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Barriers to Early Hospital Discharge: A Cross-Sectional Study at Five Academic Hospitals
Hospital discharges frequently occur in the afternoon or evening hours.1-5 Late discharges can adversely affect patient flow throughout the hospital,3,6-9 which, in turn, can result in delays in care,10-16 more medication errors,17 increased mortality,18-20 longer lengths of stay,20-22 higher costs,23 and lower patient satisfaction.24
Various interventions have been employed in the attempts to find ways of moving discharge times to earlier in the day, including preparing the discharge paperwork and medications the previous night,25 using checklists,1,25 team huddles,2 providing real-time feedback to unit staff,1 and employing multidisciplinary teamwork.1,2,6,25,26
The purpose of this study was to identify and determine the relative frequency of barriers to writing discharge orders in the hopes of identifying issues that might be addressed by targeted interventions. We also assessed the effects of daily team census, patients being on teaching versus nonteaching services, and how daily rounds were structured at the time that the discharge orders were written.
METHODS
Study Design, Setting, and Participants
We conducted a prospective, cross-sectional survey of house-staff and attending physicians on general medicine teaching and nonteaching services from November 13, 2014, through May 31, 2016. The study was conducted at the following five hospitals: Denver Health Medical Center (DHMC) and Presbyterian/Saint Luke’s Medical Center (PSL) in Denver, Colorado; Ronald Reagan University (UCLA) and Los Angeles County/University of Southern California Medical Center (LAC+USC) in Los Angeles, California; and Harborview Medical Center (HMC) in Seattle, Washington. The study was approved by the Colorado Multi-Institutional Review Board as well as by the review boards of the other participating sites.
Data Collection
The results of the focus groups composed of attending physicians at DHMC were used to develop our initial data collection template. Additional sites joining the study provided feedback, leading to modifications (Appendix 1).
Physicians were surveyed at three different time points on study days that were selected according to the convenience of the investigators. The sampling occurred only on weekdays and was done based on the investigators’ availability. Investigators would attempt to survey as many teams as they were able to but, secondary to feasibility, not all teams could be surveyed on study days. The specific time points varied as a function of physician workflows but were standardized as much as possible to occur in the early morning, around noon, and midafternoon on weekdays. Physicians were contacted either in person or by telephone for verbal consent prior to administering the first survey. All general medicine teams were eligible. For teaching teams, the order of contact was resident, intern, and then attending based on which physician was available at the time of the survey and on which member of the team was thought to know the patients the best. For the nonteaching services, the attending physicians were contacted.
During the initial survey, the investigators assessed the provider role (ie, attending or housestaff), whether the service was a teaching or a nonteaching service, and the starting patient census on that service primarily based on interviewing the provider of record for the team and looking at team census lists. Physicians were asked about their rounding style (ie, sickest patients first, patients likely to be discharged first, room-by-room, most recently admitted patients first, patients on the team the longest, or other) and then to identify all patients they thought would be definite discharges sometime during the day of the survey. Definite discharges were defined as patients whom the provider thought were either currently ready for discharge or who had only minor barriers that, if unresolved, would not prevent same-day discharge. They were asked if the discharge order had been entered and, if not, what was preventing them from doing so, if the discharge could in their opinion have occurred the day prior and, if so, why this did not occur. We also obtained the date and time of the admission and discharge orders, the actual discharge time, as well as the length of stay either through chart review (majority of sites) or from data warehouses (Denver Health and Presbyterian St. Lukes had length of stay data retrieved from their data warehouse).
Physicians were also asked to identify all patients whom they thought might possibly be discharged that day. Possible discharges were defined as patients with barriers to discharge that, if unresolved, would prevent same-day discharge. For each of these, the physicians were asked to list whatever issues needed to be resolved prior to placing the discharge order (Appendix 1).
The second survey was administered late morning on the same day, typically between 11
The third survey was administered midafternoon, typically around 3 PM similar to the first two surveys, with the exception that the third survey did not attempt to identify new definite or possible discharges.
Sample Size
We stopped collecting data after obtaining a convenience sample of 5% of total discharges at each study site or on the study end date, which was May 31, 2016, whichever came first.
Data Analysis
Data were collected and managed using a secure, web-based application electronic data capture tool (REDCap), hosted at Denver Health. REDCap (Research Electronic Data Capture, Nashville, Tennessee) is designed to support data collection for research studies.27 Data were then analyzed using SAS Enterprise Guide 5.1 (SAS Institute, Inc., Cary, North Carolina). All data entered into REDCap were reviewed by the principal investigator to ensure that data were not missing, and when there were missing data, a query was sent to verify if the data were retrievable. If retrievable, then the data would be entered. The volume of missing data that remained is described in our results.
Continuous variables were described using means and standard deviations (SD) or medians and interquartile ranges (IQR) based on tests of normality. Differences in the time that the discharge orders were placed in the electronic medical record according to morning patient census, teaching versus nonteaching service, and rounding style were compared using the Wilcoxon rank sum test. Linear regression was used to evaluate the effect of patient census on discharge order time. P < .05 was considered as significant.
RESULTS
We conducted 1,584 patient evaluations through surveys of 254 physicians over 156 days. Given surveys coincided with the existing work we had full participation (ie, 100% participation) and no dropout during the study days. Median (IQR) survey time points were 8:30
The characteristics of the five hospitals participating in the study, the patients’ final discharge status, the types of physicians surveyed, the services on which they were working, the rounding styles employed, and the median starting daily census are summarized in Table 1. The majority of the physicians surveyed were housestaff working on teaching services, and only a small minority structured rounds such that patients ready for discharge were seen first.
Over the course of the three surveys, 949 patients were identified as being definite discharges at any time point, and the large majority of these (863, 91%) were discharged on the day of the survey. The median (IQR) time that the discharge orders were written was 11:50
During the initial morning survey, 314 patients were identified as being definite discharges for that day (representing approximately 6% of the total number of patients being cared for, or 33% of the patients identified as definite discharges throughout the day). Of these, the physicians thought that 44 (<1% of the total number of patients being cared for on the services) could have been discharged on the previous day. The most frequent reasons cited for why these patients were not discharged on the previous day were “Patient did not want to leave” (n = 15, 34%), “Too late in the day” (n = 10, 23%), and “No ride” (n = 9, 20%). The remaining 10 patients (23%) had a variety of reasons related to system or social issues (ie, shelter not available, miscommunication).
At the morning time point, the most common barriers to discharge identified were that the physicians had not finished rounding on their team of patients and that the housestaff needed to staff their patients with their attending. At noon, caring for other patients and tending to the discharge processes were most commonly cited, and in the afternoon, the most common barriers were that the physicians were in the process of completing the discharge paperwork for those patients or were discharging other patients (Table 2). When comparing barriers on teaching to nonteaching teams, a higher proportion of teaching teams were still rounding on all patients and were working on discharge paperwork at the second survey. Barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied (data not shown).
The physicians identified 1,237 patients at any time point as being possible discharges during the day of the survey and these had a mean (±SD) of 1.3 (±0.5) barriers cited for why these patients were possible rather than definite discharges. The most common were that clinical improvement was needed, one or more pending issues related to their care needed to be resolved, and/or awaiting pending test results. The need to see clinical improvement generally decreased throughout the day as did the need to staff patients with an attending physician, but barriers related to consultant recommendations or completing procedures increased (Table 3). Of the 1,237 patients ever identified as possible discharges, 594 (48%) became a definite discharge by the third call and 444 (36%) became a no discharge as their final status. As with definite discharges, barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied.
Among the 949 and 1,237 patients who were ever identified as definite or possible discharges, respectively, at any time point during the study day, 28 (3%) and 444 (36%), respectively, had their discharge status changed to no discharge, most commonly because their clinical condition either worsened or expected improvements did not occur or that barriers pertaining to social work, physical therapy, or occupational therapy were not resolved.
The median time that the discharge orders were entered into the electronic medical record was 43 minutes earlier if patients were on teams with a lower versus a higher starting census (P = .0003), 48 minutes earlier if they were seen by physicians whose rounding style was to see patients first who potentially could be discharged (P = .0026), and 58 minutes earlier if they were on nonteaching versus teaching services (P < .0001; Table 4). For every one-person increase in census, the discharge order time increased by 6 minutes (β = 5.6, SE = 1.6, P = .0003).
DISCUSSION
The important findings of this study are that (1) the large majority of issues thought to delay discharging patients identified as definite discharges were related to physicians caring for other patients on their team, (2) although 91% of patients ever identified as being definite discharges were discharged on the day of the survey, only 48% of those identified as possible discharges became definite discharges by the afternoon time point, largely because the anticipated clinical improvement did not occur or care being provided by ancillary services had not been completed, and (3) discharge orders on patients identified as definite discharges were written on average 50 minutes earlier by physicians on teams with a smaller starting patient census, on nonteaching services, or when the rounding style was to see patients ready for discharges first.
Previous research has reported that physician-perceived barriers to discharge were extrinsic to providers and even extrinsic to the hospital setting (eg, awaiting subacute nursing placement and transportation).28,29 However, many of the barriers that we identified were related directly to the providers’ workload and rounding styles and whether the patients were on teaching versus nonteaching services. We also found that delays in the ability of hospital services to complete care also contributed to delayed discharges.
Our observational data suggest that delays resulting from caring for other patients might be reduced by changing rounding styles such that patients ready for discharge are seen first and are discharged prior to seeing other patients on the team, as previously reported by Beck et al.30 Intuitively, this would seem to be a straightforward way of freeing up beds earlier in the day, but such a change will, of necessity, lead to delaying care for other patients, which, in turn, could increase their length of stays. Durvasula et al. suggested that discharges could be moved to earlier in the day by completing orders and paperwork the day prior to discharge.25 Such an approach might be effective on an Obstetrical or elective Orthopedic service on which patients predictably are hospitalized for a fixed number of days (or even hours) but may be less relevant to patients on internal medicine services where lengths of stay are less predictable. Interventions to improve discharge times have resulted in earlier discharge times in some studies,2,4 but the overall length of stay either did not decrease25 or increased31 in others. Werthheimer et al.1 did find earlier discharge times, but other interventions also occurred during the study period (eg, extending social work services to include weekends).1,32
We found that discharge times were approximately 50 minutes earlier on teams with a smaller starting census, on nonteaching compared with teaching services, or when the attending’s rounding style was to see patients ready for discharges first. Although 50 minutes may seem like a small change in discharge time, Khanna et al.33 found that when discharges occur even 1 hour earlier, hospital overcrowding is reduced. To have a lower team census would require having more teams and more providers to staff these teams, raising cost-effectiveness concerns. Moving to more nonteaching services could represent a conflict with respect to one of the missions of teaching hospitals and raises a cost-benefit issue as several teaching hospitals receive substantial funding in support of their teaching activities and housestaff would have to be replaced with more expensive providers.
Delays attributable to ancillary services indicate imbalances between demand and availability of these services. Inappropriate demand and inefficiencies could be reduced by systems redesign, but in at least some instances, additional resources will be needed to add staff, increase space, or add additional equipment.
Our study has several limitations. First, we surveyed only physicians working in university-affiliated hospitals, and three of these were public safety-net hospitals. Accordingly, our results may not be generalizable to different patient populations. Second, we surveyed only physicians, and Minichiello et al.29 found that barriers to discharge perceived by physicians were different from those of other staff. Third, our data were observational and were collected only on weekdays. Fourth, we did not differentiate interns from residents, and thus, potentially the level of training could have affected these results. Similarly, the decision for a “possible” and a “definite” discharge is likely dependent on the knowledge base of the participant, such that less experienced participants may have had differing perspectives than someone with more experience. Fifth, the sites did vary based on the infrastructure and support but also had several similarities. All sites had social work and case management involved in care, although at some sites, they were assigned according to team and at others according to geographic location. Similarly, rounding times varied. Most of the services surveyed did not utilize advanced practice providers (the exception was the nonteaching services at Denver Health, and their presence was variable). These differences in staffing models could also have affected these results.
Our study also has a number of strengths. First, we assessed the barriers at five different hospitals. Second, we collected real-time data related to specific barriers at multiple time points throughout the day, allowing us to assess the dynamic nature of identifying patients as being ready or nearly ready for discharge. Third, we assessed the perceptions of barriers to discharge from physicians working on teaching as well as nonteaching services and from physicians utilizing a variety of rounding styles. Fourth, we had a very high participation rate (100%), probably due to the fact that our study was strategically aligned with participants’ daily work activities.
In conclusion, we found two distinct categories of issues that physicians perceived as most commonly delaying writing discharge orders on their patients. The first pertained to patients thought to definitely be ready for discharge and was related to the physicians having to care for other patients on their team. The second pertained to patients identified as possibly ready for discharge and was related to the need for care to be completed by a variety of ancillary services. Addressing each of these barriers would require different interventions and a need to weigh the potential improvements that could be achieved against the increased costs and/or delays in care for other patients that may result.
Disclosures
The authors report no conflicts of interest relevant to this work.
