Brief Reports

METHODS

RESULTS

DISCUSSION

METHODS

RESULTS

DISCUSSION

METHODS

RESULTS

DISCUSSION

Resident physicians routinely order inpatient laboratory tests,[1] and there is evidence to suggest that many of these tests are unnecessary[2] and potentially harmful.[3] The Society of Hospital Medicine has identified reducing the unnecessary ordering of inpatient laboratory testing as part of the Choosing Wisely campaign.[4] Hospitalists at academic medical centers face growing pressures to develop processes to reduce low‐value care and train residents to be stewards of healthcare resources.[5] Studies[6, 7, 8, 9] have described that institutional and training factors drive residents' resource utilization patterns, but, to our knowledge, none have described what factors contribute to residents' unnecessary laboratory testing. To better understand the factors associated with residents' ordering patterns, we conducted a qualitative analysis of internal medicine (IM) and general surgery (GS) residents at a large academic medical center in order to describe residents' perception of the: (1) frequency of ordering unnecessary inpatient laboratory tests, (2) factors contributing to that behavior, and (3) potential interventions to change it. We also explored differences in responses by specialty and training level.

METHODS

In October 2014, we surveyed all IM and GS residents at the Hospital of the University of Pennsylvania. We reviewed the literature and conducted focus groups with residents to formulate items for the survey instrument. A draft of the survey was administered to 8 residents from both specialties, and their feedback was collated and incorporated into the final version of the instrument. The final 15‐question survey was comprised of 4 components: (1) training information such as specialty and postgraduate year (PGY), (2) self‐reported frequency of perceived unnecessary ordering of inpatient laboratory tests, (3) perception of factors contributing to unnecessary ordering, and (4) potential interventions to reduce unnecessary ordering. An unnecessary test was defined as a test that would not change management regardless of its result. To increase response rates, participants were entered into drawings for $5 gift cards, a $200 air travel voucher, and an iPad mini.

Descriptive statistics and ²tests were conducted with Stata version 13 (StataCorp LP, College Station, TX) to explore differences in the frequency of responses by specialty and training level. To identify themes that emerged from free‐text responses, two independent reviewers (M.S.S. and E.J.K.) performed qualitative content analysis using grounded theory.[10] Reviewers read 10% of responses to create a coding guide. Another 10% of the responses were randomly selected to assess inter‐rater reliability by calculating scores. The reviewers independently coded the remaining 80% of responses. Discrepancies were adjudicated by consensus between the reviewers. The University of Pennsylvania Institutional Review Board deemed this study exempt from review.

RESULTS

The sample comprised 57.0% (85/149) of IM and 54.4% (31/57) of GS residents (Table 1). Among respondents, perceived unnecessary inpatient laboratory test ordering was self‐reported by 88.2% of IM and 67.7% of GS residents. This behavior was reported to occur on a daily basis by 43.5% and 32.3% of responding IM and GS residents, respectively. Across both specialties, the most commonly reported factors contributing to these behaviors were learned practice habit/routine (90.5%), a lack of understanding of the costs associated with lab tests (86.2%), diagnostic uncertainty (82.8%), and fear of not having the lab result information when requested by an attending (75.9%). There were no significant differences in any of these contributing factors by specialty or PGY level. Among respondents who completed a free‐text response (IM: 76 of 85; GS: 21 of 31), the most commonly proposed interventions to address these issues were increasing cost transparency (IM 40.8%; GS 33.3%), improvements to faculty role modeling (IM 30.2%; GS 33.3%), and computerized reminders or decision support (IM 21.1%; GS 28.6%) (Table 2).

DISCUSSION

A significant portion of inpatient laboratory testing is unnecessary,[2] creating an opportunity to reduce utilization and associated costs. Our findings demonstrate that these behaviors occur at high levels among residents (IM 88.2%; GS 67.7%) at a large academic medical center. These findings also reveal that residents attribute this behavior to practice habit, lack of access to cost data, and perceived expectations for daily lab ordering by faculty. Interventions to change these behaviors will need to involve changes to the health system culture, increasing transparency of the costs associated with healthcare services, and shifting to a model of education that celebrates restraint.[11]

Our study adds to the emerging quest for delivering value in healthcare and provides several important insights for hospitalists and medical educators at academic centers. First, our findings reflect the significant role that the clinical learning environment plays in influencing practice behaviors among residents. Residency training is a critical time when physicians begin to form habits that imprint upon their future practice patterns,[5] and our residents are aware that their behavior to order what they perceive to be unnecessary laboratory tests is driven by habit. Studies[6, 7] have shown that residents may implicitly accept certain styles of practice as correct and are more likely to adopt those styles during the early years of their training. In our institution, for example, the process of ordering standing or daily morning labs using a repeated copy‐forward function in the electronic health record is a common, learned practice (a ritual) that is passed down from senior to junior residents year after year. This practice is common across both training specialties. There is a need to better understand, measure, and change the culture in the clinical learning environment to demonstrate practices and values that model high‐value care for residents. Multipronged interventions that address culture, oversight, and systems change[12] are necessary to facilitate effective physician stewardship of inpatient laboratory testing and attack a problem so deeply ingrained in habit.

Second, residents in our study believe that access to cost information will better equip them to reduce unnecessary lab ordering. Two recent systematic reviews[13, 14] have shown that having real‐time access to charges changes physician ordering and prescribing behavior. Increasing cost transparency may not only be an important intervention for hospitals to reduce overuse and control cost, but also better arm resident physicians with the information they need to make higher‐value recommendations for their patients and be stewards of healthcare resources.

Third, our study highlights that residents' unnecessary laboratory utilization is driven by perceived, unspoken expectations for such ordering by faculty. This reflects an important undercurrent in the medical education system that has historically emphasized and rewarded thoroughness while often penalizing restraint.[11] Hospitalists can play a major role in changing these behaviors by sharing their expectations regarding test ordering at the beginning of teaching rotations, including teaching points that discourage overutilization during rounds, and role modeling high‐value care in their own practice. Taken together and practiced routinely, these hospitalist behaviors could prevent poor habits from forming in our trainees and discourage overinvestigation. Hospitalists must be responsible to disseminate the practice of restraint to achieve more cost‐effective care. Purposeful faculty development efforts in the area of high‐value care are needed. Additionally, supporting physician leaders that serve as the institutional bridge between graduate medical education and the health system[15] could foster an environment conducive to coaching residents and faculty to reduce unnecessary practice variation.

This study is subject to several limitations. First, the survey was conducted at a single academic medical center, with a modest response rate, and thus our findings may not be generalizable to other settings or residents of different training programs. Second, we did not validate residents' perception of whether or not tests were, in fact, unnecessary. We also did not validate residents' self‐reporting of their own behavior, which may vary from actual behavior. Lack of validation at the level of the tests and at the level of the residents' behavior are two distinct but inter‐related limitations. Although self‐reported responses among residents are an important indicator of their practice, validating these data with objective measures, such as a measure of necessity of ordered lab tests as determined by an expert physician or group of experienced physicians or the number of inpatient labs ordered by residents, may add further insights. Ordering of perceived unnecessary tests may be even more common if there was under‐reporting of this behavior. Third, although we provided a definition within the survey, interpretation among survey respondents of the term unnecessary may vary, and this variation may contribute to our findings. However, we did provide a clear definition in the survey and we attempted to mitigate this with feedback from residents on our preliminary pilot.

In conclusion, this is one of the first qualitative evaluations to explore residents' perceptions on why they order unnecessary inpatient laboratory tests. Our findings offer a rich understanding of residents' beliefs about their own role in unnecessary lab ordering and explore possible solutions through the lens of the resident. Yet, it is unclear whether tests deemed unnecessary by residents would also be considered unnecessary by attending physicians or even patients. Future efforts are needed to better define which inpatient tests are unnecessary from multiple perspectives including clinicians and patients.

Disclosures: This work was presented in part at the AAMC Integrating Quality Meeting, June 11, 2015, Chicago, Illinois; and the Alliance for Academic Internal Medicine Fall Meeting, October 9, 2015, Atlanta, Georgia. The authors report no conflicts of interest.

Resident physicians routinely order inpatient laboratory tests,[1] and there is evidence to suggest that many of these tests are unnecessary[2] and potentially harmful.[3] The Society of Hospital Medicine has identified reducing the unnecessary ordering of inpatient laboratory testing as part of the Choosing Wisely campaign.[4] Hospitalists at academic medical centers face growing pressures to develop processes to reduce low‐value care and train residents to be stewards of healthcare resources.[5] Studies[6, 7, 8, 9] have described that institutional and training factors drive residents' resource utilization patterns, but, to our knowledge, none have described what factors contribute to residents' unnecessary laboratory testing. To better understand the factors associated with residents' ordering patterns, we conducted a qualitative analysis of internal medicine (IM) and general surgery (GS) residents at a large academic medical center in order to describe residents' perception of the: (1) frequency of ordering unnecessary inpatient laboratory tests, (2) factors contributing to that behavior, and (3) potential interventions to change it. We also explored differences in responses by specialty and training level.

METHODS

In October 2014, we surveyed all IM and GS residents at the Hospital of the University of Pennsylvania. We reviewed the literature and conducted focus groups with residents to formulate items for the survey instrument. A draft of the survey was administered to 8 residents from both specialties, and their feedback was collated and incorporated into the final version of the instrument. The final 15‐question survey was comprised of 4 components: (1) training information such as specialty and postgraduate year (PGY), (2) self‐reported frequency of perceived unnecessary ordering of inpatient laboratory tests, (3) perception of factors contributing to unnecessary ordering, and (4) potential interventions to reduce unnecessary ordering. An unnecessary test was defined as a test that would not change management regardless of its result. To increase response rates, participants were entered into drawings for $5 gift cards, a $200 air travel voucher, and an iPad mini.

Descriptive statistics and ²tests were conducted with Stata version 13 (StataCorp LP, College Station, TX) to explore differences in the frequency of responses by specialty and training level. To identify themes that emerged from free‐text responses, two independent reviewers (M.S.S. and E.J.K.) performed qualitative content analysis using grounded theory.[10] Reviewers read 10% of responses to create a coding guide. Another 10% of the responses were randomly selected to assess inter‐rater reliability by calculating scores. The reviewers independently coded the remaining 80% of responses. Discrepancies were adjudicated by consensus between the reviewers. The University of Pennsylvania Institutional Review Board deemed this study exempt from review.

RESULTS

The sample comprised 57.0% (85/149) of IM and 54.4% (31/57) of GS residents (Table 1). Among respondents, perceived unnecessary inpatient laboratory test ordering was self‐reported by 88.2% of IM and 67.7% of GS residents. This behavior was reported to occur on a daily basis by 43.5% and 32.3% of responding IM and GS residents, respectively. Across both specialties, the most commonly reported factors contributing to these behaviors were learned practice habit/routine (90.5%), a lack of understanding of the costs associated with lab tests (86.2%), diagnostic uncertainty (82.8%), and fear of not having the lab result information when requested by an attending (75.9%). There were no significant differences in any of these contributing factors by specialty or PGY level. Among respondents who completed a free‐text response (IM: 76 of 85; GS: 21 of 31), the most commonly proposed interventions to address these issues were increasing cost transparency (IM 40.8%; GS 33.3%), improvements to faculty role modeling (IM 30.2%; GS 33.3%), and computerized reminders or decision support (IM 21.1%; GS 28.6%) (Table 2).

