientation through a deeper look at their work patterns. The growth in the number of hospitalists who participate in intensive care medicine, specialty comanagement, and other work that involves close working relationships with specialist physicians confirms collaborative care as one of the dominant drivers of the hospitalist movement. At the level of indirect patient care, nearly all hospitalists contributed to work that facilitates coordination, quality, patient safety, or information technology. Understanding the integrative value of hospitalists outside of their clinical productivity may be of interest to hospital administrators.

Global satisfaction measures were similar across practice models. This finding is particularly interesting given the major differences in job characteristics seen among the practice models. This similarity in global satisfaction despite real differences in the nature of the job suggests that individuals find settings that allow them to address their individual professional goals. Our study demonstrates that, in 2010, Hospital Medicine has evolved enough to accommodate a wide variety of goals and needs.

While global satisfaction did not differ among practice types, hospitalists from various models did report differences in factors considered important to global satisfaction. While workload and pay were rated as influential across most models, the degree of importance was significantly different. In academic settings, substantial pay was not a top consideration for overall job satisfaction, whereas in local and multistate hospitalist groups, pay was a very close second in importance to optimal workload. These results may prove helpful for individual hospitalists trying to find their optimal job. For example, someone who is less concerned about workload, but wants to be paid well and have a high degree of autonomy, may find satisfaction in local hospitalist groups. However, for someone who is willing to sacrifice a higher salary for variety of activities, academic Hospital Medicine may be a better fit.

There is a concerning aspect of hospitalist job satisfaction that different practice models do not seem to solve. Control over personal time is a top consideration for many hospitalists across practice models, yet their satisfaction with personal time is low. As control over personal time is seen as a draw to the Hospital Medicine specialty, group leaders may need to evaluate their programs to ensure that schedules and workload support efforts for hospitalists to balance work and homelife commitments.

There are additional findings that are important for Hospital Medicine group leaders. Regardless of practice model, compensation and workload are often used as tools to recruit and retain hospitalists. While these tools may be effective, leaders may find more nuanced approaches to improving their hospitalists' overall satisfaction. Leaders of local hospitalist groups may find their hospitalists tolerant of heavier workloads as long as they are adequately rewarded and are given real autonomy over their work. However, leaders of academic programs may be missing the primary factor that can improve their hospitalists' satisfaction. Rather than asking for higher salaries to remain competitive, it may be more effective to advocate for time and training for their hospitalists to pursue important other activities beyond direct clinical care. Given that resources will always be limited, group leaders need to understand all of the elements that can contribute to hospitalist job satisfaction.

We point out several limitations to this study. First, our adjusted response rate of 25.6% is low for survey research, in general. As mentioned above, hospitalists are not easily identified in any available national physician database. Therefore, we deliberately designed our sampling strategy to error on the side of including ineligible surveyees to reduce systematic exclusion of practicing hospitalists. Using simple post hoc methods, we identified many nonhospitalists and bad addresses from our sample, but because these methods were exclusionary as opposed to confirmatory, we believe that a significant proportion of remaining nonrespondents may also have been ineligible for the survey. Although this does not fully address concerns about potential response bias, we believe that our sample representing a large number of hospitalist groups is adequate to make estimations about a nationally representative sample of practicing hospitalists. Second, in spite of our inclusive approach, we may still have excluded categories of practicing hospitalists. We were careful not to allow SHM members to represent all US hospitalists and included non‐members in the sampling frame, but the possibility of systematic exclusion that may alter our results remains a concern. Additionally, one of our goals was to characterize pediatric hospitalists independently from their adult‐patient counterparts. Despite oversampling of pediatricians, their sample was too small for a more detailed comparison across practice models. Also, self‐reported data about workload and compensation are subject to inaccuracies related to recall and cognitive biases. Last, this is a cross‐sectional study of hospitalist satisfaction at one point in time. Consequently, our sample may not be representative of very dissatisfied hospitalists who have already left their jobs.