1. Wertheimer B, Jacobs RE, Bailey M, et al. Discharge before noon: an achievable hospital goal. J Hosp Med. 2014;9(4):210-214. doi: 10.1002/jhm.2154. PubMed
2. Kane M, Weinacker A, Arthofer R, et al. A multidisciplinary initiative to increase inpatient discharges before noon. J Nurs Adm. 2016;46(12):630-635. doi: 10.1097/NNA.0000000000000418. PubMed
3. Khanna S, Sier D, Boyle J, Zeitz K. Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emerg Med Australas. 2016;28(2):164-170. doi: 10.1111/1742-6723.12543. PubMed
4. Kravet SJ, Levine RB, Rubin HR, Wright SM. Discharging patients earlier in the day: a concept worth evaluating. Health Care Manag (Frederick). 2007;26:142-146. doi: 10.1097/01.HCM.0000268617.33491.60. PubMed
5. Khanna S, Boyle J, Good N, Lind J. Impact of admission and discharge peak times on hospital overcrowding. Stud Health Technol Inform. 2011;168:82-88. doi: 10.3233/978-1-60750-791-8-82. PubMed
6. McGowan JE, Truwit JD, Cipriano P, et al. Operating room efficiency and hospital capacity: factors affecting operating room use during maximum hospital census. J Am Coll Surg. 2007;204(5):865-871; discussion 71-72. doi: 10.1016/j.jamcollsurg.2007.01.052 PubMed
7. Khanna S, Boyle J, Good N, Lind J. Early discharge and its effect on ED length of stay and access block. Stud Health Technol Inform. 2012;178:92-98. doi: 10.3233/978-1-61499-078-9-92 PubMed
8. Powell ES, Khare RK, Venkatesh AK, Van Roo BD, Adams JG, Reinhardt G. The relationship between inpatient discharge timing and emergency department boarding. J Emerg Med. 2012;42(2):186-196. doi: 10.1016/j.jemermed.2010.06.028. PubMed
9. Wertheimer B, Jacobs RE, Iturrate E, Bailey M, Hochman K. Discharge before noon: Effect on throughput and sustainability. J Hosp Med. 2015;10(10):664-669. doi: 10.1002/jhm.2412. PubMed
10. Sikka R, Mehta S, Kaucky C, Kulstad EB. ED crowding is associated with an increased time to pneumonia treatment. Am J Emerg Med. 2010;28(7):809-812. doi: 10.1016/j.ajem.2009.06.023. PubMed
11. Coil CJ, Flood JD, Belyeu BM, Young P, Kaji AH, Lewis RJ. The effect of emergency department boarding on order completion. Ann Emerg Med. 2016;67:730-736 e2. doi: 10.1016/j.annemergmed.2015.09.018. PubMed
12. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. doi: 10.1016/j.ajem.2017.01.061. PubMed
13. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med. 2007;50(5):510-516. doi: 10.1016/j.annemergmed.2007.07.021. PubMed
14. Hwang U, Richardson L, Livote E, Harris B, Spencer N, Sean Morrison R. Emergency department crowding and decreased quality of pain care. Acad Emerg Med. 2008;15:1248-1255. doi: 10.1111/j.1553-2712.2008.00267.x. PubMed
15. Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med. 2009;16:603-608. doi: 10.1111/j.1553-2712.2009.00441.x. PubMed
16. Pines JM, Shofer FS, Isserman JA, Abbuhl SB, Mills AM. The effect of emergency department crowding on analgesia in patients with back pain in two hospitals. Acad Emerg Med. 2010;17(3):276-283. doi: 10.1111/j.1553-2712.2009.00676.x. PubMed
17. Kulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am J Emerg Med. 2010;28:304-309. doi: 10.1016/j.ajem.2008.12.014. PubMed
18. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006;184(5):213-216. PubMed
19. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126-136. doi: 10.1016/j.annemergmed.2008.03.014. PubMed
20. Singer AJ, Thode HC, Jr., Viccellio P, Pines JM. The association between length of emergency department boarding and mortality. Acad Emerg Med. 2011;18(12):1324-1329. doi: 10.1111/j.1553-2712.2011.01236.x. PubMed
21. White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DF. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med. 2013;44(1):230-235. doi: 10.1016/j.jemermed.2012.05.007. PubMed
22. Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127-133. doi: 10.1197/aemj.10.2.127. PubMed
23. Foley M, Kifaieh N, Mallon WK. Financial impact of emergency department crowding. West J Emerg Med. 2011;12(2):192-197. PubMed
24. Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM. The effect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med. 2008;15(9):825-831. doi: 10.1111/j.1553-2712.2008.00200.x. PubMed
25. Durvasula R, Kayihan A, Del Bene S, et al. A multidisciplinary care pathway significantly increases the number of early morning discharges in a large academic medical center. Qual Manag Health Care. 2015;24:45-51. doi: 10.1097/QMH.0000000000000049. PubMed
26. Cho HJ, Desai N, Florendo A, et al. E-DIP: Early Discharge Project. A Model for Throughput and Early Discharge for 1-Day Admissions. BMJ Qual Improv Rep. 2016;5(1): pii: u210035.w4128. doi: 10.1136/bmjquality.u210035.w4128. PubMed
27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010. PubMed
28. Patel H, Fang MC, Mourad M, et al. Hospitalist and internal medicine leaders’ perspectives of early discharge challenges at academic medical centers. J Hosp Med. 2018;13(6):388-391. doi: 10.12788/jhm.2885. PubMed
29. Minichiello TM, Auerbach AD, Wachter RM. Caregiver perceptions of the reasons for delayed hospital discharge. Eff Clin Pract. 2001;4(6):250-255. PubMed
30. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV. Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hosp Pract (1995). 2016;44(5):252-259. doi: 10.1080/21548331.2016.1254559. PubMed
31. Rajkomar A, Valencia V, Novelero M, Mourad M, Auerbach A. The association between discharge before noon and length of stay in medical and surgical patients. J Hosp Med. 2016;11(12):859-861. doi: 10.1002/jhm.2529. PubMed
32. Shine D. Discharge before noon: an urban legend. Am J Med. 2015;128(5):445-446. doi: 10.1016/j.amjmed.2014.12.011. PubMed
33. Khanna S, Boyle J, Good N, Lind J. Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24(5):510-517. doi: 10.1111/j.1742-6723.2012.01587.x. PubMed
Hospital discharges frequently occur in the afternoon or evening hours.1-5 Late discharges can adversely affect patient flow throughout the hospital,3,6-9 which, in turn, can result in delays in care,10-16 more medication errors,17 increased mortality,18-20 longer lengths of stay,20-22 higher costs,23 and lower patient satisfaction.24
Various interventions have been employed in the attempts to find ways of moving discharge times to earlier in the day, including preparing the discharge paperwork and medications the previous night,25 using checklists,1,25 team huddles,2 providing real-time feedback to unit staff,1 and employing multidisciplinary teamwork.1,2,6,25,26
The purpose of this study was to identify and determine the relative frequency of barriers to writing discharge orders in the hopes of identifying issues that might be addressed by targeted interventions. We also assessed the effects of daily team census, patients being on teaching versus nonteaching services, and how daily rounds were structured at the time that the discharge orders were written.
METHODS
Study Design, Setting, and Participants
We conducted a prospective, cross-sectional survey of house-staff and attending physicians on general medicine teaching and nonteaching services from November 13, 2014, through May 31, 2016. The study was conducted at the following five hospitals: Denver Health Medical Center (DHMC) and Presbyterian/Saint Luke’s Medical Center (PSL) in Denver, Colorado; Ronald Reagan University (UCLA) and Los Angeles County/University of Southern California Medical Center (LAC+USC) in Los Angeles, California; and Harborview Medical Center (HMC) in Seattle, Washington. The study was approved by the Colorado Multi-Institutional Review Board as well as by the review boards of the other participating sites.
Data Collection
The results of the focus groups composed of attending physicians at DHMC were used to develop our initial data collection template. Additional sites joining the study provided feedback, leading to modifications (Appendix 1).
Physicians were surveyed at three different time points on study days that were selected according to the convenience of the investigators. The sampling occurred only on weekdays and was done based on the investigators’ availability. Investigators would attempt to survey as many teams as they were able to but, secondary to feasibility, not all teams could be surveyed on study days. The specific time points varied as a function of physician workflows but were standardized as much as possible to occur in the early morning, around noon, and midafternoon on weekdays. Physicians were contacted either in person or by telephone for verbal consent prior to administering the first survey. All general medicine teams were eligible. For teaching teams, the order of contact was resident, intern, and then attending based on which physician was available at the time of the survey and on which member of the team was thought to know the patients the best. For the nonteaching services, the attending physicians were contacted.
During the initial survey, the investigators assessed the provider role (ie, attending or housestaff), whether the service was a teaching or a nonteaching service, and the starting patient census on that service primarily based on interviewing the provider of record for the team and looking at team census lists. Physicians were asked about their rounding style (ie, sickest patients first, patients likely to be discharged first, room-by-room, most recently admitted patients first, patients on the team the longest, or other) and then to identify all patients they thought would be definite discharges sometime during the day of the survey. Definite discharges were defined as patients whom the provider thought were either currently ready for discharge or who had only minor barriers that, if unresolved, would not prevent same-day discharge. They were asked if the discharge order had been entered and, if not, what was preventing them from doing so, if the discharge could in their opinion have occurred the day prior and, if so, why this did not occur. We also obtained the date and time of the admission and discharge orders, the actual discharge time, as well as the length of stay either through chart review (majority of sites) or from data warehouses (Denver Health and Presbyterian St. Lukes had length of stay data retrieved from their data warehouse).
Physicians were also asked to identify all patients whom they thought might possibly be discharged that day. Possible discharges were defined as patients with barriers to discharge that, if unresolved, would prevent same-day discharge. For each of these, the physicians were asked to list whatever issues needed to be resolved prior to placing the discharge order (Appendix 1).
The second survey was administered late morning on the same day, typically between 11
The third survey was administered midafternoon, typically around 3 PM similar to the first two surveys, with the exception that the third survey did not attempt to identify new definite or possible discharges.
Sample Size
We stopped collecting data after obtaining a convenience sample of 5% of total discharges at each study site or on the study end date, which was May 31, 2016, whichever came first.
Data Analysis
Data were collected and managed using a secure, web-based application electronic data capture tool (REDCap), hosted at Denver Health. REDCap (Research Electronic Data Capture, Nashville, Tennessee) is designed to support data collection for research studies.27 Data were then analyzed using SAS Enterprise Guide 5.1 (SAS Institute, Inc., Cary, North Carolina). All data entered into REDCap were reviewed by the principal investigator to ensure that data were not missing, and when there were missing data, a query was sent to verify if the data were retrievable. If retrievable, then the data would be entered. The volume of missing data that remained is described in our results.
Continuous variables were described using means and standard deviations (SD) or medians and interquartile ranges (IQR) based on tests of normality. Differences in the time that the discharge orders were placed in the electronic medical record according to morning patient census, teaching versus nonteaching service, and rounding style were compared using the Wilcoxon rank sum test. Linear regression was used to evaluate the effect of patient census on discharge order time. P < .05 was considered as significant.
RESULTS
We conducted 1,584 patient evaluations through surveys of 254 physicians over 156 days. Given surveys coincided with the existing work we had full participation (ie, 100% participation) and no dropout during the study days. Median (IQR) survey time points were 8:30
The characteristics of the five hospitals participating in the study, the patients’ final discharge status, the types of physicians surveyed, the services on which they were working, the rounding styles employed, and the median starting daily census are summarized in Table 1. The majority of the physicians surveyed were housestaff working on teaching services, and only a small minority structured rounds such that patients ready for discharge were seen first.
Over the course of the three surveys, 949 patients were identified as being definite discharges at any time point, and the large majority of these (863, 91%) were discharged on the day of the survey. The median (IQR) time that the discharge orders were written was 11:50
During the initial morning survey, 314 patients were identified as being definite discharges for that day (representing approximately 6% of the total number of patients being cared for, or 33% of the patients identified as definite discharges throughout the day). Of these, the physicians thought that 44 (<1% of the total number of patients being cared for on the services) could have been discharged on the previous day. The most frequent reasons cited for why these patients were not discharged on the previous day were “Patient did not want to leave” (n = 15, 34%), “Too late in the day” (n = 10, 23%), and “No ride” (n = 9, 20%). The remaining 10 patients (23%) had a variety of reasons related to system or social issues (ie, shelter not available, miscommunication).
At the morning time point, the most common barriers to discharge identified were that the physicians had not finished rounding on their team of patients and that the housestaff needed to staff their patients with their attending. At noon, caring for other patients and tending to the discharge processes were most commonly cited, and in the afternoon, the most common barriers were that the physicians were in the process of completing the discharge paperwork for those patients or were discharging other patients (Table 2). When comparing barriers on teaching to nonteaching teams, a higher proportion of teaching teams were still rounding on all patients and were working on discharge paperwork at the second survey. Barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied (data not shown).
The physicians identified 1,237 patients at any time point as being possible discharges during the day of the survey and these had a mean (±SD) of 1.3 (±0.5) barriers cited for why these patients were possible rather than definite discharges. The most common were that clinical improvement was needed, one or more pending issues related to their care needed to be resolved, and/or awaiting pending test results. The need to see clinical improvement generally decreased throughout the day as did the need to staff patients with an attending physician, but barriers related to consultant recommendations or completing procedures increased (Table 3). Of the 1,237 patients ever identified as possible discharges, 594 (48%) became a definite discharge by the third call and 444 (36%) became a no discharge as their final status. As with definite discharges, barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied.
Among the 949 and 1,237 patients who were ever identified as definite or possible discharges, respectively, at any time point during the study day, 28 (3%) and 444 (36%), respectively, had their discharge status changed to no discharge, most commonly because their clinical condition either worsened or expected improvements did not occur or that barriers pertaining to social work, physical therapy, or occupational therapy were not resolved.