DISCUSSION

A significant portion of inpatient laboratory testing is unnecessary,[2] creating an opportunity to reduce utilization and associated costs. Our findings demonstrate that these behaviors occur at high levels among residents (IM 88.2%; GS 67.7%) at a large academic medical center. These findings also reveal that residents attribute this behavior to practice habit, lack of access to cost data, and perceived expectations for daily lab ordering by faculty. Interventions to change these behaviors will need to involve changes to the health system culture, increasing transparency of the costs associated with healthcare services, and shifting to a model of education that celebrates restraint.[11]

Our study adds to the emerging quest for delivering value in healthcare and provides several important insights for hospitalists and medical educators at academic centers. First, our findings reflect the significant role that the clinical learning environment plays in influencing practice behaviors among residents. Residency training is a critical time when physicians begin to form habits that imprint upon their future practice patterns,[5] and our residents are aware that their behavior to order what they perceive to be unnecessary laboratory tests is driven by habit. Studies[6, 7] have shown that residents may implicitly accept certain styles of practice as correct and are more likely to adopt those styles during the early years of their training. In our institution, for example, the process of ordering standing or daily morning labs using a repeated copy‐forward function in the electronic health record is a common, learned practice (a ritual) that is passed down from senior to junior residents year after year. This practice is common across both training specialties. There is a need to better understand, measure, and change the culture in the clinical learning environment to demonstrate practices and values that model high‐value care for residents. Multipronged interventions that address culture, oversight, and systems change[12] are necessary to facilitate effective physician stewardship of inpatient laboratory testing and attack a problem so deeply ingrained in habit.

Second, residents in our study believe that access to cost information will better equip them to reduce unnecessary lab ordering. Two recent systematic reviews[13, 14] have shown that having real‐time access to charges changes physician ordering and prescribing behavior. Increasing cost transparency may not only be an important intervention for hospitals to reduce overuse and control cost, but also better arm resident physicians with the information they need to make higher‐value recommendations for their patients and be stewards of healthcare resources.

Third, our study highlights that residents' unnecessary laboratory utilization is driven by perceived, unspoken expectations for such ordering by faculty. This reflects an important undercurrent in the medical education system that has historically emphasized and rewarded thoroughness while often penalizing restraint.[11] Hospitalists can play a major role in changing these behaviors by sharing their expectations regarding test ordering at the beginning of teaching rotations, including teaching points that discourage overutilization during rounds, and role modeling high‐value care in their own practice. Taken together and practiced routinely, these hospitalist behaviors could prevent poor habits from forming in our trainees and discourage overinvestigation. Hospitalists must be responsible to disseminate the practice of restraint to achieve more cost‐effective care. Purposeful faculty development efforts in the area of high‐value care are needed. Additionally, supporting physician leaders that serve as the institutional bridge between graduate medical education and the health system[15] could foster an environment conducive to coaching residents and faculty to reduce unnecessary practice variation.

This study is subject to several limitations. First, the survey was conducted at a single academic medical center, with a modest response rate, and thus our findings may not be generalizable to other settings or residents of different training programs. Second, we did not validate residents' perception of whether or not tests were, in fact, unnecessary. We also did not validate residents' self‐reporting of their own behavior, which may vary from actual behavior. Lack of validation at the level of the tests and at the level of the residents' behavior are two distinct but inter‐related limitations. Although self‐reported responses among residents are an important indicator of their practice, validating these data with objective measures, such as a measure of necessity of ordered lab tests as determined by an expert physician or group of experienced physicians or the number of inpatient labs ordered by residents, may add further insights. Ordering of perceived unnecessary tests may be even more common if there was under‐reporting of this behavior. Third, although we provided a definition within the survey, interpretation among survey respondents of the term unnecessary may vary, and this variation may contribute to our findings. However, we did provide a clear definition in the survey and we attempted to mitigate this with feedback from residents on our preliminary pilot.

In conclusion, this is one of the first qualitative evaluations to explore residents' perceptions on why they order unnecessary inpatient laboratory tests. Our findings offer a rich understanding of residents' beliefs about their own role in unnecessary lab ordering and explore possible solutions through the lens of the resident. Yet, it is unclear whether tests deemed unnecessary by residents would also be considered unnecessary by attending physicians or even patients. Future efforts are needed to better define which inpatient tests are unnecessary from multiple perspectives including clinicians and patients.

Disclosures: This work was presented in part at the AAMC Integrating Quality Meeting, June 11, 2015, Chicago, Illinois; and the Alliance for Academic Internal Medicine Fall Meeting, October 9, 2015, Atlanta, Georgia. The authors report no conflicts of interest.

Resident physicians routinely order inpatient laboratory tests,[1] and there is evidence to suggest that many of these tests are unnecessary[2] and potentially harmful.[3] The Society of Hospital Medicine has identified reducing the unnecessary ordering of inpatient laboratory testing as part of the Choosing Wisely campaign.[4] Hospitalists at academic medical centers face growing pressures to develop processes to reduce low‐value care and train residents to be stewards of healthcare resources.[5] Studies[6, 7, 8, 9] have described that institutional and training factors drive residents' resource utilization patterns, but, to our knowledge, none have described what factors contribute to residents' unnecessary laboratory testing. To better understand the factors associated with residents' ordering patterns, we conducted a qualitative analysis of internal medicine (IM) and general surgery (GS) residents at a large academic medical center in order to describe residents' perception of the: (1) frequency of ordering unnecessary inpatient laboratory tests, (2) factors contributing to that behavior, and (3) potential interventions to change it. We also explored differences in responses by specialty and training level.

METHODS

In October 2014, we surveyed all IM and GS residents at the Hospital of the University of Pennsylvania. We reviewed the literature and conducted focus groups with residents to formulate items for the survey instrument. A draft of the survey was administered to 8 residents from both specialties, and their feedback was collated and incorporated into the final version of the instrument. The final 15‐question survey was comprised of 4 components: (1) training information such as specialty and postgraduate year (PGY), (2) self‐reported frequency of perceived unnecessary ordering of inpatient laboratory tests, (3) perception of factors contributing to unnecessary ordering, and (4) potential interventions to reduce unnecessary ordering. An unnecessary test was defined as a test that would not change management regardless of its result. To increase response rates, participants were entered into drawings for $5 gift cards, a $200 air travel voucher, and an iPad mini.

Descriptive statistics and ²tests were conducted with Stata version 13 (StataCorp LP, College Station, TX) to explore differences in the frequency of responses by specialty and training level. To identify themes that emerged from free‐text responses, two independent reviewers (M.S.S. and E.J.K.) performed qualitative content analysis using grounded theory.[10] Reviewers read 10% of responses to create a coding guide. Another 10% of the responses were randomly selected to assess inter‐rater reliability by calculating scores. The reviewers independently coded the remaining 80% of responses. Discrepancies were adjudicated by consensus between the reviewers. The University of Pennsylvania Institutional Review Board deemed this study exempt from review.

RESULTS

The sample comprised 57.0% (85/149) of IM and 54.4% (31/57) of GS residents (Table 1). Among respondents, perceived unnecessary inpatient laboratory test ordering was self‐reported by 88.2% of IM and 67.7% of GS residents. This behavior was reported to occur on a daily basis by 43.5% and 32.3% of responding IM and GS residents, respectively. Across both specialties, the most commonly reported factors contributing to these behaviors were learned practice habit/routine (90.5%), a lack of understanding of the costs associated with lab tests (86.2%), diagnostic uncertainty (82.8%), and fear of not having the lab result information when requested by an attending (75.9%). There were no significant differences in any of these contributing factors by specialty or PGY level. Among respondents who completed a free‐text response (IM: 76 of 85; GS: 21 of 31), the most commonly proposed interventions to address these issues were increasing cost transparency (IM 40.8%; GS 33.3%), improvements to faculty role modeling (IM 30.2%; GS 33.3%), and computerized reminders or decision support (IM 21.1%; GS 28.6%) (Table 2).

DISCUSSION

A significant portion of inpatient laboratory testing is unnecessary,[2] creating an opportunity to reduce utilization and associated costs. Our findings demonstrate that these behaviors occur at high levels among residents (IM 88.2%; GS 67.7%) at a large academic medical center. These findings also reveal that residents attribute this behavior to practice habit, lack of access to cost data, and perceived expectations for daily lab ordering by faculty. Interventions to change these behaviors will need to involve changes to the health system culture, increasing transparency of the costs associated with healthcare services, and shifting to a model of education that celebrates restraint.[11]

Our study adds to the emerging quest for delivering value in healthcare and provides several important insights for hospitalists and medical educators at academic centers. First, our findings reflect the significant role that the clinical learning environment plays in influencing practice behaviors among residents. Residency training is a critical time when physicians begin to form habits that imprint upon their future practice patterns,[5] and our residents are aware that their behavior to order what they perceive to be unnecessary laboratory tests is driven by habit. Studies[6, 7] have shown that residents may implicitly accept certain styles of practice as correct and are more likely to adopt those styles during the early years of their training. In our institution, for example, the process of ordering standing or daily morning labs using a repeated copy‐forward function in the electronic health record is a common, learned practice (a ritual) that is passed down from senior to junior residents year after year. This practice is common across both training specialties. There is a need to better understand, measure, and change the culture in the clinical learning environment to demonstrate practices and values that model high‐value care for residents. Multipronged interventions that address culture, oversight, and systems change[12] are necessary to facilitate effective physician stewardship of inpatient laboratory testing and attack a problem so deeply ingrained in habit.

Second, residents in our study believe that access to cost information will better equip them to reduce unnecessary lab ordering. Two recent systematic reviews[13, 14] have shown that having real‐time access to charges changes physician ordering and prescribing behavior. Increasing cost transparency may not only be an important intervention for hospitals to reduce overuse and control cost, but also better arm resident physicians with the information they need to make higher‐value recommendations for their patients and be stewards of healthcare resources.

Third, our study highlights that residents' unnecessary laboratory utilization is driven by perceived, unspoken expectations for such ordering by faculty. This reflects an important undercurrent in the medical education system that has historically emphasized and rewarded thoroughness while often penalizing restraint.[11] Hospitalists can play a major role in changing these behaviors by sharing their expectations regarding test ordering at the beginning of teaching rotations, including teaching points that discourage overutilization during rounds, and role modeling high‐value care in their own practice. Taken together and practiced routinely, these hospitalist behaviors could prevent poor habits from forming in our trainees and discourage overinvestigation. Hospitalists must be responsible to disseminate the practice of restraint to achieve more cost‐effective care. Purposeful faculty development efforts in the area of high‐value care are needed. Additionally, supporting physician leaders that serve as the institutional bridge between graduate medical education and the health system[15] could foster an environment conducive to coaching residents and faculty to reduce unnecessary practice variation.

This study is subject to several limitations. First, the survey was conducted at a single academic medical center, with a modest response rate, and thus our findings may not be generalizable to other settings or residents of different training programs. Second, we did not validate residents' perception of whether or not tests were, in fact, unnecessary. We also did not validate residents' self‐reporting of their own behavior, which may vary from actual behavior. Lack of validation at the level of the tests and at the level of the residents' behavior are two distinct but inter‐related limitations. Although self‐reported responses among residents are an important indicator of their practice, validating these data with objective measures, such as a measure of necessity of ordered lab tests as determined by an expert physician or group of experienced physicians or the number of inpatient labs ordered by residents, may add further insights. Ordering of perceived unnecessary tests may be even more common if there was under‐reporting of this behavior. Third, although we provided a definition within the survey, interpretation among survey respondents of the term unnecessary may vary, and this variation may contribute to our findings. However, we did provide a clear definition in the survey and we attempted to mitigate this with feedback from residents on our preliminary pilot.

In conclusion, this is one of the first qualitative evaluations to explore residents' perceptions on why they order unnecessary inpatient laboratory tests. Our findings offer a rich understanding of residents' beliefs about their own role in unnecessary lab ordering and explore possible solutions through the lens of the resident. Yet, it is unclear whether tests deemed unnecessary by residents would also be considered unnecessary by attending physicians or even patients. Future efforts are needed to better define which inpatient tests are unnecessary from multiple perspectives including clinicians and patients.

Disclosures: This work was presented in part at the AAMC Integrating Quality Meeting, June 11, 2015, Chicago, Illinois; and the Alliance for Academic Internal Medicine Fall Meeting, October 9, 2015, Atlanta, Georgia. The authors report no conflicts of interest.