The diversity found across existing practice models and the characteristics of the practices provide physicians with the opportunity to bring their unique skills and motivations to the hospitalist movement. As hospitals and other organizations seek to create, maintain, or grow hospitalist programs, the data provided here may prove useful to understand the relationship between practice characteristics and individual job satisfaction. Additionally, hospitalists looking for a job can consider these results as additional information to guide their choice of practice model and work patterns.

Over the past 15 years, there has been dramatic growth in the number of hospitalist physicians in the United States and in the number of hospitals served by them.13 Hospitals are motivated to hire experienced hospitalists to staff their inpatient services,4 with goals that include obtaining cost‐savings and higher quality.59 The rapid growth of Hospital Medicine saw multiple types of hospital practice models emerge with differing job characteristics, clinical duties, workload, and compensation schemes.10 The extent of the variability of hospitalist jobs across practice models is not known.

Intensifying recruitment efforts and the concomitant increase in compensation for hospitalists over the last decade suggest that demand for hospitalists is strong and sustained.11 As a result, today's cohort of hospitalists has a wide range of choices of types of jobs, practice models, and locations. The diversity of available hospitalist jobs is characterized, for example, by setting (community hospital vs academic hospital), employer (hospital vs private practice), job duties (the amount and type of clinical work, and other administrative, teaching, or research duties), and intensity (work hours and duties to maximize income or lifestyle). How these choices relate to job satisfaction and burnout are also unknown.

The Society of Hospital Medicine (SHM) has administered surveys to hospitalist group leaders biennially since 2003.1215 These surveys, however, do not address issues related to individual hospitalist worklife, recruitment, and retention. In 2005, SHM convened a Career Satisfaction Task Force that designed and executed a national survey of hospitalists in 2009‐2010. The objective of this study is to evaluate how job characteristics vary by practice model, and the association of these characteristics and practice models with job satisfaction and burnout.

METHODS

RESULTS

Response Rate

Of the 5389 originally sampled addresses, 1868 were undeliverable. Addresses were further excluded if they appeared in duplicate or were outdated. This yielded a total of 3105 eligible surveyees in the sample. As illustrated in Figure 1, 841 responded to the mailed survey and 5 responded to the Web‐based survey. After rejecting 67 non‐hospitalist respondents and 3 duplicate surveys, a total of 776 surveys were included in the final analysis. The adjusted response rate was 25.6% (776/3035). Members of SHM were more likely to return the survey than nonmembers. The adjusted response rate from hospitalists affiliated with the 3 sponsoring institutions was 6% (40/662). Because these respondents were more likely to be non‐members of SHM, we opted to analyze the responses from the sponsor hospitalists together with the sampled hospitalists. The demographics of the resulting pool of 816 respondents affiliated with over 650 unique hospitalist groups were representative of the original survey frame. We analyzed data from 794 of these who responded to the item indicating their hospitalist practice model. Demographic characteristics of responders and nonresponders to the practice model survey item were similar.

Figure 1

Components of Job Satisfaction

Hospitalists' rankings of the most important factors for job satisfaction revealed differences across models (Figure 2). Overall, hospitalists were most likely to consider optimal workload and compensation as important factors for job satisfaction from a list of 13 considerations. Local groups and academics were least likely to rank optimal workload as a top factor, and local group hospitalists were more likely to rank optimal autonomy than those of other models. Academic hospitalists had less concern for substantial pay, and more concern for the variety of tasks they perform and recognition by leaders, than other hospitalists.

Figure 2

Job Satisfaction and Burnout Risk

Differences in the ratings of 4 of the 11 satisfaction and job characteristic domains were found across the practice models (Figure 3). Multispecialty group hospitalists were less satisfied with autonomy and their relationship with patients than other practice models, and along with multistate groups, reported the highest perceived workload. Organizational fairness was rated much higher by local group hospitalists than other practice models. Despite these differences in work patterns and satisfaction, there were no differences found in level of global job satisfaction, specialty satisfaction, or burnout across the practice models. Overall, 62% of respondents reported high job satisfaction (4 on a 1 to 5 scale), and 30% indicated burnout symptoms.