The median time that the discharge orders were entered into the electronic medical record was 43 minutes earlier if patients were on teams with a lower versus a higher starting census (P = .0003), 48 minutes earlier if they were seen by physicians whose rounding style was to see patients first who potentially could be discharged (P = .0026), and 58 minutes earlier if they were on nonteaching versus teaching services (P < .0001; Table 4). For every one-person increase in census, the discharge order time increased by 6 minutes (β = 5.6, SE = 1.6, P = .0003).
DISCUSSION
The important findings of this study are that (1) the large majority of issues thought to delay discharging patients identified as definite discharges were related to physicians caring for other patients on their team, (2) although 91% of patients ever identified as being definite discharges were discharged on the day of the survey, only 48% of those identified as possible discharges became definite discharges by the afternoon time point, largely because the anticipated clinical improvement did not occur or care being provided by ancillary services had not been completed, and (3) discharge orders on patients identified as definite discharges were written on average 50 minutes earlier by physicians on teams with a smaller starting patient census, on nonteaching services, or when the rounding style was to see patients ready for discharges first.
Previous research has reported that physician-perceived barriers to discharge were extrinsic to providers and even extrinsic to the hospital setting (eg, awaiting subacute nursing placement and transportation).28,29 However, many of the barriers that we identified were related directly to the providers’ workload and rounding styles and whether the patients were on teaching versus nonteaching services. We also found that delays in the ability of hospital services to complete care also contributed to delayed discharges.
Our observational data suggest that delays resulting from caring for other patients might be reduced by changing rounding styles such that patients ready for discharge are seen first and are discharged prior to seeing other patients on the team, as previously reported by Beck et al.30 Intuitively, this would seem to be a straightforward way of freeing up beds earlier in the day, but such a change will, of necessity, lead to delaying care for other patients, which, in turn, could increase their length of stays. Durvasula et al. suggested that discharges could be moved to earlier in the day by completing orders and paperwork the day prior to discharge.25 Such an approach might be effective on an Obstetrical or elective Orthopedic service on which patients predictably are hospitalized for a fixed number of days (or even hours) but may be less relevant to patients on internal medicine services where lengths of stay are less predictable. Interventions to improve discharge times have resulted in earlier discharge times in some studies,2,4 but the overall length of stay either did not decrease25 or increased31 in others. Werthheimer et al.1 did find earlier discharge times, but other interventions also occurred during the study period (eg, extending social work services to include weekends).1,32
We found that discharge times were approximately 50 minutes earlier on teams with a smaller starting census, on nonteaching compared with teaching services, or when the attending’s rounding style was to see patients ready for discharges first. Although 50 minutes may seem like a small change in discharge time, Khanna et al.33 found that when discharges occur even 1 hour earlier, hospital overcrowding is reduced. To have a lower team census would require having more teams and more providers to staff these teams, raising cost-effectiveness concerns. Moving to more nonteaching services could represent a conflict with respect to one of the missions of teaching hospitals and raises a cost-benefit issue as several teaching hospitals receive substantial funding in support of their teaching activities and housestaff would have to be replaced with more expensive providers.
Delays attributable to ancillary services indicate imbalances between demand and availability of these services. Inappropriate demand and inefficiencies could be reduced by systems redesign, but in at least some instances, additional resources will be needed to add staff, increase space, or add additional equipment.
Our study has several limitations. First, we surveyed only physicians working in university-affiliated hospitals, and three of these were public safety-net hospitals. Accordingly, our results may not be generalizable to different patient populations. Second, we surveyed only physicians, and Minichiello et al.29 found that barriers to discharge perceived by physicians were different from those of other staff. Third, our data were observational and were collected only on weekdays. Fourth, we did not differentiate interns from residents, and thus, potentially the level of training could have affected these results. Similarly, the decision for a “possible” and a “definite” discharge is likely dependent on the knowledge base of the participant, such that less experienced participants may have had differing perspectives than someone with more experience. Fifth, the sites did vary based on the infrastructure and support but also had several similarities. All sites had social work and case management involved in care, although at some sites, they were assigned according to team and at others according to geographic location. Similarly, rounding times varied. Most of the services surveyed did not utilize advanced practice providers (the exception was the nonteaching services at Denver Health, and their presence was variable). These differences in staffing models could also have affected these results.
Our study also has a number of strengths. First, we assessed the barriers at five different hospitals. Second, we collected real-time data related to specific barriers at multiple time points throughout the day, allowing us to assess the dynamic nature of identifying patients as being ready or nearly ready for discharge. Third, we assessed the perceptions of barriers to discharge from physicians working on teaching as well as nonteaching services and from physicians utilizing a variety of rounding styles. Fourth, we had a very high participation rate (100%), probably due to the fact that our study was strategically aligned with participants’ daily work activities.
In conclusion, we found two distinct categories of issues that physicians perceived as most commonly delaying writing discharge orders on their patients. The first pertained to patients thought to definitely be ready for discharge and was related to the physicians having to care for other patients on their team. The second pertained to patients identified as possibly ready for discharge and was related to the need for care to be completed by a variety of ancillary services. Addressing each of these barriers would require different interventions and a need to weigh the potential improvements that could be achieved against the increased costs and/or delays in care for other patients that may result.
Disclosures
The authors report no conflicts of interest relevant to this work.
Hospital discharges frequently occur in the afternoon or evening hours.1-5 Late discharges can adversely affect patient flow throughout the hospital,3,6-9 which, in turn, can result in delays in care,10-16 more medication errors,17 increased mortality,18-20 longer lengths of stay,20-22 higher costs,23 and lower patient satisfaction.24
Various interventions have been employed in the attempts to find ways of moving discharge times to earlier in the day, including preparing the discharge paperwork and medications the previous night,25 using checklists,1,25 team huddles,2 providing real-time feedback to unit staff,1 and employing multidisciplinary teamwork.1,2,6,25,26
The purpose of this study was to identify and determine the relative frequency of barriers to writing discharge orders in the hopes of identifying issues that might be addressed by targeted interventions. We also assessed the effects of daily team census, patients being on teaching versus nonteaching services, and how daily rounds were structured at the time that the discharge orders were written.
METHODS
Study Design, Setting, and Participants
We conducted a prospective, cross-sectional survey of house-staff and attending physicians on general medicine teaching and nonteaching services from November 13, 2014, through May 31, 2016. The study was conducted at the following five hospitals: Denver Health Medical Center (DHMC) and Presbyterian/Saint Luke’s Medical Center (PSL) in Denver, Colorado; Ronald Reagan University (UCLA) and Los Angeles County/University of Southern California Medical Center (LAC+USC) in Los Angeles, California; and Harborview Medical Center (HMC) in Seattle, Washington. The study was approved by the Colorado Multi-Institutional Review Board as well as by the review boards of the other participating sites.
Data Collection
The results of the focus groups composed of attending physicians at DHMC were used to develop our initial data collection template. Additional sites joining the study provided feedback, leading to modifications (Appendix 1).
Physicians were surveyed at three different time points on study days that were selected according to the convenience of the investigators. The sampling occurred only on weekdays and was done based on the investigators’ availability. Investigators would attempt to survey as many teams as they were able to but, secondary to feasibility, not all teams could be surveyed on study days. The specific time points varied as a function of physician workflows but were standardized as much as possible to occur in the early morning, around noon, and midafternoon on weekdays. Physicians were contacted either in person or by telephone for verbal consent prior to administering the first survey. All general medicine teams were eligible. For teaching teams, the order of contact was resident, intern, and then attending based on which physician was available at the time of the survey and on which member of the team was thought to know the patients the best. For the nonteaching services, the attending physicians were contacted.
During the initial survey, the investigators assessed the provider role (ie, attending or housestaff), whether the service was a teaching or a nonteaching service, and the starting patient census on that service primarily based on interviewing the provider of record for the team and looking at team census lists. Physicians were asked about their rounding style (ie, sickest patients first, patients likely to be discharged first, room-by-room, most recently admitted patients first, patients on the team the longest, or other) and then to identify all patients they thought would be definite discharges sometime during the day of the survey. Definite discharges were defined as patients whom the provider thought were either currently ready for discharge or who had only minor barriers that, if unresolved, would not prevent same-day discharge. They were asked if the discharge order had been entered and, if not, what was preventing them from doing so, if the discharge could in their opinion have occurred the day prior and, if so, why this did not occur. We also obtained the date and time of the admission and discharge orders, the actual discharge time, as well as the length of stay either through chart review (majority of sites) or from data warehouses (Denver Health and Presbyterian St. Lukes had length of stay data retrieved from their data warehouse).
Physicians were also asked to identify all patients whom they thought might possibly be discharged that day. Possible discharges were defined as patients with barriers to discharge that, if unresolved, would prevent same-day discharge. For each of these, the physicians were asked to list whatever issues needed to be resolved prior to placing the discharge order (Appendix 1).
The second survey was administered late morning on the same day, typically between 11
The third survey was administered midafternoon, typically around 3 PM similar to the first two surveys, with the exception that the third survey did not attempt to identify new definite or possible discharges.
Sample Size
We stopped collecting data after obtaining a convenience sample of 5% of total discharges at each study site or on the study end date, which was May 31, 2016, whichever came first.
Data Analysis
Data were collected and managed using a secure, web-based application electronic data capture tool (REDCap), hosted at Denver Health. REDCap (Research Electronic Data Capture, Nashville, Tennessee) is designed to support data collection for research studies.27 Data were then analyzed using SAS Enterprise Guide 5.1 (SAS Institute, Inc., Cary, North Carolina). All data entered into REDCap were reviewed by the principal investigator to ensure that data were not missing, and when there were missing data, a query was sent to verify if the data were retrievable. If retrievable, then the data would be entered. The volume of missing data that remained is described in our results.
Continuous variables were described using means and standard deviations (SD) or medians and interquartile ranges (IQR) based on tests of normality. Differences in the time that the discharge orders were placed in the electronic medical record according to morning patient census, teaching versus nonteaching service, and rounding style were compared using the Wilcoxon rank sum test. Linear regression was used to evaluate the effect of patient census on discharge order time. P < .05 was considered as significant.
RESULTS
We conducted 1,584 patient evaluations through surveys of 254 physicians over 156 days. Given surveys coincided with the existing work we had full participation (ie, 100% participation) and no dropout during the study days. Median (IQR) survey time points were 8:30
The characteristics of the five hospitals participating in the study, the patients’ final discharge status, the types of physicians surveyed, the services on which they were working, the rounding styles employed, and the median starting daily census are summarized in Table 1. The majority of the physicians surveyed were housestaff working on teaching services, and only a small minority structured rounds such that patients ready for discharge were seen first.
Over the course of the three surveys, 949 patients were identified as being definite discharges at any time point, and the large majority of these (863, 91%) were discharged on the day of the survey. The median (IQR) time that the discharge orders were written was 11:50
During the initial morning survey, 314 patients were identified as being definite discharges for that day (representing approximately 6% of the total number of patients being cared for, or 33% of the patients identified as definite discharges throughout the day). Of these, the physicians thought that 44 (<1% of the total number of patients being cared for on the services) could have been discharged on the previous day. The most frequent reasons cited for why these patients were not discharged on the previous day were “Patient did not want to leave” (n = 15, 34%), “Too late in the day” (n = 10, 23%), and “No ride” (n = 9, 20%). The remaining 10 patients (23%) had a variety of reasons related to system or social issues (ie, shelter not available, miscommunication).
At the morning time point, the most common barriers to discharge identified were that the physicians had not finished rounding on their team of patients and that the housestaff needed to staff their patients with their attending. At noon, caring for other patients and tending to the discharge processes were most commonly cited, and in the afternoon, the most common barriers were that the physicians were in the process of completing the discharge paperwork for those patients or were discharging other patients (Table 2). When comparing barriers on teaching to nonteaching teams, a higher proportion of teaching teams were still rounding on all patients and were working on discharge paperwork at the second survey. Barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied (data not shown).
The physicians identified 1,237 patients at any time point as being possible discharges during the day of the survey and these had a mean (±SD) of 1.3 (±0.5) barriers cited for why these patients were possible rather than definite discharges. The most common were that clinical improvement was needed, one or more pending issues related to their care needed to be resolved, and/or awaiting pending test results. The need to see clinical improvement generally decreased throughout the day as did the need to staff patients with an attending physician, but barriers related to consultant recommendations or completing procedures increased (Table 3). Of the 1,237 patients ever identified as possible discharges, 594 (48%) became a definite discharge by the third call and 444 (36%) became a no discharge as their final status. As with definite discharges, barriers cited by sites were similar; however, the frequency at which the barriers were mentioned varied.
Among the 949 and 1,237 patients who were ever identified as definite or possible discharges, respectively, at any time point during the study day, 28 (3%) and 444 (36%), respectively, had their discharge status changed to no discharge, most commonly because their clinical condition either worsened or expected improvements did not occur or that barriers pertaining to social work, physical therapy, or occupational therapy were not resolved.
The median time that the discharge orders were entered into the electronic medical record was 43 minutes earlier if patients were on teams with a lower versus a higher starting census (P = .0003), 48 minutes earlier if they were seen by physicians whose rounding style was to see patients first who potentially could be discharged (P = .0026), and 58 minutes earlier if they were on nonteaching versus teaching services (P < .0001; Table 4). For every one-person increase in census, the discharge order time increased by 6 minutes (β = 5.6, SE = 1.6, P = .0003).