Sitting while interacting with patients is standard in the outpatient setting and encouraged in the inpatient setting as a best practice.[1, 2] Michael W. Kahn defined etiquette‐based medicine as a set of easily taught behaviors that demonstrate respect for the patient; sitting at the bedside is included.[1] A prominent healthcare consulting group also recommends that physicians and nurses sit at the bedside, claiming that the patient will estimate you were in the room 3 times longer.[3] Previous studies suggest patients may perceive physicians who sit at the bedside as more compassionate and as spending more time with them, and may perceive the overall interaction as more positive when the physician sits.[4, 5, 6] Two small studies found that patients perceived the physician as having spent more time with them if he or she sat rather than stood.[5, 6] A study in the emergency department found no effect of posture on patient perception of physician communication skills, and a study of a single attending neurosurgeon found that patients reported a better understanding of their condition when the physician sat.[5, 6] The effect of physician posture on hospitalist physician‐patient communication has not been previously studied. Despite evidence that sitting in the inpatient setting may improve physician‐patient communication, studies suggest that physicians rarely sit at the bedside of inpatients.[7, 8]

We conducted a cluster‐randomized trial of the impact of hospitalist physician posture during morning rounds. We hypothesized that patients whose physician sat rather than stood would perceive that their physician spent more time with them and would rate the physician's communication skills more highly. We also hypothesized that sitting would not prolong the length of the patient‐physician encounter.

PATIENTS AND METHODS

We conducted a cluster‐randomized clinical trial with a crossover component randomizing physicians on the order of sit/stand within a consecutive 7‐day workweek. We enrolled patients being cared for by attending hospitalists on a resident‐uncovered general internal medicine service in an academic tertiary care hospital. We also enrolled the hospitalists and collected demographics and practice information. Wall‐mounted folding chairs (Figure 1) were installed in all rooms on two 28‐bed units for use by physicians. Eligible patients were newly admitted or transferred from the intensive care unit between June 2014 and June 2015, English speaking, and adults who consented to their own medical care. Physicians were randomly assigned to sit or stand during morning rounds for the first 3 days of their workweek. The last 4 days they provided care using the other posture. Blocks of 4 weeks were used to randomize the sit/stand order.

Figure 1

We measured the length of the physician‐patient interaction, asked both the physician and the patient to estimate the length of the interaction, and administered a written survey to the patient with questions about the physician's communication skills. Research assistants timed the interaction from outside the room and entered the room to consent patients and administer the survey after the physician departed. Survey questions were modeled on the physician communication questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We aggregated all answers other than the most positive answer because HCAHPS questions are analyzed according to a top box methodology. Adherence to the intervention was measured by asking the physician whether he or she actually sat or stood for each interaction. We administered a survey to physicians to collect demographics and feedback.

We estimated descriptive statistics for physician and patient participants using cross‐tabs and means. To estimate associations, we used logistic and linear regression that employed cluster‐adjusted t statistics and clustered patients within providers. This method optimizes estimation of standard errors (and corresponding confidence intervals and P values) when the number of clusters is small (16 physicians).[9] For our primary analysis, we analyzed as randomized using an intent‐to‐treat approach. In other words, those assigned to the standing group were analyzed in the standing group even if they actually sat (and vice versa). In a sensitivity analysis we used the same methods to analyze the data according actual provider posture as reported by the physician and not as randomized. We calculated the mean and range of the number of patients seen by physicians. We compared estimates of time spent between patients and providers and patients' satisfaction according to provider posture. We complied with the Consolidated Standards of Reporting Trials 2010 guidelines.[10] Our institutional review board approved this project. All participants provided written consent.

RESULTS

All 17 hospitalists attending on the service consented to participate; 1 did not see any patients involved in the study and was removed from the analysis. Sixty‐nine percent were female and 81% had been in practice for 3 years or less at the time of study enrollment; 94% reported standing when assigned to stand and 83% reported sitting when assigned to sit. We found 31% of physicians reported they routinely sat before participating in the study, and 81% said they would sit more after the study; this result approached statistical significance (exact McNemar P = 0.06). Of the 11 physicians who reported not routinely sitting before the study all, 7 cited not having a place to sit as a reason for not sitting. Other rationale provided included being too short to see the patient if seated, believing rounds would take more time if seated, and concerns about contact precautions. Comments in the postintervention survey regarding why providers planned to sit more centered around themes of having chairs available, thinking that sitting improves communication, and thinking that patients prefer providers to sit.

Two hundred eleven patients were assessed for eligibility. Fifty‐two were excluded (27 did not meet inclusion criteria and 25 declined to participate), leaving 159 participating patients. Seven patient‐physician pairs were inadvertently assigned the wrong intervention but were analyzed as randomized. There were no demographic differences between patient groups (Table 1). Physicians participating in the study saw an average of 13 study patients (range, 118) during the study. Mean time spent in the patient's room during rounds was 12:00 minutes for seated physicians and 12:10 for standing physicians (P = 0.84). Regardless of provider posture, patients overestimated the amount of time their physician spent in the room (mean difference 4:10 minutes, P = 0.01). Patients' estimates of the time the physician spent did not vary by posture (16:00 minutes for seated, 16:19 for standing, P = 0.86).

Patients whose physician sat on rounds were statistically significantly more likely to choose the answer always to the questions regarding their physician listening carefully to them (P = 0.02) and explaining things in a way that was easy to understand (P = 0.05, Table 2). There was no difference in the patients' response to questions about the physician interrupting the patient when talking or treating them with courtesy and respect. Nearly all patients chose just right when asked to rate the amount of time their physician had spent with them on rounds (Table 2). The results of our sensitivity analysis that classified physicians according to their actual posture yielded different results; none of the findings in that analysis including questions regarding the physician listening carefully or explaining things in a way that was easy to understand were statistically significant (see Supporting Information, Appendix 1, in the online version of this article).

DISCUSSION

In our study involving general medicine inpatients cared for by academic hospitalists, physicians did not spend more time in the room when seated, and were willing to adopt this practice. Patients perceived that seated compared to standing physicians listened more carefully and explained things in a way that was easy to understand when analyzed using an intent‐to‐treat approach. Patients did not perceive that seated physicians spent more time with them than standing physicians. To our knowledge, this is the first study showing the effects of hospitalist rounding posture on patient experience.

Our finding that patients rated seated physicians more highly on listening carefully and explaining things well indicates that training hospitalists to sit at the bedside may ultimately improve patient satisfaction. Our findings suggest seated interaction may improve satisfaction with communication without increasing time burden on physicians. However, given that these findings were not statistically significant when we analyzed our data according to actual behavior, larger studies should verify the impact of physician posture on patient experience.

Previous studies found that a minority of physicians sit in the inpatient setting, but did not study barriers to sitting while on rounds.[7, 8] A majority of physicians in our study sat when instructed to do so and when chairs were provided, and over 80% of physicians in our study said they planned to continue sitting while on rounds after the study was complete. A lack of chairs may be a major barrier to physicians adopting this facet of etiquette‐based medicine, and institutions wishing to promote this practice should consider providing chairs. Written comments from physician participants suggest physicians who are introduced to this practice enjoy sitting and think it improves physician‐patient communication. Further studies are needed to test our assumption that physicians continue sitting when chairs are provided.

Our work differs from previous studies. Johnson et al. studied interactions in the emergency room with a mean length of 8.6 minutes,[5] and Swayden et al. studied postoperative visits by a single neurosurgeon with a mean length of about 1 minute.[6] One explanation for the lack of a difference in time spent by posture might be that an average visit time of 12 minutes passes a threshold where patients make more accurate estimates of visit length or where factors other than posture more strongly influence perceptions of duration.

Limitations of our study include the relatively small sample size, single location, and limitation to English‐speaking patients able to consent themselves. Reasons for the limited sample size include that chairs were only installed in 2 units, and not all patients on the unit were under the care of participating physicians. Physician subjects were not blinded to their interactions being timed or to the fact that patients were surveyed about their communication skills. It is possible that factors that may have affected patients' responses such as severity of illness, number of consultants involved in their care, or prior experiences in the healthcare system were not equally distributed between our 2 groups. Additionally, our use of questions similar to those used in the HCAHPS instrument is not compliant with Centers for Medicare and Medicaid Services (CMS) policy. We caution others against using questions that might invalidate their hospital's participation in CMS payment programs.

Our study was limited to rounds involving 1 physician; our practice is that in a larger team the presenting member is encouraged to sit and others sit if there are additional chairs. Best practices on a teaching service are unclear and could be the subject of further study. The longer‐term sustainability of the practice of sitting on rounds is unclear. However, our physician subjects reported that they plan to continue to sit after the study, and we have shared the results with physicians in order to provide them with evidence supporting this practice. Not having a place to sit and thinking that sitting increases the amount of time spent on rounds were concerns provided in our preintervention survey, and we believe our study addresses these concerns.

Our study demonstrates the effects of a simple intervention on patient satisfaction without increasing burden on providers. Sitting at the bedside does not impact the amount of time spent with the patient, but may improve the patient's perception of the physician's communication skills and thus impact the patient experience. This simple intervention could improve patient satisfaction at little cost.

Sitting while interacting with patients is standard in the outpatient setting and encouraged in the inpatient setting as a best practice.[1, 2] Michael W. Kahn defined etiquette‐based medicine as a set of easily taught behaviors that demonstrate respect for the patient; sitting at the bedside is included.[1] A prominent healthcare consulting group also recommends that physicians and nurses sit at the bedside, claiming that the patient will estimate you were in the room 3 times longer.[3] Previous studies suggest patients may perceive physicians who sit at the bedside as more compassionate and as spending more time with them, and may perceive the overall interaction as more positive when the physician sits.[4, 5, 6] Two small studies found that patients perceived the physician as having spent more time with them if he or she sat rather than stood.[5, 6] A study in the emergency department found no effect of posture on patient perception of physician communication skills, and a study of a single attending neurosurgeon found that patients reported a better understanding of their condition when the physician sat.[5, 6] The effect of physician posture on hospitalist physician‐patient communication has not been previously studied. Despite evidence that sitting in the inpatient setting may improve physician‐patient communication, studies suggest that physicians rarely sit at the bedside of inpatients.[7, 8]

We conducted a cluster‐randomized trial of the impact of hospitalist physician posture during morning rounds. We hypothesized that patients whose physician sat rather than stood would perceive that their physician spent more time with them and would rate the physician's communication skills more highly. We also hypothesized that sitting would not prolong the length of the patient‐physician encounter.

PATIENTS AND METHODS

We conducted a cluster‐randomized clinical trial with a crossover component randomizing physicians on the order of sit/stand within a consecutive 7‐day workweek. We enrolled patients being cared for by attending hospitalists on a resident‐uncovered general internal medicine service in an academic tertiary care hospital. We also enrolled the hospitalists and collected demographics and practice information. Wall‐mounted folding chairs (Figure 1) were installed in all rooms on two 28‐bed units for use by physicians. Eligible patients were newly admitted or transferred from the intensive care unit between June 2014 and June 2015, English speaking, and adults who consented to their own medical care. Physicians were randomly assigned to sit or stand during morning rounds for the first 3 days of their workweek. The last 4 days they provided care using the other posture. Blocks of 4 weeks were used to randomize the sit/stand order.

Figure 1

We measured the length of the physician‐patient interaction, asked both the physician and the patient to estimate the length of the interaction, and administered a written survey to the patient with questions about the physician's communication skills. Research assistants timed the interaction from outside the room and entered the room to consent patients and administer the survey after the physician departed. Survey questions were modeled on the physician communication questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We aggregated all answers other than the most positive answer because HCAHPS questions are analyzed according to a top box methodology. Adherence to the intervention was measured by asking the physician whether he or she actually sat or stood for each interaction. We administered a survey to physicians to collect demographics and feedback.