Figure 3

DISCUSSION

In our sample of US hospitalists, we found major differences in work patterns and compensation across hospitalist practice models, but no differences in job satisfaction, specialty satisfaction, and burnout. In particular, differences across these models included variations in hospitalist workload, hours, pay, and distribution of work activities. We found that hospitalists perform a variety of clinical and nonclinical tasks, for many of which there are not standard reimbursement mechanisms. We also found that features of a job that individual hospitalists considered most important vary by practice model.

Previous analysis of this data explored the overall state of hospitalist satisfaction.16 The present analysis offers a glimpse into hospitalists' systems‐orientation through a deeper look at their work patterns. The growth in the number of hospitalists who participate in intensive care medicine, specialty comanagement, and other work that involves close working relationships with specialist physicians confirms collaborative care as one of the dominant drivers of the hospitalist movement. At the level of indirect patient care, nearly all hospitalists contributed to work that facilitates coordination, quality, patient safety, or information technology. Understanding the integrative value of hospitalists outside of their clinical productivity may be of interest to hospital administrators.

Global satisfaction measures were similar across practice models. This finding is particularly interesting given the major differences in job characteristics seen among the practice models. This similarity in global satisfaction despite real differences in the nature of the job suggests that individuals find settings that allow them to address their individual professional goals. Our study demonstrates that, in 2010, Hospital Medicine has evolved enough to accommodate a wide variety of goals and needs.

While global satisfaction did not differ among practice types, hospitalists from various models did report differences in factors considered important to global satisfaction. While workload and pay were rated as influential across most models, the degree of importance was significantly different. In academic settings, substantial pay was not a top consideration for overall job satisfaction, whereas in local and multistate hospitalist groups, pay was a very close second in importance to optimal workload. These results may prove helpful for individual hospitalists trying to find their optimal job. For example, someone who is less concerned about workload, but wants to be paid well and have a high degree of autonomy, may find satisfaction in local hospitalist groups. However, for someone who is willing to sacrifice a higher salary for variety of activities, academic Hospital Medicine may be a better fit.

There is a concerning aspect of hospitalist job satisfaction that different practice models do not seem to solve. Control over personal time is a top consideration for many hospitalists across practice models, yet their satisfaction with personal time is low. As control over personal time is seen as a draw to the Hospital Medicine specialty, group leaders may need to evaluate their programs to ensure that schedules and workload support efforts for hospitalists to balance work and homelife commitments.

There are additional findings that are important for Hospital Medicine group leaders. Regardless of practice model, compensation and workload are often used as tools to recruit and retain hospitalists. While these tools may be effective, leaders may find more nuanced approaches to improving their hospitalists' overall satisfaction. Leaders of local hospitalist groups may find their hospitalists tolerant of heavier workloads as long as they are adequately rewarded and are given real autonomy over their work. However, leaders of academic programs may be missing the primary factor that can improve their hospitalists' satisfaction. Rather than asking for higher salaries to remain competitive, it may be more effective to advocate for time and training for their hospitalists to pursue important other activities beyond direct clinical care. Given that resources will always be limited, group leaders need to understand all of the elements that can contribute to hospitalist job satisfaction.