DISCUSSION
The important findings of this study are that (1) the large majority of issues thought to delay discharging patients identified as definite discharges were related to physicians caring for other patients on their team, (2) although 91% of patients ever identified as being definite discharges were discharged on the day of the survey, only 48% of those identified as possible discharges became definite discharges by the afternoon time point, largely because the anticipated clinical improvement did not occur or care being provided by ancillary services had not been completed, and (3) discharge orders on patients identified as definite discharges were written on average 50 minutes earlier by physicians on teams with a smaller starting patient census, on nonteaching services, or when the rounding style was to see patients ready for discharges first.
Previous research has reported that physician-perceived barriers to discharge were extrinsic to providers and even extrinsic to the hospital setting (eg, awaiting subacute nursing placement and transportation).28,29 However, many of the barriers that we identified were related directly to the providers’ workload and rounding styles and whether the patients were on teaching versus nonteaching services. We also found that delays in the ability of hospital services to complete care also contributed to delayed discharges.
Our observational data suggest that delays resulting from caring for other patients might be reduced by changing rounding styles such that patients ready for discharge are seen first and are discharged prior to seeing other patients on the team, as previously reported by Beck et al.30 Intuitively, this would seem to be a straightforward way of freeing up beds earlier in the day, but such a change will, of necessity, lead to delaying care for other patients, which, in turn, could increase their length of stays. Durvasula et al. suggested that discharges could be moved to earlier in the day by completing orders and paperwork the day prior to discharge.25 Such an approach might be effective on an Obstetrical or elective Orthopedic service on which patients predictably are hospitalized for a fixed number of days (or even hours) but may be less relevant to patients on internal medicine services where lengths of stay are less predictable. Interventions to improve discharge times have resulted in earlier discharge times in some studies,2,4 but the overall length of stay either did not decrease25 or increased31 in others. Werthheimer et al.1 did find earlier discharge times, but other interventions also occurred during the study period (eg, extending social work services to include weekends).1,32
We found that discharge times were approximately 50 minutes earlier on teams with a smaller starting census, on nonteaching compared with teaching services, or when the attending’s rounding style was to see patients ready for discharges first. Although 50 minutes may seem like a small change in discharge time, Khanna et al.33 found that when discharges occur even 1 hour earlier, hospital overcrowding is reduced. To have a lower team census would require having more teams and more providers to staff these teams, raising cost-effectiveness concerns. Moving to more nonteaching services could represent a conflict with respect to one of the missions of teaching hospitals and raises a cost-benefit issue as several teaching hospitals receive substantial funding in support of their teaching activities and housestaff would have to be replaced with more expensive providers.
Delays attributable to ancillary services indicate imbalances between demand and availability of these services. Inappropriate demand and inefficiencies could be reduced by systems redesign, but in at least some instances, additional resources will be needed to add staff, increase space, or add additional equipment.
Our study has several limitations. First, we surveyed only physicians working in university-affiliated hospitals, and three of these were public safety-net hospitals. Accordingly, our results may not be generalizable to different patient populations. Second, we surveyed only physicians, and Minichiello et al.29 found that barriers to discharge perceived by physicians were different from those of other staff. Third, our data were observational and were collected only on weekdays. Fourth, we did not differentiate interns from residents, and thus, potentially the level of training could have affected these results. Similarly, the decision for a “possible” and a “definite” discharge is likely dependent on the knowledge base of the participant, such that less experienced participants may have had differing perspectives than someone with more experience. Fifth, the sites did vary based on the infrastructure and support but also had several similarities. All sites had social work and case management involved in care, although at some sites, they were assigned according to team and at others according to geographic location. Similarly, rounding times varied. Most of the services surveyed did not utilize advanced practice providers (the exception was the nonteaching services at Denver Health, and their presence was variable). These differences in staffing models could also have affected these results.
Our study also has a number of strengths. First, we assessed the barriers at five different hospitals. Second, we collected real-time data related to specific barriers at multiple time points throughout the day, allowing us to assess the dynamic nature of identifying patients as being ready or nearly ready for discharge. Third, we assessed the perceptions of barriers to discharge from physicians working on teaching as well as nonteaching services and from physicians utilizing a variety of rounding styles. Fourth, we had a very high participation rate (100%), probably due to the fact that our study was strategically aligned with participants’ daily work activities.
In conclusion, we found two distinct categories of issues that physicians perceived as most commonly delaying writing discharge orders on their patients. The first pertained to patients thought to definitely be ready for discharge and was related to the physicians having to care for other patients on their team. The second pertained to patients identified as possibly ready for discharge and was related to the need for care to be completed by a variety of ancillary services. Addressing each of these barriers would require different interventions and a need to weigh the potential improvements that could be achieved against the increased costs and/or delays in care for other patients that may result.
Disclosures
The authors report no conflicts of interest relevant to this work.
1. Wertheimer B, Jacobs RE, Bailey M, et al. Discharge before noon: an achievable hospital goal. J Hosp Med. 2014;9(4):210-214. doi: 10.1002/jhm.2154. PubMed
2. Kane M, Weinacker A, Arthofer R, et al. A multidisciplinary initiative to increase inpatient discharges before noon. J Nurs Adm. 2016;46(12):630-635. doi: 10.1097/NNA.0000000000000418. PubMed
3. Khanna S, Sier D, Boyle J, Zeitz K. Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emerg Med Australas. 2016;28(2):164-170. doi: 10.1111/1742-6723.12543. PubMed
4. Kravet SJ, Levine RB, Rubin HR, Wright SM. Discharging patients earlier in the day: a concept worth evaluating. Health Care Manag (Frederick). 2007;26:142-146. doi: 10.1097/01.HCM.0000268617.33491.60. PubMed
5. Khanna S, Boyle J, Good N, Lind J. Impact of admission and discharge peak times on hospital overcrowding. Stud Health Technol Inform. 2011;168:82-88. doi: 10.3233/978-1-60750-791-8-82. PubMed
6. McGowan JE, Truwit JD, Cipriano P, et al. Operating room efficiency and hospital capacity: factors affecting operating room use during maximum hospital census. J Am Coll Surg. 2007;204(5):865-871; discussion 71-72. doi: 10.1016/j.jamcollsurg.2007.01.052 PubMed
7. Khanna S, Boyle J, Good N, Lind J. Early discharge and its effect on ED length of stay and access block. Stud Health Technol Inform. 2012;178:92-98. doi: 10.3233/978-1-61499-078-9-92 PubMed
8. Powell ES, Khare RK, Venkatesh AK, Van Roo BD, Adams JG, Reinhardt G. The relationship between inpatient discharge timing and emergency department boarding. J Emerg Med. 2012;42(2):186-196. doi: 10.1016/j.jemermed.2010.06.028. PubMed
9. Wertheimer B, Jacobs RE, Iturrate E, Bailey M, Hochman K. Discharge before noon: Effect on throughput and sustainability. J Hosp Med. 2015;10(10):664-669. doi: 10.1002/jhm.2412. PubMed
10. Sikka R, Mehta S, Kaucky C, Kulstad EB. ED crowding is associated with an increased time to pneumonia treatment. Am J Emerg Med. 2010;28(7):809-812. doi: 10.1016/j.ajem.2009.06.023. PubMed
11. Coil CJ, Flood JD, Belyeu BM, Young P, Kaji AH, Lewis RJ. The effect of emergency department boarding on order completion. Ann Emerg Med. 2016;67:730-736 e2. doi: 10.1016/j.annemergmed.2015.09.018. PubMed
12. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. doi: 10.1016/j.ajem.2017.01.061. PubMed
13. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med. 2007;50(5):510-516. doi: 10.1016/j.annemergmed.2007.07.021. PubMed
14. Hwang U, Richardson L, Livote E, Harris B, Spencer N, Sean Morrison R. Emergency department crowding and decreased quality of pain care. Acad Emerg Med. 2008;15:1248-1255. doi: 10.1111/j.1553-2712.2008.00267.x. PubMed
15. Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med. 2009;16:603-608. doi: 10.1111/j.1553-2712.2009.00441.x. PubMed
16. Pines JM, Shofer FS, Isserman JA, Abbuhl SB, Mills AM. The effect of emergency department crowding on analgesia in patients with back pain in two hospitals. Acad Emerg Med. 2010;17(3):276-283. doi: 10.1111/j.1553-2712.2009.00676.x. PubMed
17. Kulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am J Emerg Med. 2010;28:304-309. doi: 10.1016/j.ajem.2008.12.014. PubMed
18. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006;184(5):213-216. PubMed
19. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126-136. doi: 10.1016/j.annemergmed.2008.03.014. PubMed
20. Singer AJ, Thode HC, Jr., Viccellio P, Pines JM. The association between length of emergency department boarding and mortality. Acad Emerg Med. 2011;18(12):1324-1329. doi: 10.1111/j.1553-2712.2011.01236.x. PubMed
21. White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DF. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med. 2013;44(1):230-235. doi: 10.1016/j.jemermed.2012.05.007. PubMed
22. Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127-133. doi: 10.1197/aemj.10.2.127. PubMed
23. Foley M, Kifaieh N, Mallon WK. Financial impact of emergency department crowding. West J Emerg Med. 2011;12(2):192-197. PubMed
24. Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM. The effect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med. 2008;15(9):825-831. doi: 10.1111/j.1553-2712.2008.00200.x. PubMed
25. Durvasula R, Kayihan A, Del Bene S, et al. A multidisciplinary care pathway significantly increases the number of early morning discharges in a large academic medical center. Qual Manag Health Care. 2015;24:45-51. doi: 10.1097/QMH.0000000000000049. PubMed
26. Cho HJ, Desai N, Florendo A, et al. E-DIP: Early Discharge Project. A Model for Throughput and Early Discharge for 1-Day Admissions. BMJ Qual Improv Rep. 2016;5(1): pii: u210035.w4128. doi: 10.1136/bmjquality.u210035.w4128. PubMed
27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010. PubMed
28. Patel H, Fang MC, Mourad M, et al. Hospitalist and internal medicine leaders’ perspectives of early discharge challenges at academic medical centers. J Hosp Med. 2018;13(6):388-391. doi: 10.12788/jhm.2885. PubMed
29. Minichiello TM, Auerbach AD, Wachter RM. Caregiver perceptions of the reasons for delayed hospital discharge. Eff Clin Pract. 2001;4(6):250-255. PubMed
30. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV. Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hosp Pract (1995). 2016;44(5):252-259. doi: 10.1080/21548331.2016.1254559. PubMed
31. Rajkomar A, Valencia V, Novelero M, Mourad M, Auerbach A. The association between discharge before noon and length of stay in medical and surgical patients. J Hosp Med. 2016;11(12):859-861. doi: 10.1002/jhm.2529. PubMed
32. Shine D. Discharge before noon: an urban legend. Am J Med. 2015;128(5):445-446. doi: 10.1016/j.amjmed.2014.12.011. PubMed33. Khanna S, Boyle J, Good N, Lind J. Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24(5):510-517. doi: 10.1111/j.1742-6723.2012.01587.x. PubMed
1. Wertheimer B, Jacobs RE, Bailey M, et al. Discharge before noon: an achievable hospital goal. J Hosp Med. 2014;9(4):210-214. doi: 10.1002/jhm.2154. PubMed
2. Kane M, Weinacker A, Arthofer R, et al. A multidisciplinary initiative to increase inpatient discharges before noon. J Nurs Adm. 2016;46(12):630-635. doi: 10.1097/NNA.0000000000000418. PubMed
3. Khanna S, Sier D, Boyle J, Zeitz K. Discharge timeliness and its impact on hospital crowding and emergency department flow performance. Emerg Med Australas. 2016;28(2):164-170. doi: 10.1111/1742-6723.12543. PubMed
4. Kravet SJ, Levine RB, Rubin HR, Wright SM. Discharging patients earlier in the day: a concept worth evaluating. Health Care Manag (Frederick). 2007;26:142-146. doi: 10.1097/01.HCM.0000268617.33491.60. PubMed
5. Khanna S, Boyle J, Good N, Lind J. Impact of admission and discharge peak times on hospital overcrowding. Stud Health Technol Inform. 2011;168:82-88. doi: 10.3233/978-1-60750-791-8-82. PubMed
6. McGowan JE, Truwit JD, Cipriano P, et al. Operating room efficiency and hospital capacity: factors affecting operating room use during maximum hospital census. J Am Coll Surg. 2007;204(5):865-871; discussion 71-72. doi: 10.1016/j.jamcollsurg.2007.01.052 PubMed
7. Khanna S, Boyle J, Good N, Lind J. Early discharge and its effect on ED length of stay and access block. Stud Health Technol Inform. 2012;178:92-98. doi: 10.3233/978-1-61499-078-9-92 PubMed
8. Powell ES, Khare RK, Venkatesh AK, Van Roo BD, Adams JG, Reinhardt G. The relationship between inpatient discharge timing and emergency department boarding. J Emerg Med. 2012;42(2):186-196. doi: 10.1016/j.jemermed.2010.06.028. PubMed
9. Wertheimer B, Jacobs RE, Iturrate E, Bailey M, Hochman K. Discharge before noon: Effect on throughput and sustainability. J Hosp Med. 2015;10(10):664-669. doi: 10.1002/jhm.2412. PubMed
10. Sikka R, Mehta S, Kaucky C, Kulstad EB. ED crowding is associated with an increased time to pneumonia treatment. Am J Emerg Med. 2010;28(7):809-812. doi: 10.1016/j.ajem.2009.06.023. PubMed
11. Coil CJ, Flood JD, Belyeu BM, Young P, Kaji AH, Lewis RJ. The effect of emergency department boarding on order completion. Ann Emerg Med. 2016;67:730-736 e2. doi: 10.1016/j.annemergmed.2015.09.018. PubMed