We estimated descriptive statistics for physician and patient participants using cross‐tabs and means. To estimate associations, we used logistic and linear regression that employed cluster‐adjusted t statistics and clustered patients within providers. This method optimizes estimation of standard errors (and corresponding confidence intervals and P values) when the number of clusters is small (16 physicians).[9] For our primary analysis, we analyzed as randomized using an intent‐to‐treat approach. In other words, those assigned to the standing group were analyzed in the standing group even if they actually sat (and vice versa). In a sensitivity analysis we used the same methods to analyze the data according actual provider posture as reported by the physician and not as randomized. We calculated the mean and range of the number of patients seen by physicians. We compared estimates of time spent between patients and providers and patients' satisfaction according to provider posture. We complied with the Consolidated Standards of Reporting Trials 2010 guidelines.[10] Our institutional review board approved this project. All participants provided written consent.

RESULTS

All 17 hospitalists attending on the service consented to participate; 1 did not see any patients involved in the study and was removed from the analysis. Sixty‐nine percent were female and 81% had been in practice for 3 years or less at the time of study enrollment; 94% reported standing when assigned to stand and 83% reported sitting when assigned to sit. We found 31% of physicians reported they routinely sat before participating in the study, and 81% said they would sit more after the study; this result approached statistical significance (exact McNemar P = 0.06). Of the 11 physicians who reported not routinely sitting before the study all, 7 cited not having a place to sit as a reason for not sitting. Other rationale provided included being too short to see the patient if seated, believing rounds would take more time if seated, and concerns about contact precautions. Comments in the postintervention survey regarding why providers planned to sit more centered around themes of having chairs available, thinking that sitting improves communication, and thinking that patients prefer providers to sit.

Two hundred eleven patients were assessed for eligibility. Fifty‐two were excluded (27 did not meet inclusion criteria and 25 declined to participate), leaving 159 participating patients. Seven patient‐physician pairs were inadvertently assigned the wrong intervention but were analyzed as randomized. There were no demographic differences between patient groups (Table 1). Physicians participating in the study saw an average of 13 study patients (range, 118) during the study. Mean time spent in the patient's room during rounds was 12:00 minutes for seated physicians and 12:10 for standing physicians (P = 0.84). Regardless of provider posture, patients overestimated the amount of time their physician spent in the room (mean difference 4:10 minutes, P = 0.01). Patients' estimates of the time the physician spent did not vary by posture (16:00 minutes for seated, 16:19 for standing, P = 0.86).

Patients whose physician sat on rounds were statistically significantly more likely to choose the answer always to the questions regarding their physician listening carefully to them (P = 0.02) and explaining things in a way that was easy to understand (P = 0.05, Table 2). There was no difference in the patients' response to questions about the physician interrupting the patient when talking or treating them with courtesy and respect. Nearly all patients chose just right when asked to rate the amount of time their physician had spent with them on rounds (Table 2). The results of our sensitivity analysis that classified physicians according to their actual posture yielded different results; none of the findings in that analysis including questions regarding the physician listening carefully or explaining things in a way that was easy to understand were statistically significant (see Supporting Information, Appendix 1, in the online version of this article).

DISCUSSION

In our study involving general medicine inpatients cared for by academic hospitalists, physicians did not spend more time in the room when seated, and were willing to adopt this practice. Patients perceived that seated compared to standing physicians listened more carefully and explained things in a way that was easy to understand when analyzed using an intent‐to‐treat approach. Patients did not perceive that seated physicians spent more time with them than standing physicians. To our knowledge, this is the first study showing the effects of hospitalist rounding posture on patient experience.

Our finding that patients rated seated physicians more highly on listening carefully and explaining things well indicates that training hospitalists to sit at the bedside may ultimately improve patient satisfaction. Our findings suggest seated interaction may improve satisfaction with communication without increasing time burden on physicians. However, given that these findings were not statistically significant when we analyzed our data according to actual behavior, larger studies should verify the impact of physician posture on patient experience.

Previous studies found that a minority of physicians sit in the inpatient setting, but did not study barriers to sitting while on rounds.[7, 8] A majority of physicians in our study sat when instructed to do so and when chairs were provided, and over 80% of physicians in our study said they planned to continue sitting while on rounds after the study was complete. A lack of chairs may be a major barrier to physicians adopting this facet of etiquette‐based medicine, and institutions wishing to promote this practice should consider providing chairs. Written comments from physician participants suggest physicians who are introduced to this practice enjoy sitting and think it improves physician‐patient communication. Further studies are needed to test our assumption that physicians continue sitting when chairs are provided.

Our work differs from previous studies. Johnson et al. studied interactions in the emergency room with a mean length of 8.6 minutes,[5] and Swayden et al. studied postoperative visits by a single neurosurgeon with a mean length of about 1 minute.[6] One explanation for the lack of a difference in time spent by posture might be that an average visit time of 12 minutes passes a threshold where patients make more accurate estimates of visit length or where factors other than posture more strongly influence perceptions of duration.

Limitations of our study include the relatively small sample size, single location, and limitation to English‐speaking patients able to consent themselves. Reasons for the limited sample size include that chairs were only installed in 2 units, and not all patients on the unit were under the care of participating physicians. Physician subjects were not blinded to their interactions being timed or to the fact that patients were surveyed about their communication skills. It is possible that factors that may have affected patients' responses such as severity of illness, number of consultants involved in their care, or prior experiences in the healthcare system were not equally distributed between our 2 groups. Additionally, our use of questions similar to those used in the HCAHPS instrument is not compliant with Centers for Medicare and Medicaid Services (CMS) policy. We caution others against using questions that might invalidate their hospital's participation in CMS payment programs.

Our study was limited to rounds involving 1 physician; our practice is that in a larger team the presenting member is encouraged to sit and others sit if there are additional chairs. Best practices on a teaching service are unclear and could be the subject of further study. The longer‐term sustainability of the practice of sitting on rounds is unclear. However, our physician subjects reported that they plan to continue to sit after the study, and we have shared the results with physicians in order to provide them with evidence supporting this practice. Not having a place to sit and thinking that sitting increases the amount of time spent on rounds were concerns provided in our preintervention survey, and we believe our study addresses these concerns.

Our study demonstrates the effects of a simple intervention on patient satisfaction without increasing burden on providers. Sitting at the bedside does not impact the amount of time spent with the patient, but may improve the patient's perception of the physician's communication skills and thus impact the patient experience. This simple intervention could improve patient satisfaction at little cost.

Sitting while interacting with patients is standard in the outpatient setting and encouraged in the inpatient setting as a best practice.[1, 2] Michael W. Kahn defined etiquette‐based medicine as a set of easily taught behaviors that demonstrate respect for the patient; sitting at the bedside is included.[1] A prominent healthcare consulting group also recommends that physicians and nurses sit at the bedside, claiming that the patient will estimate you were in the room 3 times longer.[3] Previous studies suggest patients may perceive physicians who sit at the bedside as more compassionate and as spending more time with them, and may perceive the overall interaction as more positive when the physician sits.[4, 5, 6] Two small studies found that patients perceived the physician as having spent more time with them if he or she sat rather than stood.[5, 6] A study in the emergency department found no effect of posture on patient perception of physician communication skills, and a study of a single attending neurosurgeon found that patients reported a better understanding of their condition when the physician sat.[5, 6] The effect of physician posture on hospitalist physician‐patient communication has not been previously studied. Despite evidence that sitting in the inpatient setting may improve physician‐patient communication, studies suggest that physicians rarely sit at the bedside of inpatients.[7, 8]

We conducted a cluster‐randomized trial of the impact of hospitalist physician posture during morning rounds. We hypothesized that patients whose physician sat rather than stood would perceive that their physician spent more time with them and would rate the physician's communication skills more highly. We also hypothesized that sitting would not prolong the length of the patient‐physician encounter.

PATIENTS AND METHODS

We conducted a cluster‐randomized clinical trial with a crossover component randomizing physicians on the order of sit/stand within a consecutive 7‐day workweek. We enrolled patients being cared for by attending hospitalists on a resident‐uncovered general internal medicine service in an academic tertiary care hospital. We also enrolled the hospitalists and collected demographics and practice information. Wall‐mounted folding chairs (Figure 1) were installed in all rooms on two 28‐bed units for use by physicians. Eligible patients were newly admitted or transferred from the intensive care unit between June 2014 and June 2015, English speaking, and adults who consented to their own medical care. Physicians were randomly assigned to sit or stand during morning rounds for the first 3 days of their workweek. The last 4 days they provided care using the other posture. Blocks of 4 weeks were used to randomize the sit/stand order.

Figure 1

We measured the length of the physician‐patient interaction, asked both the physician and the patient to estimate the length of the interaction, and administered a written survey to the patient with questions about the physician's communication skills. Research assistants timed the interaction from outside the room and entered the room to consent patients and administer the survey after the physician departed. Survey questions were modeled on the physician communication questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We aggregated all answers other than the most positive answer because HCAHPS questions are analyzed according to a top box methodology. Adherence to the intervention was measured by asking the physician whether he or she actually sat or stood for each interaction. We administered a survey to physicians to collect demographics and feedback.

We estimated descriptive statistics for physician and patient participants using cross‐tabs and means. To estimate associations, we used logistic and linear regression that employed cluster‐adjusted t statistics and clustered patients within providers. This method optimizes estimation of standard errors (and corresponding confidence intervals and P values) when the number of clusters is small (16 physicians).[9] For our primary analysis, we analyzed as randomized using an intent‐to‐treat approach. In other words, those assigned to the standing group were analyzed in the standing group even if they actually sat (and vice versa). In a sensitivity analysis we used the same methods to analyze the data according actual provider posture as reported by the physician and not as randomized. We calculated the mean and range of the number of patients seen by physicians. We compared estimates of time spent between patients and providers and patients' satisfaction according to provider posture. We complied with the Consolidated Standards of Reporting Trials 2010 guidelines.[10] Our institutional review board approved this project. All participants provided written consent.

RESULTS

All 17 hospitalists attending on the service consented to participate; 1 did not see any patients involved in the study and was removed from the analysis. Sixty‐nine percent were female and 81% had been in practice for 3 years or less at the time of study enrollment; 94% reported standing when assigned to stand and 83% reported sitting when assigned to sit. We found 31% of physicians reported they routinely sat before participating in the study, and 81% said they would sit more after the study; this result approached statistical significance (exact McNemar P = 0.06). Of the 11 physicians who reported not routinely sitting before the study all, 7 cited not having a place to sit as a reason for not sitting. Other rationale provided included being too short to see the patient if seated, believing rounds would take more time if seated, and concerns about contact precautions. Comments in the postintervention survey regarding why providers planned to sit more centered around themes of having chairs available, thinking that sitting improves communication, and thinking that patients prefer providers to sit.

Two hundred eleven patients were assessed for eligibility. Fifty‐two were excluded (27 did not meet inclusion criteria and 25 declined to participate), leaving 159 participating patients. Seven patient‐physician pairs were inadvertently assigned the wrong intervention but were analyzed as randomized. There were no demographic differences between patient groups (Table 1). Physicians participating in the study saw an average of 13 study patients (range, 118) during the study. Mean time spent in the patient's room during rounds was 12:00 minutes for seated physicians and 12:10 for standing physicians (P = 0.84). Regardless of provider posture, patients overestimated the amount of time their physician spent in the room (mean difference 4:10 minutes, P = 0.01). Patients' estimates of the time the physician spent did not vary by posture (16:00 minutes for seated, 16:19 for standing, P = 0.86).

Patients whose physician sat on rounds were statistically significantly more likely to choose the answer always to the questions regarding their physician listening carefully to them (P = 0.02) and explaining things in a way that was easy to understand (P = 0.05, Table 2). There was no difference in the patients' response to questions about the physician interrupting the patient when talking or treating them with courtesy and respect. Nearly all patients chose just right when asked to rate the amount of time their physician had spent with them on rounds (Table 2). The results of our sensitivity analysis that classified physicians according to their actual posture yielded different results; none of the findings in that analysis including questions regarding the physician listening carefully or explaining things in a way that was easy to understand were statistically significant (see Supporting Information, Appendix 1, in the online version of this article).