We point out several limitations to this study. First, our adjusted response rate of 25.6% is low for survey research, in general. As mentioned above, hospitalists are not easily identified in any available national physician database. Therefore, we deliberately designed our sampling strategy to error on the side of including ineligible surveyees to reduce systematic exclusion of practicing hospitalists. Using simple post hoc methods, we identified many nonhospitalists and bad addresses from our sample, but because these methods were exclusionary as opposed to confirmatory, we believe that a significant proportion of remaining nonrespondents may also have been ineligible for the survey. Although this does not fully address concerns about potential response bias, we believe that our sample representing a large number of hospitalist groups is adequate to make estimations about a nationally representative sample of practicing hospitalists. Second, in spite of our inclusive approach, we may still have excluded categories of practicing hospitalists. We were careful not to allow SHM members to represent all US hospitalists and included non‐members in the sampling frame, but the possibility of systematic exclusion that may alter our results remains a concern. Additionally, one of our goals was to characterize pediatric hospitalists independently from their adult‐patient counterparts. Despite oversampling of pediatricians, their sample was too small for a more detailed comparison across practice models. Also, self‐reported data about workload and compensation are subject to inaccuracies related to recall and cognitive biases. Last, this is a cross‐sectional study of hospitalist satisfaction at one point in time. Consequently, our sample may not be representative of very dissatisfied hospitalists who have already left their jobs.

The diversity found across existing practice models and the characteristics of the practices provide physicians with the opportunity to bring their unique skills and motivations to the hospitalist movement. As hospitals and other organizations seek to create, maintain, or grow hospitalist programs, the data provided here may prove useful to understand the relationship between practice characteristics and individual job satisfaction. Additionally, hospitalists looking for a job can consider these results as additional information to guide their choice of practice model and work patterns.

Over the past 15 years, there has been dramatic growth in the number of hospitalist physicians in the United States and in the number of hospitals served by them.13 Hospitals are motivated to hire experienced hospitalists to staff their inpatient services,4 with goals that include obtaining cost‐savings and higher quality.59 The rapid growth of Hospital Medicine saw multiple types of hospital practice models emerge with differing job characteristics, clinical duties, workload, and compensation schemes.10 The extent of the variability of hospitalist jobs across practice models is not known.

Intensifying recruitment efforts and the concomitant increase in compensation for hospitalists over the last decade suggest that demand for hospitalists is strong and sustained.11 As a result, today's cohort of hospitalists has a wide range of choices of types of jobs, practice models, and locations. The diversity of available hospitalist jobs is characterized, for example, by setting (community hospital vs academic hospital), employer (hospital vs private practice), job duties (the amount and type of clinical work, and other administrative, teaching, or research duties), and intensity (work hours and duties to maximize income or lifestyle). How these choices relate to job satisfaction and burnout are also unknown.

The Society of Hospital Medicine (SHM) has administered surveys to hospitalist group leaders biennially since 2003.1215 These surveys, however, do not address issues related to individual hospitalist worklife, recruitment, and retention. In 2005, SHM convened a Career Satisfaction Task Force that designed and executed a national survey of hospitalists in 2009‐2010. The objective of this study is to evaluate how job characteristics vary by practice model, and the association of these characteristics and practice models with job satisfaction and burnout.

METHODS

RESULTS

Response Rate

Of the 5389 originally sampled addresses, 1868 were undeliverable. Addresses were further excluded if they appeared in duplicate or were outdated. This yielded a total of 3105 eligible surveyees in the sample. As illustrated in Figure 1, 841 responded to the mailed survey and 5 responded to the Web‐based survey. After rejecting 67 non‐hospitalist respondents and 3 duplicate surveys, a total of 776 surveys were included in the final analysis. The adjusted response rate was 25.6% (776/3035). Members of SHM were more likely to return the survey than nonmembers. The adjusted response rate from hospitalists affiliated with the 3 sponsoring institutions was 6% (40/662). Because these respondents were more likely to be non‐members of SHM, we opted to analyze the responses from the sponsor hospitalists together with the sampled hospitalists. The demographics of the resulting pool of 816 respondents affiliated with over 650 unique hospitalist groups were representative of the original survey frame. We analyzed data from 794 of these who responded to the item indicating their hospitalist practice model. Demographic characteristics of responders and nonresponders to the practice model survey item were similar.

Figure 1