12. Gaieski DF, Agarwal AK, Mikkelsen ME, et al. The impact of ED crowding on early interventions and mortality in patients with severe sepsis. Am J Emerg Med. 2017;35:953-960. doi: 10.1016/j.ajem.2017.01.061. PubMed
13. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med. 2007;50(5):510-516. doi: 10.1016/j.annemergmed.2007.07.021. PubMed
14. Hwang U, Richardson L, Livote E, Harris B, Spencer N, Sean Morrison R. Emergency department crowding and decreased quality of pain care. Acad Emerg Med. 2008;15:1248-1255. doi: 10.1111/j.1553-2712.2008.00267.x. PubMed
15. Mills AM, Shofer FS, Chen EH, Hollander JE, Pines JM. The association between emergency department crowding and analgesia administration in acute abdominal pain patients. Acad Emerg Med. 2009;16:603-608. doi: 10.1111/j.1553-2712.2009.00441.x. PubMed
16. Pines JM, Shofer FS, Isserman JA, Abbuhl SB, Mills AM. The effect of emergency department crowding on analgesia in patients with back pain in two hospitals. Acad Emerg Med. 2010;17(3):276-283. doi: 10.1111/j.1553-2712.2009.00676.x. PubMed
17. Kulstad EB, Sikka R, Sweis RT, Kelley KM, Rzechula KH. ED overcrowding is associated with an increased frequency of medication errors. Am J Emerg Med. 2010;28:304-309. doi: 10.1016/j.ajem.2008.12.014. PubMed
18. Richardson DB. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust. 2006;184(5):213-216. PubMed
19. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med. 2008;52(2):126-136. doi: 10.1016/j.annemergmed.2008.03.014. PubMed
20. Singer AJ, Thode HC, Jr., Viccellio P, Pines JM. The association between length of emergency department boarding and mortality. Acad Emerg Med. 2011;18(12):1324-1329. doi: 10.1111/j.1553-2712.2011.01236.x. PubMed
21. White BA, Biddinger PD, Chang Y, Grabowski B, Carignan S, Brown DF. Boarding inpatients in the emergency department increases discharged patient length of stay. J Emerg Med. 2013;44(1):230-235. doi: 10.1016/j.jemermed.2012.05.007. PubMed
22. Forster AJ, Stiell I, Wells G, Lee AJ, van Walraven C. The effect of hospital occupancy on emergency department length of stay and patient disposition. Acad Emerg Med. 2003;10(2):127-133. doi: 10.1197/aemj.10.2.127. PubMed
23. Foley M, Kifaieh N, Mallon WK. Financial impact of emergency department crowding. West J Emerg Med. 2011;12(2):192-197. PubMed
24. Pines JM, Iyer S, Disbot M, Hollander JE, Shofer FS, Datner EM. The effect of emergency department crowding on patient satisfaction for admitted patients. Acad Emerg Med. 2008;15(9):825-831. doi: 10.1111/j.1553-2712.2008.00200.x. PubMed
25. Durvasula R, Kayihan A, Del Bene S, et al. A multidisciplinary care pathway significantly increases the number of early morning discharges in a large academic medical center. Qual Manag Health Care. 2015;24:45-51. doi: 10.1097/QMH.0000000000000049. PubMed
26. Cho HJ, Desai N, Florendo A, et al. E-DIP: Early Discharge Project. A Model for Throughput and Early Discharge for 1-Day Admissions. BMJ Qual Improv Rep. 2016;5(1): pii: u210035.w4128. doi: 10.1136/bmjquality.u210035.w4128. PubMed
27. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. doi: 10.1016/j.jbi.2008.08.010. PubMed
28. Patel H, Fang MC, Mourad M, et al. Hospitalist and internal medicine leaders’ perspectives of early discharge challenges at academic medical centers. J Hosp Med. 2018;13(6):388-391. doi: 10.12788/jhm.2885. PubMed
29. Minichiello TM, Auerbach AD, Wachter RM. Caregiver perceptions of the reasons for delayed hospital discharge. Eff Clin Pract. 2001;4(6):250-255. PubMed
30. Beck MJ, Okerblom D, Kumar A, Bandyopadhyay S, Scalzi LV. Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hosp Pract (1995). 2016;44(5):252-259. doi: 10.1080/21548331.2016.1254559. PubMed
31. Rajkomar A, Valencia V, Novelero M, Mourad M, Auerbach A. The association between discharge before noon and length of stay in medical and surgical patients. J Hosp Med. 2016;11(12):859-861. doi: 10.1002/jhm.2529. PubMed
32. Shine D. Discharge before noon: an urban legend. Am J Med. 2015;128(5):445-446. doi: 10.1016/j.amjmed.2014.12.011. PubMed33. Khanna S, Boyle J, Good N, Lind J. Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block. Emerg Med Australas. 2012;24(5):510-517. doi: 10.1111/j.1742-6723.2012.01587.x. PubMed
Gender Disparities for Academic Hospitalists
Gender disparities still exist for women in academic medicine.[1, 2, 3, 4, 5, 6, 7, 8, 9] The most recent data from the Association of American Medical Colleges (AAMC) show that although gender disparities are decreasing, women are still under‐represented in the assistant, associate, and full‐professor ranks as well as in leadership positions.[1]
Some studies indicate that gender differences are less evident when examining younger cohorts.[1, 10, 11, 12, 13] Hospital medicine emerged around 1996, when the term hospitalist was first coined.[14] The gender distribution of academic hospitalists is likely nearly equal,[15, 16] and they are generally younger physicians.[15, 17, 18, 19, 20] Accordingly, we questioned whether gender disparities existed in academic hospital medicine (HM) and, if so, whether these disparities were greater than those that might exist in academic general internal medicine (GIM).
METHODS
This study consisted of both prospective and retrospective observation of data collected for academic adult hospitalists and general internists who practice in the United States. It was approved by the Colorado Multiple Institutional Review Board.
Gender distribution was assessed with respect to: (1) academic HM and GIM faculty, (2) leadership (ie, division or section heads), and (3) scholarly work (ie, speaking opportunities and publications). Data were collected between October 1, 2012 and August 31, 2014.
Gender Distribution of Faculty and Division/Section Heads
All US internal medicine residency programs were identified from the list of members or affiliates of the AAMC that were fully accredited by the Liaison Committee on Medical Education[21] using the Graduate Medical Education Directory.[22] We then determined the primary training hospital(s) affiliated with each program and selected those that were considered to be university hospitals and eliminated those that did not have divisions or sections of HM or GIM. We determined the gender of the respective division/section heads on the basis of the faculty member's first name (and often from accompanying photos) as well as from information obtained via Internet searches and, if necessary, contacted the individual institutions via email or phone call(s). We also determined the number and gender of all of the HM and GIM faculty members in a random sample of 25% of these hospitals from information on their respective websites.
Gender Distribution for Scholarly Productivity
We determined the gender and specialty of all speakers at the Society of Hospital Medicine and the Society of General Internal Medicine national conferences from 2006 to 2012. A list of speakers at each conference was obtained from conference pamphlets or agendas that were available via Internet searches or obtained directly from the organization. We also determined whether each presenter was a featured speaker (defined as one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), or if they spoke in a group format (also as indicated in the conference pamphlets). Because of the low number of featured and plenary speakers, these data were combined. Faculty labeled as additional faculty when presenting in a group format were excluded as were speakers at precourses, those presenting abstracts, and those participating in interest group sessions.
For authorship, a PubMed search was used to identify all articles published in the Journal of Hospital Medicine (JHM) and the Journal of General Internal Medicine (JGIM) from January 1, 2006 through December 31, 2012, and the gender and specialty of all the first and last authors were determined as described above. Specialty was determined from the division, section or department affiliation indicated for each author and by Internet searches. In some instances, it was necessary to contact the authors or their departments directly to verify their specialty. When articles had only 1 author, the author was considered a first author.
Duplicate records (eg, same author, same journal) and articles without an author were excluded, as were authors who did not have an MD, DO, or MBBS degree and those who were not affiliated with an institution in the United States. All manuscripts, with the exception of errata, were analyzed together as well as in 3 subgroups: original research, editorials, and others.
A second investigator corroborated data regarding gender and specialty for all speakers and authors to strengthen data integrity. On the rare occasion when discrepancies were found, a third investigator adjudicated the results.
Definitions
Physicians were defined as being hospitalists if they were listed as a member of a division or section of HM on their publications or if Internet searches indicated that they were a hospitalist or primarily worked on inpatient medical services. Physicians were considered to be general internists if they were listed as such on their publications and their specialty could be verified in Web‐based searches. If physicians appeared to have changing roles over time, we attempted to assign their specialty based upon their role at the time the article was published or the presentation was delivered. If necessary, phone calls and/or emails were also done to determine the physician's specialty.
Analysis
REDCap, a secure, Web‐based application for building and managing online surveys and databases, was used to collect and manage all study data.[23] All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc., Cary, NC). A [2] test was used to compare proportions of male versus female physicians, and data from hospitalists versus general internists. Because we performed multiple comparisons when analyzing presentations and publications, a Bonferroni adjustment was made such that a P<0.0125 for presentations and P<0.006 (within specialty) or P<0.0125 (between specialty) for the publication analyses were considered significant. P<0.05 was considered significant for all other comparisons.
RESULTS
Gender Distribution of Faculty
Eighteen HM and 20 GIM programs from university hospitals were randomly selected for review (see Supporting Figure 1 in the online version of this article). Seven of the HM programs and 1 of the GIM programs did not have a website, did not differentiate hospitalists from other faculty, or did not list their faculty on the website and were excluded from the analysis. In the remaining 11 HM programs and 19 GIM programs, women made up 277/568 (49%) and 555/1099 (51%) of the faculty, respectively (P=0.50).
Gender Distribution of Division/Section Heads
Eighty‐six of the programs were classified as university hospitals (see Supporting Figure 1 in the online version of this article), and in these, women led 11/69 (16%) of the HM divisions or sections and 28/80 (35%) of the GIM divisions (P=0.008).
Gender Distribution for Scholarly Productivity
Speaking Opportunities
A total of 1227 presentations were given at the 2 conferences from 2006 to 2012, with 1343 of the speakers meeting inclusion criteria (see Supporting Figure 2 in the online version of this article). Hospitalists accounted for 557 of the speakers, of which 146 (26%) were women. General internists accounted for 580 of the speakers, of which 291 (50%) were women (P<0.0001) (Table 1).
| Male, N (%) | Female, N (%) | |
|---|---|---|
| ||
| Hospitalists | ||
| All presentations | 411 (74) | 146 (26)* |
| Featured or plenary presentations | 49 (91) | 5 (9)* |
| General internists | ||
| All presentations | 289 (50) | 291 (50) |
| Featured or plenary presentations | 27 (55) | 22 (45) |
Of the 117 featured or plenary speakers, 54 were hospitalists and 5 (9%) of these were women. Of the 49 who were general internists, 22 (45%) were women (P<0.0001).
Authorship
The PubMed search identified a total of 3285 articles published in the JHM and the JGIM from 2006 to 2012, and 2172 first authors and 1869 last authors met inclusion criteria (see Supporting Figure 3 in the online version of this article). Hospitalists were listed as first or last authors on 464 and 305 articles, respectively, and of these, women were first authors on 153 (33%) and last authors on 63 (21%). General internists were listed as first or last authors on 895 and 769 articles, respectively, with women as first authors on 423 (47%) and last authors on 265 (34%). Compared with general internists, fewer women hospitalists were listed as either first or last authors (both P<0.0001) (Table 2).
| First Author | Last Author | |||
|---|---|---|---|---|
| Male, N (%) | Female, N (%) | Male, N (%) | Female, N (%) | |
| ||||
| Hospitalists | ||||
| All publications | 311 (67) | 153 (33)* | 242 (79) | 63 (21)* |
| Original investigations/brief reports | 124 (61) | 79 (39)* | 96 (76) | 30 (24)* |
| Editorials | 34 (77) | 10 (23)* | 18 (86) | 3 (14)* |
| Other | 153 (71) | 64 (29)* | 128 (81) | 30 (19)* |
| General internists | ||||
| All publications | 472 (53) | 423 (47) | 504 (66) | 265 (34)* |
| Original investigations/brief reports | 218 (46) | 261 (54) | 310 (65) | 170 (35)* |
| Editorial | 98 (68) | 46 (32)* | 43 (73) | 16 (27)* |
| Other | 156 (57) | 116 (43) | 151 (66) | 79 (34)* |
Fewer women hospitalists were listed as first or last authors on all article types. For original research articles written by general internists, there was a trend for more women to be listed as first authors than men (261/479, 54%), but this difference was not statistically significant.
DISCUSSION
The important findings of this study are that, despite an equal gender distribution of academic HM and GIM faculty, fewer women were HM division/section chiefs, fewer women were speakers at the 2 selected national meetings, and fewer women were first or last authors of publications in 2 selected journals in comparison with general internists.
Previous studies have found that women lag behind their male counterparts with respect to academic productivity, leadership, and promotion.[1, 5, 7] Some studies suggest, however, that gender differences are reduced when younger cohorts are examined.[1, 10, 11, 12, 13] Surveys indicate that that the mean age of hospitalists is younger than most other specialties.[15, 19, 20, 24] The mean age of academic GIM physicians is unknown, but surveys of GIM (not differentiating academic from nonacademic) suggest that it is an older cohort than that of HM.[24] Despite hospitalists being a younger cohort, we found gender disparities in all areas investigated.