DISCUSSION

In our study involving general medicine inpatients cared for by academic hospitalists, physicians did not spend more time in the room when seated, and were willing to adopt this practice. Patients perceived that seated compared to standing physicians listened more carefully and explained things in a way that was easy to understand when analyzed using an intent‐to‐treat approach. Patients did not perceive that seated physicians spent more time with them than standing physicians. To our knowledge, this is the first study showing the effects of hospitalist rounding posture on patient experience.

Our finding that patients rated seated physicians more highly on listening carefully and explaining things well indicates that training hospitalists to sit at the bedside may ultimately improve patient satisfaction. Our findings suggest seated interaction may improve satisfaction with communication without increasing time burden on physicians. However, given that these findings were not statistically significant when we analyzed our data according to actual behavior, larger studies should verify the impact of physician posture on patient experience.

Previous studies found that a minority of physicians sit in the inpatient setting, but did not study barriers to sitting while on rounds.[7, 8] A majority of physicians in our study sat when instructed to do so and when chairs were provided, and over 80% of physicians in our study said they planned to continue sitting while on rounds after the study was complete. A lack of chairs may be a major barrier to physicians adopting this facet of etiquette‐based medicine, and institutions wishing to promote this practice should consider providing chairs. Written comments from physician participants suggest physicians who are introduced to this practice enjoy sitting and think it improves physician‐patient communication. Further studies are needed to test our assumption that physicians continue sitting when chairs are provided.

Our work differs from previous studies. Johnson et al. studied interactions in the emergency room with a mean length of 8.6 minutes,[5] and Swayden et al. studied postoperative visits by a single neurosurgeon with a mean length of about 1 minute.[6] One explanation for the lack of a difference in time spent by posture might be that an average visit time of 12 minutes passes a threshold where patients make more accurate estimates of visit length or where factors other than posture more strongly influence perceptions of duration.

Limitations of our study include the relatively small sample size, single location, and limitation to English‐speaking patients able to consent themselves. Reasons for the limited sample size include that chairs were only installed in 2 units, and not all patients on the unit were under the care of participating physicians. Physician subjects were not blinded to their interactions being timed or to the fact that patients were surveyed about their communication skills. It is possible that factors that may have affected patients' responses such as severity of illness, number of consultants involved in their care, or prior experiences in the healthcare system were not equally distributed between our 2 groups. Additionally, our use of questions similar to those used in the HCAHPS instrument is not compliant with Centers for Medicare and Medicaid Services (CMS) policy. We caution others against using questions that might invalidate their hospital's participation in CMS payment programs.

Our study was limited to rounds involving 1 physician; our practice is that in a larger team the presenting member is encouraged to sit and others sit if there are additional chairs. Best practices on a teaching service are unclear and could be the subject of further study. The longer‐term sustainability of the practice of sitting on rounds is unclear. However, our physician subjects reported that they plan to continue to sit after the study, and we have shared the results with physicians in order to provide them with evidence supporting this practice. Not having a place to sit and thinking that sitting increases the amount of time spent on rounds were concerns provided in our preintervention survey, and we believe our study addresses these concerns.

Our study demonstrates the effects of a simple intervention on patient satisfaction without increasing burden on providers. Sitting at the bedside does not impact the amount of time spent with the patient, but may improve the patient's perception of the physician's communication skills and thus impact the patient experience. This simple intervention could improve patient satisfaction at little cost.

Hand hygiene (HH) is believed to be one of the single most important interventions to prevent healthcare‐associated infection, yet physicians are notorious for their poor compliance.[1, 2, 3] At our 800‐bed acute care academic hospital, we implemented a multifaceted HH program[4] in 2007, which was associated with improved HH compliance rates from 43% to 87%. Despite this improvement, HH compliance among physicians remained suboptimal, with rates below 60% in some patient areas. A targeted campaign focused on recruitment of physician champions, resulted in some improvement, but physician compliance has consistently remained below performance of nurses (70%75% for physicians vs 85%90% for nurses).

Our experience parallels the results seen in multinational surveys demonstrating consistently lower physician HH compliance.[5] Given the multiple improvement efforts directed at physicians and the apparent ceiling observed in HH performance, we wanted to confirm whether physicians are truly recalcitrant to cleaning their hands, or whether lower compliance among physicians reflected a differential in the Hawthorne effect inherent to direct observation methods. Specifically, we wondered if nurses tend to recognize auditors more readily than physicians and therefore show higher apparent HH compliance when auditors are present. We also wanted to verify whether the behavior of attending physicians influenced compliance of their physician trainees. To test these hypotheses, we trained 2 clinical observers to covertly measure HH compliance of nurses and physicians on 3 different clinical services.

METHODS

Between May 27, 2015 and July 31, 2015, 2 student observers joined clinical rotations on physician and nursing teams, respectively. Healthcare teams were unaware that the student observers were measuring HH compliance during their clinical rotation. Students rotated in the emergency department, general medical and surgical wards for no more than 1 week at a time to increase exposure to different providers and minimize risk of exposing the covert observation.

Prior to the study period, the students underwent training and validation with a hospital HH auditor at another clinical setting offsite to avoid any recognition of these students by healthcare providers as observers of HH at the main hospital. Training with the auditors occurred until interobserver agreement between all HH opportunities reached 100% agreement for 2 consecutive observation days.

During their rotations, students covertly recorded HH compliance based on moments of hand hygiene[4] and also noted location, presence, and compliance of the attending physician, team size during patient encounter, and isolation requirements. Both students measured HH compliance of nurses and physicians around them. Although students spent the majority of their time with their assigned physician or nurse teams, they did not limit their observations to these individuals only, but recorded compliance of any nurse or physician on the ward as long as they were within sight during an HH opportunity. To limit clustering of observations of the same healthcare worker, up to a maximum of 2 observations per healthcare worker per day was recorded.

We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Differences in proportion of HH compliance were compared with hospital audits during the same period with a ² test. Difference between differences in overtly and covertly measured HH compliance for nurses and physicians was compared using Breslow day test.

The study was approved by the hospital's research ethics board. Although deception was used in this study,[2, 6] all data were collected for quality improvement purposes, and the aggregate results were disclosed to hospital staff following the study.

RESULTS

Covertly observed HH compliance was 50.0% (799/1597) compared with 83.7% (2769/3309) recorded by hospital auditors during the same time period (P < 0.0002) (Table 1). There was no significant difference in the compliance measured by each student (50.1%, 473/928 vs 48.7%, 326/669) (P = 0.3), and their results were combined for the rest of the analysis. Compliance before contact with the patient or patient environment was 43.1% (344/798), 74.3% (26/35) before clean/aseptic procedures, 34.8% (8/23) after potential body fluid exposure, and 56.8% (483/851) after contact with the patient or patient environment. Healthcare providers examining patients with isolation precautions were found to have a HH compliance of 74.8% (101/135) compared to 47.0% (385/820) when isolation precautions were not required (P < 0.0002).

Hospital auditor data showed that surgery and medicine had similarly high rates of compliance (91.0% and 85.0%, respectively), whereas the emergency department had a notably lower rate of 73.8%. Covert observation confirmed a lower rate in the emergency department (43.9%), but showed a higher compliance on general medicine than on surgery (58.9% vs 45.7%; P = 0.02). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2%, 175/239 vs. 54.2%, 469/865); for nurses this difference was much higher at 40.7% (85.8%, 754/879 vs. 45.1%, 330/732) (P < 0.0001) (Table 1).

In terms of physician compliance, primary teams tended to have lower HH compliance of 50.4% (323/641) compared with consulting services at 57.0% (158/277) (P = 0.06). Team rounds of 3 members were associated with higher compliance compared with encounters involving <3 members (62.1%, 282/454 vs. 42.0%, 128/308) (P < 0.0002). Presence of attending physician did not affect trainee HH compliance (55.5%, 201/362 when attending present vs. 56.8%, 133/234 when attending absent; P = 0.79). However, trainee HH compliance improved markedly when attending staff cleaned their hands and decreased markedly when they did not (79.5%, 174/219 vs. 18.9%, 27/143; P < 0.0002).

DISCUSSION

We introduced covert HH observers at our hospital to determine whether differences in Hawthorne effect accounted for measured disparity between physician HH compliance, and to gain further insights into the barriers and enablers of physician HH compliance. We discovered that performance differences between physicians and nurses decreased when neither group was aware that HH was being measured, suggesting that healthcare professions are differentially affected by the Hawthorne effect. This difference may be explained by the continuity of nurses on the ward that makes them more aware of visitors like HH auditors,[7] compared with physicians who rotate periodically on the ward.

Although hospital auditors play a central role in HH education through in‐the‐moment feedback, use of these data to benchmark performance can lead to inappropriate inferences about HH compliance. Prior studies using automated HH surveillance have suggested that the magnitude of the Hawthorne effect varies based on baseline HH rates,[8] whereas our study suggests a differential Hawthorne effect between professions and clinical services. If we relied only on auditor data, we would have continued to believe that only physicians in our organization had poor HH compliance, and we would not be aware of the global nature of the HH problem.

Our results are similar to that of Pan et al., who used covert medical students to measure HH and found compliance of 44.1% compared with 94.1% by unit auditors.[2] Because their study involved an active feedback intervention, the differential in Hawthorne effect between professions could not be reliably assessed. However, they observed a progressive increase in nurse HH compliance using covert observation methods, suggesting improvement in HH performance independent of observer bias.[7]

Covert observation in our study also provided important insights regarding barriers and enablers of HH compliance. Self‐preservation behaviors were common among both nurses and physicians, as HH compliance was consistently higher after patient contact compared to before or when seeing patients who required additional precautions. This finding confirms that the perceived risk of transmission seems to be a powerful motivating factor for HH.[9] Larger groups of trainees were more likely to clean their hands, likely due to peer effects.[10] The strong impact of role modeling on HH was also noted as previously suggested in the literature,[3, 6] but our study captures the magnitude of this effect. Whether or not the attending physician cleaned their hands during rounds either positively or negatively influenced HH compliance of the rest of the physician team (80% when compliant vs 20% when noncompliant).

Our study has several important limitations. The differential Hawthorne effect seen at our center may not reflect other institutions that have numerous HH auditors or high staff turnover resulting in lower ability to recognize auditors. We cannot exclude the possibility of Hawthorne effect using covert methods that could have affected nurse and physician performance differently, but frequent rotation of the students helped maintain covertness of observations. Finally, due to the nature of the covert student observers, a longer observation time frame could not be sustained.

Our experience using covert HH auditors suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences regarding HH performance due to relative differences in Hawthorne effect. The answer to the question regarding whether physicians clean their hands appears to be that they do just as often as nurses, but that all healthcare workers have tremendous room for improvement. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees.

Hand hygiene (HH) is believed to be one of the single most important interventions to prevent healthcare‐associated infection, yet physicians are notorious for their poor compliance.[1, 2, 3] At our 800‐bed acute care academic hospital, we implemented a multifaceted HH program[4] in 2007, which was associated with improved HH compliance rates from 43% to 87%. Despite this improvement, HH compliance among physicians remained suboptimal, with rates below 60% in some patient areas. A targeted campaign focused on recruitment of physician champions, resulted in some improvement, but physician compliance has consistently remained below performance of nurses (70%75% for physicians vs 85%90% for nurses).

Our experience parallels the results seen in multinational surveys demonstrating consistently lower physician HH compliance.[5] Given the multiple improvement efforts directed at physicians and the apparent ceiling observed in HH performance, we wanted to confirm whether physicians are truly recalcitrant to cleaning their hands, or whether lower compliance among physicians reflected a differential in the Hawthorne effect inherent to direct observation methods. Specifically, we wondered if nurses tend to recognize auditors more readily than physicians and therefore show higher apparent HH compliance when auditors are present. We also wanted to verify whether the behavior of attending physicians influenced compliance of their physician trainees. To test these hypotheses, we trained 2 clinical observers to covertly measure HH compliance of nurses and physicians on 3 different clinical services.

METHODS

Between May 27, 2015 and July 31, 2015, 2 student observers joined clinical rotations on physician and nursing teams, respectively. Healthcare teams were unaware that the student observers were measuring HH compliance during their clinical rotation. Students rotated in the emergency department, general medical and surgical wards for no more than 1 week at a time to increase exposure to different providers and minimize risk of exposing the covert observation.