Our findings with respect to gender disparities in HM division or section leadership are consistent with the annual AAMC Women in US Academic Medicine and Science Benchmarking Report that found only 22% of all permanent division or section heads were women.[1]
Gender disparities with respect to authorship of medical publications have been previously noted,[3, 6, 15, 25] but to our knowledge, this is the first study to investigate the gender of authors who were hospitalists. Although we found a higher proportion of women hospitalists who were first or last authors than was observed by Jagsi and colleagues,[3] women hospitalists were still under‐represented with respect to this measure of academic productivity. Erren et al. reviewed 6 major journals from 2010 and 2011, and found that first authorship of original research by women ranged from 23.7% to 46.7%, and for last authorship from 18.3% to 28.8%.[25] Interestingly, we found no significant gender difference for first authors who were general internists, and there was a trend toward more women general internists being first authors than men for original research, reviews, and brief reports (data not shown).
Our study did not attempt to answer the question of why gender disparities persist, but many previous studies have explored this issue.[4, 8, 12, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] Issues raised by others include the quantity of academic work (ie, publications and grants obtained), differences in hours worked and allocation of time, lack of mentorship, family responsibilities, discrimination, differences in career motivation, and levels of institutional support, to name a few.
The under‐representation of women hospitalists in leadership, authorship, and speaking opportunities may be consistent with gender‐related differences in research productivity. Fewer publications could lead to fewer national presentations, which could lead to fewer leadership opportunities. Our findings with respect to general internists are not consistent with this idea, however, as whereas women were under‐represented in GIM leadership positions, we found no disparities with respect to the gender of first authors or speakers at national meetings for general internists. The finding that hospitalists had gender disparities with respect to first authors and national speakers but general internists did not, argues against several hypotheses (ie, that women lack mentorship, have less career motivation, fewer career building opportunities).
One notable hypothesis, and perhaps one that is often discussed in the literature, is that women shoulder the majority of family responsibilities, and this may result in women having less time for their careers. Jolly and colleagues studied physician‐researchers and noted that women were more likely than men to have spouses or domestic partners who were fully employed, spent 8.5 more hours per week on domestic activities, and were more likely to take time off during disruptions of usual child care.[33] Carr and colleagues found that women with children (compared to men with children) had fewer publications, slower self‐perceived career progress, and lower career satisfaction, but having children had little effect on faculty aspirations and goals.[2] Kaplan et al., however, found that family responsibilities do not appear to account for sex differences in academic advancement.[4] Interestingly, in a study comparing physicians from Generation X to those of the Baby Boomer age, Generation X women reported working more than their male Generation X counterparts, and both had more of a focus on worklife balance than the older generation.[12]
The nature the of 2 specialties' work environment and job requirements could have also resulted in some of the differences seen. Primary care clinical work is typically conducted Monday through Friday, and hospitalist work frequently includes some weekend, evening, night, and holiday coverage. Although these are known differences, both specialties have also been noted to offer many advantages to women and men alike, including collaborative working environments and flexible work hours.[16]
Finally, finding disparity in leadership positions in both specialties supports the possibility that those responsible for hiring could have implicit gender biases. Under‐representation in entry‐level positions is also not a likely explanation for the differences we observed, because nearly an equal number of men and women graduate from medical school, pursue residency training in internal medicine, and become either academic hospitalists or general internists at university settings.[1, 15, 24] This hypothesis could, however, explain why disparities exist with respect to senior authorship and leadership positions, as typically, these individuals have been in practice longer and the current trends of improved gender equality have not always been the case.
Our study has a number of limitations. First, we only examined publications in 2 journals and presentations at 2 national conferences, although the journals and conferences selected are considered to be the major ones in the 2 specialties. Second, using Internet searches may have resulted in inaccurate gender and specialty assignment, but previous studies have used similar methodology.[3, 43] Additionally, we also attempted to contact individuals for direct confirmation when the information we obtained was not clear and had a second investigator independently verify the gender and specialty data. Third, we utilized division/department websites when available to determine the gender of HM divisions/sections. If not recently updated, these websites may not have reflected the most current leader of the unit, but this concern would seemingly pertain to both hospitalists and general internists. Fourth, we opted to only study faculty and division/section heads at university hospitals, as typically these institutions had GIM and hospitalist groups and also typically had websites. Because we only studied faculty and leadership at university hospitals, our data are not generalizable to all hospitalist and GIM groups. Finally, we excluded pediatric hospitalists, and thus, this study is representative of adult hospitalists only. Including pediatric hospitalists was out of the scope of this project.
Our study also had a number of strengths. To our knowledge, this is the first study to provide an estimate of the gender distribution in academic HM, of hospitalists as speakers at national meetings, as first and last authors, and of HM division or section heads, and is the first to compare these results with those observed for general internists. In addition, we examined 7 years of data from 2 of the major journals and national conferences for these specialties.
In summary, despite HM being a newer field with a younger cohort of physicians, we found that gender disparities exist for women with respect to authorship, national speaking opportunities, and division or section leadership. Identifying why these gender differences exist presents an important next step.
Disclosures: Nothing to report. Marisha Burden, MD and Maria G. Frank, MD are coprincipal authors.
- Association of American Medical Colleges. Women in U.S. academic medicine and science: Statistics and benchmarking report. 2012. Available at: https://members.aamc.org/eweb/upload/Women%20in%20U%20S%20%20Academic%20Medicine%20Statistics%20and%20Benchmarking%20Report%202011-20123.pdf. Accessed September 1, 2014.
- , , , et al. Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Intern Med. 1998;129:532–538.
- , , , et al. The “gender gap” in authorship of academic medical literature—a 35‐year perspective. N Engl J Med. 2006;355:281–287.
- , , , , , . Sex differences in academic advancement. Results of a national study of pediatricians. N Engl J Med. 1996;335:1282–1289.
- . Women physicians in academic medicine: new insights from cohort studies. N Engl J Med. 2000;342:399–405.
- , , , , . Gender differences in academic productivity and leadership appointments of physicians throughout academic careers. Acad Med. 2011;86:43–47.
- , , , . Promotion of women physicians in academic medicine. Glass ceiling or sticky floor? JAMA. 1995;273:1022–1025.
- , , , . Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141:205–212.
- , , . Women physicians: choosing a career in academic medicine. Acad Med. 2012;87:105–114.
- , , , . The status of women at one academic medical center. Breaking through the glass ceiling. JAMA. 1990;264:1813–1817.
- , . Status of women in academic anesthesiology. Anesthesiology. 1986;64:496–500.
- , , . The generation and gender shifts in medicine: an exploratory survey of internal medicine physicians. BMC Health Serv Res. 2006;6:55.
- Pew Research Center. On pay gap, millenial women near parity—for now. December 2013. Available at: http://www.pewsocialtrends.org/files/2013/12/gender-and-work_final.pdf. Published December 11, 2013. Accessed February 5, 2015.
- , . The emerging role of "hospitalists" in the American health care system. N Engl J Med. 1996;335:514–517.
- , , , , , . Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27:23–27.
- . The gender factor. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/the‐gender‐factor. Published March 1, 2006. Accessed September 1, 2014.
- Association of American Medical Colleges. Analysis in brief: Supplemental information for estimating the number and characteristics of hospitalist physicians in the United States and their possible workforce implications. Available at: https://www.aamc.org/download/300686/data/aibvol12_no3-supplemental.pdf. Published August 2012. Accessed September 1, 2014.
- , , , . Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6:5–9.
- State of Hospital Medicine: 2011 Report Based on 2010 Data. Medical Group Management Association and Society of Hospital Medicine. www.mgma.com, www.hospitalmedicine.org.
- Today's Hospitalist Survey. Compensation and Career Survey Results. 2013. Available at: http://www.todayshospitalist.com/index.php?b=salary_survey_results. Accessed January 11, 2015.
- Association of American Medical Colleges. Women in U.S. Academic Medicine: Statistics and Benchmarking Report. 2009–2010. Available at: https://www.aamc.org/download/182674/data/gwims_stats_2009‐2010.pdf. Accessed September 1, 2014.
- American Medical Association. Graduate Medical Education Directory 2012–2013. Chicago, IL: American Medical Association; 2012:182–203.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
- Association of American Medical Colleges. 2012 Physician Specialty Data Book. Center for Workforce Studies. Available at: https://www.aamc.org/download/313228/data/2012physicianspecialtydatabook.pdf. Published November 2012. Accessed September 1, 2014.
- , , , . Representation of women as authors, reviewers, editors in chief, and editorial board members at 6 general medical journals in 2010 and 2011. JAMA Intern Med. 2014;174:633–635.
- , , , et al. Relationships of gender and career motivation to medical faculty members' production of academic publications. Acad Med. 1998;73:180–186.
- , , , et al. Faculty perceptions of gender discrimination and sexual harassment in academic medicine. Ann Intern Med. 2000;132:889–896.
- , , , . Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey. Arch Intern Med. 2000;160:2625–2629.
- , , , , . A "ton of feathers": gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018.
- , , . Perceived obstacles to career success for women in academic surgery. Arch Surg. 2000;135:972–977.
- , , , . Career satisfaction of US women physicians: results from the Women Physicians' Health Study. Society of General Internal Medicine Career Satisfaction Study Group. Arch Intern Med. 1999;159:1417–1426.
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- , , , , , . Gender differences in time spent on parenting and domestic responsibilities by high‐achieving young physician‐researchers. Ann Intern Med. 2014;160:344–353.
- , , , , . Stories from early‐career women physicians who have left academic medicine: a qualitative study at a single institution. Acad Med. 2011;86:752–758.
- , , , . The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30:193–201.
- , , , , . Experiencing the culture of academic medicine: gender matters, a national study. J Gen Intern Med. 2013;28:201–207.
- . Gender pay gaps in hospital medicine. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/gender‐pay‐gaps‐in‐hospital‐medicine. Published February 29, 2012. Accessed September 1, 2014.
- , , . Mentoring in academic medicine: a systematic review. JAMA. 2006;296:1103–1115.
- . Inequality quantified: mind the gender gap. Nature. 2013;495:22–24.
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- . Gender factors in reviewer recommendations for manuscript publication. J Appl Behav Anal. 1990;23:539–543.
- , , , . Scientific impact of women in academic surgery. J Surg Res. 2008;148:13–16.
Gender disparities still exist for women in academic medicine.[1, 2, 3, 4, 5, 6, 7, 8, 9] The most recent data from the Association of American Medical Colleges (AAMC) show that although gender disparities are decreasing, women are still under‐represented in the assistant, associate, and full‐professor ranks as well as in leadership positions.[1]
Some studies indicate that gender differences are less evident when examining younger cohorts.[1, 10, 11, 12, 13] Hospital medicine emerged around 1996, when the term hospitalist was first coined.[14] The gender distribution of academic hospitalists is likely nearly equal,[15, 16] and they are generally younger physicians.[15, 17, 18, 19, 20] Accordingly, we questioned whether gender disparities existed in academic hospital medicine (HM) and, if so, whether these disparities were greater than those that might exist in academic general internal medicine (GIM).
METHODS
This study consisted of both prospective and retrospective observation of data collected for academic adult hospitalists and general internists who practice in the United States. It was approved by the Colorado Multiple Institutional Review Board.
Gender distribution was assessed with respect to: (1) academic HM and GIM faculty, (2) leadership (ie, division or section heads), and (3) scholarly work (ie, speaking opportunities and publications). Data were collected between October 1, 2012 and August 31, 2014.
Gender Distribution of Faculty and Division/Section Heads
All US internal medicine residency programs were identified from the list of members or affiliates of the AAMC that were fully accredited by the Liaison Committee on Medical Education[21] using the Graduate Medical Education Directory.[22] We then determined the primary training hospital(s) affiliated with each program and selected those that were considered to be university hospitals and eliminated those that did not have divisions or sections of HM or GIM. We determined the gender of the respective division/section heads on the basis of the faculty member's first name (and often from accompanying photos) as well as from information obtained via Internet searches and, if necessary, contacted the individual institutions via email or phone call(s). We also determined the number and gender of all of the HM and GIM faculty members in a random sample of 25% of these hospitals from information on their respective websites.
Gender Distribution for Scholarly Productivity
We determined the gender and specialty of all speakers at the Society of Hospital Medicine and the Society of General Internal Medicine national conferences from 2006 to 2012. A list of speakers at each conference was obtained from conference pamphlets or agendas that were available via Internet searches or obtained directly from the organization. We also determined whether each presenter was a featured speaker (defined as one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), or if they spoke in a group format (also as indicated in the conference pamphlets). Because of the low number of featured and plenary speakers, these data were combined. Faculty labeled as additional faculty when presenting in a group format were excluded as were speakers at precourses, those presenting abstracts, and those participating in interest group sessions.
For authorship, a PubMed search was used to identify all articles published in the Journal of Hospital Medicine (JHM) and the Journal of General Internal Medicine (JGIM) from January 1, 2006 through December 31, 2012, and the gender and specialty of all the first and last authors were determined as described above. Specialty was determined from the division, section or department affiliation indicated for each author and by Internet searches. In some instances, it was necessary to contact the authors or their departments directly to verify their specialty. When articles had only 1 author, the author was considered a first author.
Duplicate records (eg, same author, same journal) and articles without an author were excluded, as were authors who did not have an MD, DO, or MBBS degree and those who were not affiliated with an institution in the United States. All manuscripts, with the exception of errata, were analyzed together as well as in 3 subgroups: original research, editorials, and others.