Prior to the study period, the students underwent training and validation with a hospital HH auditor at another clinical setting offsite to avoid any recognition of these students by healthcare providers as observers of HH at the main hospital. Training with the auditors occurred until interobserver agreement between all HH opportunities reached 100% agreement for 2 consecutive observation days.

During their rotations, students covertly recorded HH compliance based on moments of hand hygiene[4] and also noted location, presence, and compliance of the attending physician, team size during patient encounter, and isolation requirements. Both students measured HH compliance of nurses and physicians around them. Although students spent the majority of their time with their assigned physician or nurse teams, they did not limit their observations to these individuals only, but recorded compliance of any nurse or physician on the ward as long as they were within sight during an HH opportunity. To limit clustering of observations of the same healthcare worker, up to a maximum of 2 observations per healthcare worker per day was recorded.

We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Differences in proportion of HH compliance were compared with hospital audits during the same period with a ² test. Difference between differences in overtly and covertly measured HH compliance for nurses and physicians was compared using Breslow day test.

The study was approved by the hospital's research ethics board. Although deception was used in this study,[2, 6] all data were collected for quality improvement purposes, and the aggregate results were disclosed to hospital staff following the study.

RESULTS

Covertly observed HH compliance was 50.0% (799/1597) compared with 83.7% (2769/3309) recorded by hospital auditors during the same time period (P < 0.0002) (Table 1). There was no significant difference in the compliance measured by each student (50.1%, 473/928 vs 48.7%, 326/669) (P = 0.3), and their results were combined for the rest of the analysis. Compliance before contact with the patient or patient environment was 43.1% (344/798), 74.3% (26/35) before clean/aseptic procedures, 34.8% (8/23) after potential body fluid exposure, and 56.8% (483/851) after contact with the patient or patient environment. Healthcare providers examining patients with isolation precautions were found to have a HH compliance of 74.8% (101/135) compared to 47.0% (385/820) when isolation precautions were not required (P < 0.0002).

Hospital auditor data showed that surgery and medicine had similarly high rates of compliance (91.0% and 85.0%, respectively), whereas the emergency department had a notably lower rate of 73.8%. Covert observation confirmed a lower rate in the emergency department (43.9%), but showed a higher compliance on general medicine than on surgery (58.9% vs 45.7%; P = 0.02). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2%, 175/239 vs. 54.2%, 469/865); for nurses this difference was much higher at 40.7% (85.8%, 754/879 vs. 45.1%, 330/732) (P < 0.0001) (Table 1).

In terms of physician compliance, primary teams tended to have lower HH compliance of 50.4% (323/641) compared with consulting services at 57.0% (158/277) (P = 0.06). Team rounds of 3 members were associated with higher compliance compared with encounters involving <3 members (62.1%, 282/454 vs. 42.0%, 128/308) (P < 0.0002). Presence of attending physician did not affect trainee HH compliance (55.5%, 201/362 when attending present vs. 56.8%, 133/234 when attending absent; P = 0.79). However, trainee HH compliance improved markedly when attending staff cleaned their hands and decreased markedly when they did not (79.5%, 174/219 vs. 18.9%, 27/143; P < 0.0002).

DISCUSSION

We introduced covert HH observers at our hospital to determine whether differences in Hawthorne effect accounted for measured disparity between physician HH compliance, and to gain further insights into the barriers and enablers of physician HH compliance. We discovered that performance differences between physicians and nurses decreased when neither group was aware that HH was being measured, suggesting that healthcare professions are differentially affected by the Hawthorne effect. This difference may be explained by the continuity of nurses on the ward that makes them more aware of visitors like HH auditors,[7] compared with physicians who rotate periodically on the ward.

Although hospital auditors play a central role in HH education through in‐the‐moment feedback, use of these data to benchmark performance can lead to inappropriate inferences about HH compliance. Prior studies using automated HH surveillance have suggested that the magnitude of the Hawthorne effect varies based on baseline HH rates,[8] whereas our study suggests a differential Hawthorne effect between professions and clinical services. If we relied only on auditor data, we would have continued to believe that only physicians in our organization had poor HH compliance, and we would not be aware of the global nature of the HH problem.

Our results are similar to that of Pan et al., who used covert medical students to measure HH and found compliance of 44.1% compared with 94.1% by unit auditors.[2] Because their study involved an active feedback intervention, the differential in Hawthorne effect between professions could not be reliably assessed. However, they observed a progressive increase in nurse HH compliance using covert observation methods, suggesting improvement in HH performance independent of observer bias.[7]

Covert observation in our study also provided important insights regarding barriers and enablers of HH compliance. Self‐preservation behaviors were common among both nurses and physicians, as HH compliance was consistently higher after patient contact compared to before or when seeing patients who required additional precautions. This finding confirms that the perceived risk of transmission seems to be a powerful motivating factor for HH.[9] Larger groups of trainees were more likely to clean their hands, likely due to peer effects.[10] The strong impact of role modeling on HH was also noted as previously suggested in the literature,[3, 6] but our study captures the magnitude of this effect. Whether or not the attending physician cleaned their hands during rounds either positively or negatively influenced HH compliance of the rest of the physician team (80% when compliant vs 20% when noncompliant).

Our study has several important limitations. The differential Hawthorne effect seen at our center may not reflect other institutions that have numerous HH auditors or high staff turnover resulting in lower ability to recognize auditors. We cannot exclude the possibility of Hawthorne effect using covert methods that could have affected nurse and physician performance differently, but frequent rotation of the students helped maintain covertness of observations. Finally, due to the nature of the covert student observers, a longer observation time frame could not be sustained.

Our experience using covert HH auditors suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences regarding HH performance due to relative differences in Hawthorne effect. The answer to the question regarding whether physicians clean their hands appears to be that they do just as often as nurses, but that all healthcare workers have tremendous room for improvement. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees.

Hand hygiene (HH) is believed to be one of the single most important interventions to prevent healthcare‐associated infection, yet physicians are notorious for their poor compliance.[1, 2, 3] At our 800‐bed acute care academic hospital, we implemented a multifaceted HH program[4] in 2007, which was associated with improved HH compliance rates from 43% to 87%. Despite this improvement, HH compliance among physicians remained suboptimal, with rates below 60% in some patient areas. A targeted campaign focused on recruitment of physician champions, resulted in some improvement, but physician compliance has consistently remained below performance of nurses (70%75% for physicians vs 85%90% for nurses).

Our experience parallels the results seen in multinational surveys demonstrating consistently lower physician HH compliance.[5] Given the multiple improvement efforts directed at physicians and the apparent ceiling observed in HH performance, we wanted to confirm whether physicians are truly recalcitrant to cleaning their hands, or whether lower compliance among physicians reflected a differential in the Hawthorne effect inherent to direct observation methods. Specifically, we wondered if nurses tend to recognize auditors more readily than physicians and therefore show higher apparent HH compliance when auditors are present. We also wanted to verify whether the behavior of attending physicians influenced compliance of their physician trainees. To test these hypotheses, we trained 2 clinical observers to covertly measure HH compliance of nurses and physicians on 3 different clinical services.

METHODS

Between May 27, 2015 and July 31, 2015, 2 student observers joined clinical rotations on physician and nursing teams, respectively. Healthcare teams were unaware that the student observers were measuring HH compliance during their clinical rotation. Students rotated in the emergency department, general medical and surgical wards for no more than 1 week at a time to increase exposure to different providers and minimize risk of exposing the covert observation.

Prior to the study period, the students underwent training and validation with a hospital HH auditor at another clinical setting offsite to avoid any recognition of these students by healthcare providers as observers of HH at the main hospital. Training with the auditors occurred until interobserver agreement between all HH opportunities reached 100% agreement for 2 consecutive observation days.

During their rotations, students covertly recorded HH compliance based on moments of hand hygiene[4] and also noted location, presence, and compliance of the attending physician, team size during patient encounter, and isolation requirements. Both students measured HH compliance of nurses and physicians around them. Although students spent the majority of their time with their assigned physician or nurse teams, they did not limit their observations to these individuals only, but recorded compliance of any nurse or physician on the ward as long as they were within sight during an HH opportunity. To limit clustering of observations of the same healthcare worker, up to a maximum of 2 observations per healthcare worker per day was recorded.

We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Differences in proportion of HH compliance were compared with hospital audits during the same period with a ² test. Difference between differences in overtly and covertly measured HH compliance for nurses and physicians was compared using Breslow day test.

The study was approved by the hospital's research ethics board. Although deception was used in this study,[2, 6] all data were collected for quality improvement purposes, and the aggregate results were disclosed to hospital staff following the study.

RESULTS

Covertly observed HH compliance was 50.0% (799/1597) compared with 83.7% (2769/3309) recorded by hospital auditors during the same time period (P < 0.0002) (Table 1). There was no significant difference in the compliance measured by each student (50.1%, 473/928 vs 48.7%, 326/669) (P = 0.3), and their results were combined for the rest of the analysis. Compliance before contact with the patient or patient environment was 43.1% (344/798), 74.3% (26/35) before clean/aseptic procedures, 34.8% (8/23) after potential body fluid exposure, and 56.8% (483/851) after contact with the patient or patient environment. Healthcare providers examining patients with isolation precautions were found to have a HH compliance of 74.8% (101/135) compared to 47.0% (385/820) when isolation precautions were not required (P < 0.0002).

Hospital auditor data showed that surgery and medicine had similarly high rates of compliance (91.0% and 85.0%, respectively), whereas the emergency department had a notably lower rate of 73.8%. Covert observation confirmed a lower rate in the emergency department (43.9%), but showed a higher compliance on general medicine than on surgery (58.9% vs 45.7%; P = 0.02). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2%, 175/239 vs. 54.2%, 469/865); for nurses this difference was much higher at 40.7% (85.8%, 754/879 vs. 45.1%, 330/732) (P < 0.0001) (Table 1).

In terms of physician compliance, primary teams tended to have lower HH compliance of 50.4% (323/641) compared with consulting services at 57.0% (158/277) (P = 0.06). Team rounds of 3 members were associated with higher compliance compared with encounters involving <3 members (62.1%, 282/454 vs. 42.0%, 128/308) (P < 0.0002). Presence of attending physician did not affect trainee HH compliance (55.5%, 201/362 when attending present vs. 56.8%, 133/234 when attending absent; P = 0.79). However, trainee HH compliance improved markedly when attending staff cleaned their hands and decreased markedly when they did not (79.5%, 174/219 vs. 18.9%, 27/143; P < 0.0002).

DISCUSSION

We introduced covert HH observers at our hospital to determine whether differences in Hawthorne effect accounted for measured disparity between physician HH compliance, and to gain further insights into the barriers and enablers of physician HH compliance. We discovered that performance differences between physicians and nurses decreased when neither group was aware that HH was being measured, suggesting that healthcare professions are differentially affected by the Hawthorne effect. This difference may be explained by the continuity of nurses on the ward that makes them more aware of visitors like HH auditors,[7] compared with physicians who rotate periodically on the ward.

Although hospital auditors play a central role in HH education through in‐the‐moment feedback, use of these data to benchmark performance can lead to inappropriate inferences about HH compliance. Prior studies using automated HH surveillance have suggested that the magnitude of the Hawthorne effect varies based on baseline HH rates,[8] whereas our study suggests a differential Hawthorne effect between professions and clinical services. If we relied only on auditor data, we would have continued to believe that only physicians in our organization had poor HH compliance, and we would not be aware of the global nature of the HH problem.