A second investigator corroborated data regarding gender and specialty for all speakers and authors to strengthen data integrity. On the rare occasion when discrepancies were found, a third investigator adjudicated the results.
Definitions
Physicians were defined as being hospitalists if they were listed as a member of a division or section of HM on their publications or if Internet searches indicated that they were a hospitalist or primarily worked on inpatient medical services. Physicians were considered to be general internists if they were listed as such on their publications and their specialty could be verified in Web‐based searches. If physicians appeared to have changing roles over time, we attempted to assign their specialty based upon their role at the time the article was published or the presentation was delivered. If necessary, phone calls and/or emails were also done to determine the physician's specialty.
Analysis
REDCap, a secure, Web‐based application for building and managing online surveys and databases, was used to collect and manage all study data.[23] All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc., Cary, NC). A [2] test was used to compare proportions of male versus female physicians, and data from hospitalists versus general internists. Because we performed multiple comparisons when analyzing presentations and publications, a Bonferroni adjustment was made such that a P<0.0125 for presentations and P<0.006 (within specialty) or P<0.0125 (between specialty) for the publication analyses were considered significant. P<0.05 was considered significant for all other comparisons.
RESULTS
Gender Distribution of Faculty
Eighteen HM and 20 GIM programs from university hospitals were randomly selected for review (see Supporting Figure 1 in the online version of this article). Seven of the HM programs and 1 of the GIM programs did not have a website, did not differentiate hospitalists from other faculty, or did not list their faculty on the website and were excluded from the analysis. In the remaining 11 HM programs and 19 GIM programs, women made up 277/568 (49%) and 555/1099 (51%) of the faculty, respectively (P=0.50).
Gender Distribution of Division/Section Heads
Eighty‐six of the programs were classified as university hospitals (see Supporting Figure 1 in the online version of this article), and in these, women led 11/69 (16%) of the HM divisions or sections and 28/80 (35%) of the GIM divisions (P=0.008).
Gender Distribution for Scholarly Productivity
Speaking Opportunities
A total of 1227 presentations were given at the 2 conferences from 2006 to 2012, with 1343 of the speakers meeting inclusion criteria (see Supporting Figure 2 in the online version of this article). Hospitalists accounted for 557 of the speakers, of which 146 (26%) were women. General internists accounted for 580 of the speakers, of which 291 (50%) were women (P<0.0001) (Table 1).
| Male, N (%) | Female, N (%) | |
|---|---|---|
| ||
| Hospitalists | ||
| All presentations | 411 (74) | 146 (26)* |
| Featured or plenary presentations | 49 (91) | 5 (9)* |
| General internists | ||
| All presentations | 289 (50) | 291 (50) |
| Featured or plenary presentations | 27 (55) | 22 (45) |
Of the 117 featured or plenary speakers, 54 were hospitalists and 5 (9%) of these were women. Of the 49 who were general internists, 22 (45%) were women (P<0.0001).
Authorship
The PubMed search identified a total of 3285 articles published in the JHM and the JGIM from 2006 to 2012, and 2172 first authors and 1869 last authors met inclusion criteria (see Supporting Figure 3 in the online version of this article). Hospitalists were listed as first or last authors on 464 and 305 articles, respectively, and of these, women were first authors on 153 (33%) and last authors on 63 (21%). General internists were listed as first or last authors on 895 and 769 articles, respectively, with women as first authors on 423 (47%) and last authors on 265 (34%). Compared with general internists, fewer women hospitalists were listed as either first or last authors (both P<0.0001) (Table 2).
| First Author | Last Author | |||
|---|---|---|---|---|
| Male, N (%) | Female, N (%) | Male, N (%) | Female, N (%) | |
| ||||
| Hospitalists | ||||
| All publications | 311 (67) | 153 (33)* | 242 (79) | 63 (21)* |
| Original investigations/brief reports | 124 (61) | 79 (39)* | 96 (76) | 30 (24)* |
| Editorials | 34 (77) | 10 (23)* | 18 (86) | 3 (14)* |
| Other | 153 (71) | 64 (29)* | 128 (81) | 30 (19)* |
| General internists | ||||
| All publications | 472 (53) | 423 (47) | 504 (66) | 265 (34)* |
| Original investigations/brief reports | 218 (46) | 261 (54) | 310 (65) | 170 (35)* |
| Editorial | 98 (68) | 46 (32)* | 43 (73) | 16 (27)* |
| Other | 156 (57) | 116 (43) | 151 (66) | 79 (34)* |
Fewer women hospitalists were listed as first or last authors on all article types. For original research articles written by general internists, there was a trend for more women to be listed as first authors than men (261/479, 54%), but this difference was not statistically significant.
DISCUSSION
The important findings of this study are that, despite an equal gender distribution of academic HM and GIM faculty, fewer women were HM division/section chiefs, fewer women were speakers at the 2 selected national meetings, and fewer women were first or last authors of publications in 2 selected journals in comparison with general internists.
Previous studies have found that women lag behind their male counterparts with respect to academic productivity, leadership, and promotion.[1, 5, 7] Some studies suggest, however, that gender differences are reduced when younger cohorts are examined.[1, 10, 11, 12, 13] Surveys indicate that that the mean age of hospitalists is younger than most other specialties.[15, 19, 20, 24] The mean age of academic GIM physicians is unknown, but surveys of GIM (not differentiating academic from nonacademic) suggest that it is an older cohort than that of HM.[24] Despite hospitalists being a younger cohort, we found gender disparities in all areas investigated.
Our findings with respect to gender disparities in HM division or section leadership are consistent with the annual AAMC Women in US Academic Medicine and Science Benchmarking Report that found only 22% of all permanent division or section heads were women.[1]
Gender disparities with respect to authorship of medical publications have been previously noted,[3, 6, 15, 25] but to our knowledge, this is the first study to investigate the gender of authors who were hospitalists. Although we found a higher proportion of women hospitalists who were first or last authors than was observed by Jagsi and colleagues,[3] women hospitalists were still under‐represented with respect to this measure of academic productivity. Erren et al. reviewed 6 major journals from 2010 and 2011, and found that first authorship of original research by women ranged from 23.7% to 46.7%, and for last authorship from 18.3% to 28.8%.[25] Interestingly, we found no significant gender difference for first authors who were general internists, and there was a trend toward more women general internists being first authors than men for original research, reviews, and brief reports (data not shown).
Our study did not attempt to answer the question of why gender disparities persist, but many previous studies have explored this issue.[4, 8, 12, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] Issues raised by others include the quantity of academic work (ie, publications and grants obtained), differences in hours worked and allocation of time, lack of mentorship, family responsibilities, discrimination, differences in career motivation, and levels of institutional support, to name a few.
The under‐representation of women hospitalists in leadership, authorship, and speaking opportunities may be consistent with gender‐related differences in research productivity. Fewer publications could lead to fewer national presentations, which could lead to fewer leadership opportunities. Our findings with respect to general internists are not consistent with this idea, however, as whereas women were under‐represented in GIM leadership positions, we found no disparities with respect to the gender of first authors or speakers at national meetings for general internists. The finding that hospitalists had gender disparities with respect to first authors and national speakers but general internists did not, argues against several hypotheses (ie, that women lack mentorship, have less career motivation, fewer career building opportunities).
One notable hypothesis, and perhaps one that is often discussed in the literature, is that women shoulder the majority of family responsibilities, and this may result in women having less time for their careers. Jolly and colleagues studied physician‐researchers and noted that women were more likely than men to have spouses or domestic partners who were fully employed, spent 8.5 more hours per week on domestic activities, and were more likely to take time off during disruptions of usual child care.[33] Carr and colleagues found that women with children (compared to men with children) had fewer publications, slower self‐perceived career progress, and lower career satisfaction, but having children had little effect on faculty aspirations and goals.[2] Kaplan et al., however, found that family responsibilities do not appear to account for sex differences in academic advancement.[4] Interestingly, in a study comparing physicians from Generation X to those of the Baby Boomer age, Generation X women reported working more than their male Generation X counterparts, and both had more of a focus on worklife balance than the older generation.[12]
The nature the of 2 specialties' work environment and job requirements could have also resulted in some of the differences seen. Primary care clinical work is typically conducted Monday through Friday, and hospitalist work frequently includes some weekend, evening, night, and holiday coverage. Although these are known differences, both specialties have also been noted to offer many advantages to women and men alike, including collaborative working environments and flexible work hours.[16]
Finally, finding disparity in leadership positions in both specialties supports the possibility that those responsible for hiring could have implicit gender biases. Under‐representation in entry‐level positions is also not a likely explanation for the differences we observed, because nearly an equal number of men and women graduate from medical school, pursue residency training in internal medicine, and become either academic hospitalists or general internists at university settings.[1, 15, 24] This hypothesis could, however, explain why disparities exist with respect to senior authorship and leadership positions, as typically, these individuals have been in practice longer and the current trends of improved gender equality have not always been the case.
Our study has a number of limitations. First, we only examined publications in 2 journals and presentations at 2 national conferences, although the journals and conferences selected are considered to be the major ones in the 2 specialties. Second, using Internet searches may have resulted in inaccurate gender and specialty assignment, but previous studies have used similar methodology.[3, 43] Additionally, we also attempted to contact individuals for direct confirmation when the information we obtained was not clear and had a second investigator independently verify the gender and specialty data. Third, we utilized division/department websites when available to determine the gender of HM divisions/sections. If not recently updated, these websites may not have reflected the most current leader of the unit, but this concern would seemingly pertain to both hospitalists and general internists. Fourth, we opted to only study faculty and division/section heads at university hospitals, as typically these institutions had GIM and hospitalist groups and also typically had websites. Because we only studied faculty and leadership at university hospitals, our data are not generalizable to all hospitalist and GIM groups. Finally, we excluded pediatric hospitalists, and thus, this study is representative of adult hospitalists only. Including pediatric hospitalists was out of the scope of this project.
Our study also had a number of strengths. To our knowledge, this is the first study to provide an estimate of the gender distribution in academic HM, of hospitalists as speakers at national meetings, as first and last authors, and of HM division or section heads, and is the first to compare these results with those observed for general internists. In addition, we examined 7 years of data from 2 of the major journals and national conferences for these specialties.
In summary, despite HM being a newer field with a younger cohort of physicians, we found that gender disparities exist for women with respect to authorship, national speaking opportunities, and division or section leadership. Identifying why these gender differences exist presents an important next step.
Disclosures: Nothing to report. Marisha Burden, MD and Maria G. Frank, MD are coprincipal authors.
Gender disparities still exist for women in academic medicine.[1, 2, 3, 4, 5, 6, 7, 8, 9] The most recent data from the Association of American Medical Colleges (AAMC) show that although gender disparities are decreasing, women are still under‐represented in the assistant, associate, and full‐professor ranks as well as in leadership positions.[1]
Some studies indicate that gender differences are less evident when examining younger cohorts.[1, 10, 11, 12, 13] Hospital medicine emerged around 1996, when the term hospitalist was first coined.[14] The gender distribution of academic hospitalists is likely nearly equal,[15, 16] and they are generally younger physicians.[15, 17, 18, 19, 20] Accordingly, we questioned whether gender disparities existed in academic hospital medicine (HM) and, if so, whether these disparities were greater than those that might exist in academic general internal medicine (GIM).
METHODS
This study consisted of both prospective and retrospective observation of data collected for academic adult hospitalists and general internists who practice in the United States. It was approved by the Colorado Multiple Institutional Review Board.
Gender distribution was assessed with respect to: (1) academic HM and GIM faculty, (2) leadership (ie, division or section heads), and (3) scholarly work (ie, speaking opportunities and publications). Data were collected between October 1, 2012 and August 31, 2014.
Gender Distribution of Faculty and Division/Section Heads
All US internal medicine residency programs were identified from the list of members or affiliates of the AAMC that were fully accredited by the Liaison Committee on Medical Education[21] using the Graduate Medical Education Directory.[22] We then determined the primary training hospital(s) affiliated with each program and selected those that were considered to be university hospitals and eliminated those that did not have divisions or sections of HM or GIM. We determined the gender of the respective division/section heads on the basis of the faculty member's first name (and often from accompanying photos) as well as from information obtained via Internet searches and, if necessary, contacted the individual institutions via email or phone call(s). We also determined the number and gender of all of the HM and GIM faculty members in a random sample of 25% of these hospitals from information on their respective websites.
Gender Distribution for Scholarly Productivity
We determined the gender and specialty of all speakers at the Society of Hospital Medicine and the Society of General Internal Medicine national conferences from 2006 to 2012. A list of speakers at each conference was obtained from conference pamphlets or agendas that were available via Internet searches or obtained directly from the organization. We also determined whether each presenter was a featured speaker (defined as one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), or if they spoke in a group format (also as indicated in the conference pamphlets). Because of the low number of featured and plenary speakers, these data were combined. Faculty labeled as additional faculty when presenting in a group format were excluded as were speakers at precourses, those presenting abstracts, and those participating in interest group sessions.
For authorship, a PubMed search was used to identify all articles published in the Journal of Hospital Medicine (JHM) and the Journal of General Internal Medicine (JGIM) from January 1, 2006 through December 31, 2012, and the gender and specialty of all the first and last authors were determined as described above. Specialty was determined from the division, section or department affiliation indicated for each author and by Internet searches. In some instances, it was necessary to contact the authors or their departments directly to verify their specialty. When articles had only 1 author, the author was considered a first author.