Our results are similar to that of Pan et al., who used covert medical students to measure HH and found compliance of 44.1% compared with 94.1% by unit auditors.[2] Because their study involved an active feedback intervention, the differential in Hawthorne effect between professions could not be reliably assessed. However, they observed a progressive increase in nurse HH compliance using covert observation methods, suggesting improvement in HH performance independent of observer bias.[7]

Covert observation in our study also provided important insights regarding barriers and enablers of HH compliance. Self‐preservation behaviors were common among both nurses and physicians, as HH compliance was consistently higher after patient contact compared to before or when seeing patients who required additional precautions. This finding confirms that the perceived risk of transmission seems to be a powerful motivating factor for HH.[9] Larger groups of trainees were more likely to clean their hands, likely due to peer effects.[10] The strong impact of role modeling on HH was also noted as previously suggested in the literature,[3, 6] but our study captures the magnitude of this effect. Whether or not the attending physician cleaned their hands during rounds either positively or negatively influenced HH compliance of the rest of the physician team (80% when compliant vs 20% when noncompliant).

Our study has several important limitations. The differential Hawthorne effect seen at our center may not reflect other institutions that have numerous HH auditors or high staff turnover resulting in lower ability to recognize auditors. We cannot exclude the possibility of Hawthorne effect using covert methods that could have affected nurse and physician performance differently, but frequent rotation of the students helped maintain covertness of observations. Finally, due to the nature of the covert student observers, a longer observation time frame could not be sustained.

Our experience using covert HH auditors suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences regarding HH performance due to relative differences in Hawthorne effect. The answer to the question regarding whether physicians clean their hands appears to be that they do just as often as nurses, but that all healthcare workers have tremendous room for improvement. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees.

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

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

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

METHODS AND MATERIALS

RESULTS

DISCUSSION

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

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

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

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

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

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

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

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

METHODS AND MATERIALS

RESULTS

DISCUSSION

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

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

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

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

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

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

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

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

METHODS AND MATERIALS

RESULTS

DISCUSSION

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

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

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

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

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

Heart failure is a frequent cause of hospital admission in the United States, with an estimated cost of $31 billion dollars per year.[1] Discharging a patient with heart failure requires a multidisciplinary approach that includes anticipating a discharge date, scheduling follow‐up, reconciling medications, assessing home‐care or placement needs, and delivering patient education.[2, 3] Comprehensive transitional care interventions reduce readmissions and mortality.[2] Individually tailored and structured discharge plans decrease length of stay and readmissions.[3] The Centers for Medicare and Medicaid Services recently proposed that discharge planning begin within 24 hours of inpatient admissions,[4] despite inadequate data surrounding the optimal time to begin discharge planning.[3] In addition to enabling transitional care, identifying patients vulnerable to extended hospitalization aids in risk stratification, as prolonged length of stay is associated with increased risk of readmission and mortality.[5, 6]

Physicians are not able to accurately prognosticate whether patients will experience short‐term outcomes such as readmissions or mortality.[7, 8] Likewise, physicians do not predict length of stay accurately for heterogeneous patient populations,[9, 10, 11] even on the morning prior to anticipated discharge.[12] Prediction accuracy for patients admitted with heart failure, however, has not been adequately studied. The objectives of this study were to measure the accuracy of inpatient physicians' early predictions of length of stay for patients admitted with heart failure and to determine whether level of experience improved accuracy.

METHODS

In this prospective, observational study, we measured physicians' predictions of length of stay for patients admitted to a heart failure teaching service at an academic tertiary care hospital. Three resident/emntern teams rotate admitting responsibilities every 3 days, supervised by 1 attending cardiologist. Patients admitted overnight may be admitted independently by the on‐call resident without intern collaboration.

All physicians staffing our center's heart failure teaching service between August 1, 2013 and November 19, 2013 were recruited, and consecutively admitted adult patients were included. Patients were excluded if they did not have any cardiac diagnosis or if still admitted at study completion in February 2014. Deceased patients' time of death was counted as discharge.

Interns, residents, and attending cardiologists were interviewed independently within 24 hours of admission and asked to predict length of stay. Interns and residents were interviewed prior to rounds, and attendings thereafter. Electronic medical records were reviewed to determine date and time of admission and discharge, demographics, clinical variables, and discharge diagnoses.

The primary outcome was accuracy of predictions of length of stay stratified by level of experience. Based on prior pilot data, at 80% power and significance level () of 0.05, we estimated that predictions were needed on 100 patients to detect a 2‐day difference between actual and predicted length of stay.

Student t tests were used to compare the difference between predicted and actual length of stay for each level of training. Analysis of variance (ANOVA) was used to compare accuracy of prediction by training level. Generalized estimating equation (GEE) modeling was applied to compare predictions among interns, residents, and attending cardiologists, accounting for clustering by individual physician. GEE models were adjusted for study week in a sensitivity analysis to determine if predictions improved over time.

Analysis was performed using SAS 9.3 (SAS Institute Inc., Cary, NC) and R 2.14 (The R Foundation for Statistical Computing, Vienna, Austria). Institutional review board approval was granted, and physicians provided informed consent. All authors had access to primary data devoid of protected health information.

RESULTS

In total, 22 interns (<6 months experience), 25 residents (13 years experience), and 8 attending cardiologists (mean 19 9.7 years experience) were studied. Predictions were performed on 171 consecutively admitted patients. Five patients had noncardiac diagnoses and 1 patient remained admitted, leaving 165 patients for analysis. Predictions were made by all 3 physician levels on 98 patients. There were 67 patients with incomplete predictions as a result of 63 intern, 13 attending, and 4 resident predictions that were unobtainable. Absent intern data predominantly resulted from night shift admissions. Remaining missing data were due to time‐sensitive physician tasks that interfered with physician interviews.

Patient characteristics are described in Table 1. Physicians provided 415 predictions on 165 patients, 157 (95%) of whom survived to hospital discharge. Mean and median lengths of stay were 10.9 and 8 days (interquartile range [IQR], 4 to 13). Mean intern (N = 102), resident (N = 161), and attending (N = 152) predictions were 5.4 days (95% confidence interval [CI]: 4.6 to 6.2), 6.6 days (95% CI: 5.8 to 7.4) and 7.2 days (95% CI: 6.4 to 7.9), respectively. Median intern, resident, and attending predictions were 5 days (IQR, 3 to 7), 5 days (IQR, 3 to 7), and 6 days (IQR, 4 to 10). Mean differences between predicted and actual length of stay for interns, residents and attendings were 9 days (95% CI: 8.2 to 3.6), 4.3 days (95% C: 6.0 to 2.7), and 3.5 days (95% CI: 5.1 to 2.0). The mean difference between predicted and actual length of stay was statistically significant for all groups (P < 0.0001). Median intern, resident, and attending differences between predicted and actual were 2 days (IQR, 7 to 0), 2 days (IQR, 7 to 0), and 1 day (IQR, 5 to 1), respectively. Predictions correlated poorly with actual length of stay (R² = 0.11).

Ninety‐eight patients (59%) received predictions from physicians at all 3 experience levels. Mean and median lengths of stay were 11.3 days and 7.5 days (IQR, 4 to 13). Concordant with the entire cohort, median intern, resident, and attending predictions for these patients were 5 days (IQR, 3 to 7), 5 days (IQR, 3 to 7), and 6 days (IQR, 4 to 10), respectively. Differences between predicted and actual length of stay were statistically significant for all groups: the mean difference for interns, residents, and attendings was 5.8 days (95% CI: 8.2 to 3.4, P < 0.0001), 4.6 days (95% CI: 7.1 to 2.0, P = 0.0001), and 4.3 days (95% CI: 6.5 to 2.1, P = 0.0003), respectively (Figure 1).

Figure 1

There are differences among providers with improved prediction as level of experience increased, but this is not statistically significant as determined by ANOVA (p=0.64) or by GEE modeling to account for clustering of predictions by physician (P = 0.61). Analysis that adjusted for study week yielded similar results. Thus, experience did not improve accuracy.

DISCUSSION

We prospectively measured accuracy of physicians' length of stay predictions of heart failure patients and compared accuracy by experience level. All physicians underestimated length of stay, with average differences between 3.5 and 6 days. Most notably, level of experience did not improve accuracy. Although we anticipated that experience would improve prediction, our findings are not compatible with this hypothesis. Future studies of factors affecting length of stay predictions would help to better understand our findings.

Our results are consistent with small, single‐center studies of different patient and physician cohorts. Hulter Asberg found that internists at a hospital were unable to predict whether a patient would remain admitted 10 days or more, with poor interobserver reliability.[9] Mak et al. demonstrated that emergency physicians underestimated length of stay by an average of 2 days when predicting length of stay on a broad spectrum of patients in an emergency department.[10] Physician predictions of length of stay have been found to be inaccurate in a center's oncologic intensive care unit population.[11] Sullivan et al. found that academic general medicine physicians predicted discharge with 27% sensitivity the morning prior to next‐day discharge, which improved significantly to 67% by the afternoon, concluding that physicians can provide meaningful discharge predictions the afternoon prior to next‐day discharge.[12] By focusing on patients with heart failure, a major driver of hospitalization and readmission, and comparing providers by level of experience, we augment this existing body of work.

In addition to identifying patients at risk for readmission and mortality,[5, 6] accurate discharge prediction may improve safety of weekend discharges and patient satisfaction. Heart failure patients discharged on weekends receive less complete discharge instructions,[13] suffer higher mortality, and are readmitted more frequently than those discharged on weekdays.[14] Early and accurate predictions may enhance interventions targeting patients with anticipated weekend discharges. Furthermore, inadequate communication regarding anticipated discharge timing is a source of patient dissatisfaction,[15] and accurate prediction of discharge, if shared with patients, may improve patient satisfaction.

Limitations of our study include that it was a single‐center study at a large academic tertiary care hospital with predictions assessed on a teaching service. Severity of illness of this cohort may be a barrier to generalizability, and physicians may predict prognosis of healthier patients more accurately. We recorded predictions at the time of admission, and did not assess whether accuracy improved closer to discharge. We did not collect predictions from non‐physician team members. Sample size and absent data regarding the causes of prolonged hospitalization prohibited an analyses of variables associated with prediction inaccuracy.

CONCLUSIONS

Physicians do not accurately forecast heart failure patients' length of stay at the time of admission, and level of experience does not improve accuracy. Future studies are warranted to determine whether predictions closer to discharge, by an interdisciplinary team, or with assistance of risk‐prediction models are more accurate than physician predictions at admission, and whether early identification of patients at risk for prolonged hospitalization improves outcomes. Ultimately, early and accurate length of stay forecasts may improve risk stratification, patient satisfaction, and discharge planning, and reduce adverse outcomes related to at‐risk discharges.

Heart failure is a frequent cause of hospital admission in the United States, with an estimated cost of $31 billion dollars per year.[1] Discharging a patient with heart failure requires a multidisciplinary approach that includes anticipating a discharge date, scheduling follow‐up, reconciling medications, assessing home‐care or placement needs, and delivering patient education.[2, 3] Comprehensive transitional care interventions reduce readmissions and mortality.[2] Individually tailored and structured discharge plans decrease length of stay and readmissions.[3] The Centers for Medicare and Medicaid Services recently proposed that discharge planning begin within 24 hours of inpatient admissions,[4] despite inadequate data surrounding the optimal time to begin discharge planning.[3] In addition to enabling transitional care, identifying patients vulnerable to extended hospitalization aids in risk stratification, as prolonged length of stay is associated with increased risk of readmission and mortality.[5, 6]

Physicians are not able to accurately prognosticate whether patients will experience short‐term outcomes such as readmissions or mortality.[7, 8] Likewise, physicians do not predict length of stay accurately for heterogeneous patient populations,[9, 10, 11] even on the morning prior to anticipated discharge.[12] Prediction accuracy for patients admitted with heart failure, however, has not been adequately studied. The objectives of this study were to measure the accuracy of inpatient physicians' early predictions of length of stay for patients admitted with heart failure and to determine whether level of experience improved accuracy.

METHODS

In this prospective, observational study, we measured physicians' predictions of length of stay for patients admitted to a heart failure teaching service at an academic tertiary care hospital. Three resident/emntern teams rotate admitting responsibilities every 3 days, supervised by 1 attending cardiologist. Patients admitted overnight may be admitted independently by the on‐call resident without intern collaboration.

All physicians staffing our center's heart failure teaching service between August 1, 2013 and November 19, 2013 were recruited, and consecutively admitted adult patients were included. Patients were excluded if they did not have any cardiac diagnosis or if still admitted at study completion in February 2014. Deceased patients' time of death was counted as discharge.

Interns, residents, and attending cardiologists were interviewed independently within 24 hours of admission and asked to predict length of stay. Interns and residents were interviewed prior to rounds, and attendings thereafter. Electronic medical records were reviewed to determine date and time of admission and discharge, demographics, clinical variables, and discharge diagnoses.

The primary outcome was accuracy of predictions of length of stay stratified by level of experience. Based on prior pilot data, at 80% power and significance level () of 0.05, we estimated that predictions were needed on 100 patients to detect a 2‐day difference between actual and predicted length of stay.

Student t tests were used to compare the difference between predicted and actual length of stay for each level of training. Analysis of variance (ANOVA) was used to compare accuracy of prediction by training level. Generalized estimating equation (GEE) modeling was applied to compare predictions among interns, residents, and attending cardiologists, accounting for clustering by individual physician. GEE models were adjusted for study week in a sensitivity analysis to determine if predictions improved over time.

Analysis was performed using SAS 9.3 (SAS Institute Inc., Cary, NC) and R 2.14 (The R Foundation for Statistical Computing, Vienna, Austria). Institutional review board approval was granted, and physicians provided informed consent. All authors had access to primary data devoid of protected health information.

RESULTS

In total, 22 interns (<6 months experience), 25 residents (13 years experience), and 8 attending cardiologists (mean 19 9.7 years experience) were studied. Predictions were performed on 171 consecutively admitted patients. Five patients had noncardiac diagnoses and 1 patient remained admitted, leaving 165 patients for analysis. Predictions were made by all 3 physician levels on 98 patients. There were 67 patients with incomplete predictions as a result of 63 intern, 13 attending, and 4 resident predictions that were unobtainable. Absent intern data predominantly resulted from night shift admissions. Remaining missing data were due to time‐sensitive physician tasks that interfered with physician interviews.

Patient characteristics are described in Table 1. Physicians provided 415 predictions on 165 patients, 157 (95%) of whom survived to hospital discharge. Mean and median lengths of stay were 10.9 and 8 days (interquartile range [IQR], 4 to 13). Mean intern (N = 102), resident (N = 161), and attending (N = 152) predictions were 5.4 days (95% confidence interval [CI]: 4.6 to 6.2), 6.6 days (95% CI: 5.8 to 7.4) and 7.2 days (95% CI: 6.4 to 7.9), respectively. Median intern, resident, and attending predictions were 5 days (IQR, 3 to 7), 5 days (IQR, 3 to 7), and 6 days (IQR, 4 to 10). Mean differences between predicted and actual length of stay for interns, residents and attendings were 9 days (95% CI: 8.2 to 3.6), 4.3 days (95% C: 6.0 to 2.7), and 3.5 days (95% CI: 5.1 to 2.0). The mean difference between predicted and actual length of stay was statistically significant for all groups (P < 0.0001). Median intern, resident, and attending differences between predicted and actual were 2 days (IQR, 7 to 0), 2 days (IQR, 7 to 0), and 1 day (IQR, 5 to 1), respectively. Predictions correlated poorly with actual length of stay (R² = 0.11).

Ninety‐eight patients (59%) received predictions from physicians at all 3 experience levels. Mean and median lengths of stay were 11.3 days and 7.5 days (IQR, 4 to 13). Concordant with the entire cohort, median intern, resident, and attending predictions for these patients were 5 days (IQR, 3 to 7), 5 days (IQR, 3 to 7), and 6 days (IQR, 4 to 10), respectively. Differences between predicted and actual length of stay were statistically significant for all groups: the mean difference for interns, residents, and attendings was 5.8 days (95% CI: 8.2 to 3.4, P < 0.0001), 4.6 days (95% CI: 7.1 to 2.0, P = 0.0001), and 4.3 days (95% CI: 6.5 to 2.1, P = 0.0003), respectively (Figure 1).

Figure 1

There are differences among providers with improved prediction as level of experience increased, but this is not statistically significant as determined by ANOVA (p=0.64) or by GEE modeling to account for clustering of predictions by physician (P = 0.61). Analysis that adjusted for study week yielded similar results. Thus, experience did not improve accuracy.

DISCUSSION

We prospectively measured accuracy of physicians' length of stay predictions of heart failure patients and compared accuracy by experience level. All physicians underestimated length of stay, with average differences between 3.5 and 6 days. Most notably, level of experience did not improve accuracy. Although we anticipated that experience would improve prediction, our findings are not compatible with this hypothesis. Future studies of factors affecting length of stay predictions would help to better understand our findings.

Our results are consistent with small, single‐center studies of different patient and physician cohorts. Hulter Asberg found that internists at a hospital were unable to predict whether a patient would remain admitted 10 days or more, with poor interobserver reliability.[9] Mak et al. demonstrated that emergency physicians underestimated length of stay by an average of 2 days when predicting length of stay on a broad spectrum of patients in an emergency department.[10] Physician predictions of length of stay have been found to be inaccurate in a center's oncologic intensive care unit population.[11] Sullivan et al. found that academic general medicine physicians predicted discharge with 27% sensitivity the morning prior to next‐day discharge, which improved significantly to 67% by the afternoon, concluding that physicians can provide meaningful discharge predictions the afternoon prior to next‐day discharge.[12] By focusing on patients with heart failure, a major driver of hospitalization and readmission, and comparing providers by level of experience, we augment this existing body of work.

In addition to identifying patients at risk for readmission and mortality,[5, 6] accurate discharge prediction may improve safety of weekend discharges and patient satisfaction. Heart failure patients discharged on weekends receive less complete discharge instructions,[13] suffer higher mortality, and are readmitted more frequently than those discharged on weekdays.[14] Early and accurate predictions may enhance interventions targeting patients with anticipated weekend discharges. Furthermore, inadequate communication regarding anticipated discharge timing is a source of patient dissatisfaction,[15] and accurate prediction of discharge, if shared with patients, may improve patient satisfaction.

Limitations of our study include that it was a single‐center study at a large academic tertiary care hospital with predictions assessed on a teaching service. Severity of illness of this cohort may be a barrier to generalizability, and physicians may predict prognosis of healthier patients more accurately. We recorded predictions at the time of admission, and did not assess whether accuracy improved closer to discharge. We did not collect predictions from non‐physician team members. Sample size and absent data regarding the causes of prolonged hospitalization prohibited an analyses of variables associated with prediction inaccuracy.

CONCLUSIONS

Physicians do not accurately forecast heart failure patients' length of stay at the time of admission, and level of experience does not improve accuracy. Future studies are warranted to determine whether predictions closer to discharge, by an interdisciplinary team, or with assistance of risk‐prediction models are more accurate than physician predictions at admission, and whether early identification of patients at risk for prolonged hospitalization improves outcomes. Ultimately, early and accurate length of stay forecasts may improve risk stratification, patient satisfaction, and discharge planning, and reduce adverse outcomes related to at‐risk discharges.

Heart failure is a frequent cause of hospital admission in the United States, with an estimated cost of $31 billion dollars per year.[1] Discharging a patient with heart failure requires a multidisciplinary approach that includes anticipating a discharge date, scheduling follow‐up, reconciling medications, assessing home‐care or placement needs, and delivering patient education.[2, 3] Comprehensive transitional care interventions reduce readmissions and mortality.[2] Individually tailored and structured discharge plans decrease length of stay and readmissions.[3] The Centers for Medicare and Medicaid Services recently proposed that discharge planning begin within 24 hours of inpatient admissions,[4] despite inadequate data surrounding the optimal time to begin discharge planning.[3] In addition to enabling transitional care, identifying patients vulnerable to extended hospitalization aids in risk stratification, as prolonged length of stay is associated with increased risk of readmission and mortality.[5, 6]

Physicians are not able to accurately prognosticate whether patients will experience short‐term outcomes such as readmissions or mortality.[7, 8] Likewise, physicians do not predict length of stay accurately for heterogeneous patient populations,[9, 10, 11] even on the morning prior to anticipated discharge.[12] Prediction accuracy for patients admitted with heart failure, however, has not been adequately studied. The objectives of this study were to measure the accuracy of inpatient physicians' early predictions of length of stay for patients admitted with heart failure and to determine whether level of experience improved accuracy.

METHODS

In this prospective, observational study, we measured physicians' predictions of length of stay for patients admitted to a heart failure teaching service at an academic tertiary care hospital. Three resident/emntern teams rotate admitting responsibilities every 3 days, supervised by 1 attending cardiologist. Patients admitted overnight may be admitted independently by the on‐call resident without intern collaboration.

All physicians staffing our center's heart failure teaching service between August 1, 2013 and November 19, 2013 were recruited, and consecutively admitted adult patients were included. Patients were excluded if they did not have any cardiac diagnosis or if still admitted at study completion in February 2014. Deceased patients' time of death was counted as discharge.

Interns, residents, and attending cardiologists were interviewed independently within 24 hours of admission and asked to predict length of stay. Interns and residents were interviewed prior to rounds, and attendings thereafter. Electronic medical records were reviewed to determine date and time of admission and discharge, demographics, clinical variables, and discharge diagnoses.

The primary outcome was accuracy of predictions of length of stay stratified by level of experience. Based on prior pilot data, at 80% power and significance level () of 0.05, we estimated that predictions were needed on 100 patients to detect a 2‐day difference between actual and predicted length of stay.

Student t tests were used to compare the difference between predicted and actual length of stay for each level of training. Analysis of variance (ANOVA) was used to compare accuracy of prediction by training level. Generalized estimating equation (GEE) modeling was applied to compare predictions among interns, residents, and attending cardiologists, accounting for clustering by individual physician. GEE models were adjusted for study week in a sensitivity analysis to determine if predictions improved over time.

Analysis was performed using SAS 9.3 (SAS Institute Inc., Cary, NC) and R 2.14 (The R Foundation for Statistical Computing, Vienna, Austria). Institutional review board approval was granted, and physicians provided informed consent. All authors had access to primary data devoid of protected health information.

RESULTS

In total, 22 interns (<6 months experience), 25 residents (13 years experience), and 8 attending cardiologists (mean 19 9.7 years experience) were studied. Predictions were performed on 171 consecutively admitted patients. Five patients had noncardiac diagnoses and 1 patient remained admitted, leaving 165 patients for analysis. Predictions were made by all 3 physician levels on 98 patients. There were 67 patients with incomplete predictions as a result of 63 intern, 13 attending, and 4 resident predictions that were unobtainable. Absent intern data predominantly resulted from night shift admissions. Remaining missing data were due to time‐sensitive physician tasks that interfered with physician interviews.

Patient characteristics are described in Table 1. Physicians provided 415 predictions on 165 patients, 157 (95%) of whom survived to hospital discharge. Mean and median lengths of stay were 10.9 and 8 days (interquartile range [IQR], 4 to 13). Mean intern (N = 102), resident (N = 161), and attending (N = 152) predictions were 5.4 days (95% confidence interval [CI]: 4.6 to 6.2), 6.6 days (95% CI: 5.8 to 7.4) and 7.2 days (95% CI: 6.4 to 7.9), respectively. Median intern, resident, and attending predictions were 5 days (IQR, 3 to 7), 5 days (IQR, 3 to 7), and 6 days (IQR, 4 to 10). Mean differences between predicted and actual length of stay for interns, residents and attendings were 9 days (95% CI: 8.2 to 3.6), 4.3 days (95% C: 6.0 to 2.7), and 3.5 days (95% CI: 5.1 to 2.0). The mean difference between predicted and actual length of stay was statistically significant for all groups (P < 0.0001). Median intern, resident, and attending differences between predicted and actual were 2 days (IQR, 7 to 0), 2 days (IQR, 7 to 0), and 1 day (IQR, 5 to 1), respectively. Predictions correlated poorly with actual length of stay (R² = 0.11).