Duplicate records (eg, same author, same journal) and articles without an author were excluded, as were authors who did not have an MD, DO, or MBBS degree and those who were not affiliated with an institution in the United States. All manuscripts, with the exception of errata, were analyzed together as well as in 3 subgroups: original research, editorials, and others.
A second investigator corroborated data regarding gender and specialty for all speakers and authors to strengthen data integrity. On the rare occasion when discrepancies were found, a third investigator adjudicated the results.
Definitions
Physicians were defined as being hospitalists if they were listed as a member of a division or section of HM on their publications or if Internet searches indicated that they were a hospitalist or primarily worked on inpatient medical services. Physicians were considered to be general internists if they were listed as such on their publications and their specialty could be verified in Web‐based searches. If physicians appeared to have changing roles over time, we attempted to assign their specialty based upon their role at the time the article was published or the presentation was delivered. If necessary, phone calls and/or emails were also done to determine the physician's specialty.
Analysis
REDCap, a secure, Web‐based application for building and managing online surveys and databases, was used to collect and manage all study data.[23] All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc., Cary, NC). A [2] test was used to compare proportions of male versus female physicians, and data from hospitalists versus general internists. Because we performed multiple comparisons when analyzing presentations and publications, a Bonferroni adjustment was made such that a P<0.0125 for presentations and P<0.006 (within specialty) or P<0.0125 (between specialty) for the publication analyses were considered significant. P<0.05 was considered significant for all other comparisons.
RESULTS
Gender Distribution of Faculty
Eighteen HM and 20 GIM programs from university hospitals were randomly selected for review (see Supporting Figure 1 in the online version of this article). Seven of the HM programs and 1 of the GIM programs did not have a website, did not differentiate hospitalists from other faculty, or did not list their faculty on the website and were excluded from the analysis. In the remaining 11 HM programs and 19 GIM programs, women made up 277/568 (49%) and 555/1099 (51%) of the faculty, respectively (P=0.50).
Gender Distribution of Division/Section Heads
Eighty‐six of the programs were classified as university hospitals (see Supporting Figure 1 in the online version of this article), and in these, women led 11/69 (16%) of the HM divisions or sections and 28/80 (35%) of the GIM divisions (P=0.008).
Gender Distribution for Scholarly Productivity
Speaking Opportunities
A total of 1227 presentations were given at the 2 conferences from 2006 to 2012, with 1343 of the speakers meeting inclusion criteria (see Supporting Figure 2 in the online version of this article). Hospitalists accounted for 557 of the speakers, of which 146 (26%) were women. General internists accounted for 580 of the speakers, of which 291 (50%) were women (P<0.0001) (Table 1).
| Male, N (%) | Female, N (%) | |
|---|---|---|
| ||
| Hospitalists | ||
| All presentations | 411 (74) | 146 (26)* |
| Featured or plenary presentations | 49 (91) | 5 (9)* |
| General internists | ||
| All presentations | 289 (50) | 291 (50) |
| Featured or plenary presentations | 27 (55) | 22 (45) |
Of the 117 featured or plenary speakers, 54 were hospitalists and 5 (9%) of these were women. Of the 49 who were general internists, 22 (45%) were women (P<0.0001).
Authorship
The PubMed search identified a total of 3285 articles published in the JHM and the JGIM from 2006 to 2012, and 2172 first authors and 1869 last authors met inclusion criteria (see Supporting Figure 3 in the online version of this article). Hospitalists were listed as first or last authors on 464 and 305 articles, respectively, and of these, women were first authors on 153 (33%) and last authors on 63 (21%). General internists were listed as first or last authors on 895 and 769 articles, respectively, with women as first authors on 423 (47%) and last authors on 265 (34%). Compared with general internists, fewer women hospitalists were listed as either first or last authors (both P<0.0001) (Table 2).
| First Author | Last Author | |||
|---|---|---|---|---|
| Male, N (%) | Female, N (%) | Male, N (%) | Female, N (%) | |
| ||||
| Hospitalists | ||||
| All publications | 311 (67) | 153 (33)* | 242 (79) | 63 (21)* |
| Original investigations/brief reports | 124 (61) | 79 (39)* | 96 (76) | 30 (24)* |
| Editorials | 34 (77) | 10 (23)* | 18 (86) | 3 (14)* |
| Other | 153 (71) | 64 (29)* | 128 (81) | 30 (19)* |
| General internists | ||||
| All publications | 472 (53) | 423 (47) | 504 (66) | 265 (34)* |
| Original investigations/brief reports | 218 (46) | 261 (54) | 310 (65) | 170 (35)* |
| Editorial | 98 (68) | 46 (32)* | 43 (73) | 16 (27)* |
| Other | 156 (57) | 116 (43) | 151 (66) | 79 (34)* |
Fewer women hospitalists were listed as first or last authors on all article types. For original research articles written by general internists, there was a trend for more women to be listed as first authors than men (261/479, 54%), but this difference was not statistically significant.
DISCUSSION
The important findings of this study are that, despite an equal gender distribution of academic HM and GIM faculty, fewer women were HM division/section chiefs, fewer women were speakers at the 2 selected national meetings, and fewer women were first or last authors of publications in 2 selected journals in comparison with general internists.
Previous studies have found that women lag behind their male counterparts with respect to academic productivity, leadership, and promotion.[1, 5, 7] Some studies suggest, however, that gender differences are reduced when younger cohorts are examined.[1, 10, 11, 12, 13] Surveys indicate that that the mean age of hospitalists is younger than most other specialties.[15, 19, 20, 24] The mean age of academic GIM physicians is unknown, but surveys of GIM (not differentiating academic from nonacademic) suggest that it is an older cohort than that of HM.[24] Despite hospitalists being a younger cohort, we found gender disparities in all areas investigated.
Our findings with respect to gender disparities in HM division or section leadership are consistent with the annual AAMC Women in US Academic Medicine and Science Benchmarking Report that found only 22% of all permanent division or section heads were women.[1]
Gender disparities with respect to authorship of medical publications have been previously noted,[3, 6, 15, 25] but to our knowledge, this is the first study to investigate the gender of authors who were hospitalists. Although we found a higher proportion of women hospitalists who were first or last authors than was observed by Jagsi and colleagues,[3] women hospitalists were still under‐represented with respect to this measure of academic productivity. Erren et al. reviewed 6 major journals from 2010 and 2011, and found that first authorship of original research by women ranged from 23.7% to 46.7%, and for last authorship from 18.3% to 28.8%.[25] Interestingly, we found no significant gender difference for first authors who were general internists, and there was a trend toward more women general internists being first authors than men for original research, reviews, and brief reports (data not shown).
Our study did not attempt to answer the question of why gender disparities persist, but many previous studies have explored this issue.[4, 8, 12, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42] Issues raised by others include the quantity of academic work (ie, publications and grants obtained), differences in hours worked and allocation of time, lack of mentorship, family responsibilities, discrimination, differences in career motivation, and levels of institutional support, to name a few.
The under‐representation of women hospitalists in leadership, authorship, and speaking opportunities may be consistent with gender‐related differences in research productivity. Fewer publications could lead to fewer national presentations, which could lead to fewer leadership opportunities. Our findings with respect to general internists are not consistent with this idea, however, as whereas women were under‐represented in GIM leadership positions, we found no disparities with respect to the gender of first authors or speakers at national meetings for general internists. The finding that hospitalists had gender disparities with respect to first authors and national speakers but general internists did not, argues against several hypotheses (ie, that women lack mentorship, have less career motivation, fewer career building opportunities).
One notable hypothesis, and perhaps one that is often discussed in the literature, is that women shoulder the majority of family responsibilities, and this may result in women having less time for their careers. Jolly and colleagues studied physician‐researchers and noted that women were more likely than men to have spouses or domestic partners who were fully employed, spent 8.5 more hours per week on domestic activities, and were more likely to take time off during disruptions of usual child care.[33] Carr and colleagues found that women with children (compared to men with children) had fewer publications, slower self‐perceived career progress, and lower career satisfaction, but having children had little effect on faculty aspirations and goals.[2] Kaplan et al., however, found that family responsibilities do not appear to account for sex differences in academic advancement.[4] Interestingly, in a study comparing physicians from Generation X to those of the Baby Boomer age, Generation X women reported working more than their male Generation X counterparts, and both had more of a focus on worklife balance than the older generation.[12]
The nature the of 2 specialties' work environment and job requirements could have also resulted in some of the differences seen. Primary care clinical work is typically conducted Monday through Friday, and hospitalist work frequently includes some weekend, evening, night, and holiday coverage. Although these are known differences, both specialties have also been noted to offer many advantages to women and men alike, including collaborative working environments and flexible work hours.[16]
Finally, finding disparity in leadership positions in both specialties supports the possibility that those responsible for hiring could have implicit gender biases. Under‐representation in entry‐level positions is also not a likely explanation for the differences we observed, because nearly an equal number of men and women graduate from medical school, pursue residency training in internal medicine, and become either academic hospitalists or general internists at university settings.[1, 15, 24] This hypothesis could, however, explain why disparities exist with respect to senior authorship and leadership positions, as typically, these individuals have been in practice longer and the current trends of improved gender equality have not always been the case.
Our study has a number of limitations. First, we only examined publications in 2 journals and presentations at 2 national conferences, although the journals and conferences selected are considered to be the major ones in the 2 specialties. Second, using Internet searches may have resulted in inaccurate gender and specialty assignment, but previous studies have used similar methodology.[3, 43] Additionally, we also attempted to contact individuals for direct confirmation when the information we obtained was not clear and had a second investigator independently verify the gender and specialty data. Third, we utilized division/department websites when available to determine the gender of HM divisions/sections. If not recently updated, these websites may not have reflected the most current leader of the unit, but this concern would seemingly pertain to both hospitalists and general internists. Fourth, we opted to only study faculty and division/section heads at university hospitals, as typically these institutions had GIM and hospitalist groups and also typically had websites. Because we only studied faculty and leadership at university hospitals, our data are not generalizable to all hospitalist and GIM groups. Finally, we excluded pediatric hospitalists, and thus, this study is representative of adult hospitalists only. Including pediatric hospitalists was out of the scope of this project.
Our study also had a number of strengths. To our knowledge, this is the first study to provide an estimate of the gender distribution in academic HM, of hospitalists as speakers at national meetings, as first and last authors, and of HM division or section heads, and is the first to compare these results with those observed for general internists. In addition, we examined 7 years of data from 2 of the major journals and national conferences for these specialties.
In summary, despite HM being a newer field with a younger cohort of physicians, we found that gender disparities exist for women with respect to authorship, national speaking opportunities, and division or section leadership. Identifying why these gender differences exist presents an important next step.
Disclosures: Nothing to report. Marisha Burden, MD and Maria G. Frank, MD are coprincipal authors.
- Association of American Medical Colleges. Women in U.S. academic medicine and science: Statistics and benchmarking report. 2012. Available at: https://members.aamc.org/eweb/upload/Women%20in%20U%20S%20%20Academic%20Medicine%20Statistics%20and%20Benchmarking%20Report%202011-20123.pdf. Accessed September 1, 2014.
- , , , et al. Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Intern Med. 1998;129:532–538.
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- . The gender factor. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/the‐gender‐factor. Published March 1, 2006. Accessed September 1, 2014.
- Association of American Medical Colleges. Analysis in brief: Supplemental information for estimating the number and characteristics of hospitalist physicians in the United States and their possible workforce implications. Available at: https://www.aamc.org/download/300686/data/aibvol12_no3-supplemental.pdf. Published August 2012. Accessed September 1, 2014.
- , , , . Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6:5–9.
- State of Hospital Medicine: 2011 Report Based on 2010 Data. Medical Group Management Association and Society of Hospital Medicine. www.mgma.com, www.hospitalmedicine.org.
- Today's Hospitalist Survey. Compensation and Career Survey Results. 2013. Available at: http://www.todayshospitalist.com/index.php?b=salary_survey_results. Accessed January 11, 2015.
- Association of American Medical Colleges. Women in U.S. Academic Medicine: Statistics and Benchmarking Report. 2009–2010. Available at: https://www.aamc.org/download/182674/data/gwims_stats_2009‐2010.pdf. Accessed September 1, 2014.
- American Medical Association. Graduate Medical Education Directory 2012–2013. Chicago, IL: American Medical Association; 2012:182–203.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
- Association of American Medical Colleges. 2012 Physician Specialty Data Book. Center for Workforce Studies. Available at: https://www.aamc.org/download/313228/data/2012physicianspecialtydatabook.pdf. Published November 2012. Accessed September 1, 2014.
- , , , . Representation of women as authors, reviewers, editors in chief, and editorial board members at 6 general medical journals in 2010 and 2011. JAMA Intern Med. 2014;174:633–635.
- , , , et al. Relationships of gender and career motivation to medical faculty members' production of academic publications. Acad Med. 1998;73:180–186.
- , , , et al. Faculty perceptions of gender discrimination and sexual harassment in academic medicine. Ann Intern Med. 2000;132:889–896.
- , , , . Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey. Arch Intern Med. 2000;160:2625–2629.
- , , , , . A "ton of feathers": gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018.
- , , . Perceived obstacles to career success for women in academic surgery. Arch Surg. 2000;135:972–977.
- , , , . Career satisfaction of US women physicians: results from the Women Physicians' Health Study. Society of General Internal Medicine Career Satisfaction Study Group. Arch Intern Med. 1999;159:1417–1426.
- . Doing the same and earning less: male and female physicians in a new medical specialty. Inquiry. 2004;41:301–315.
- , , , , , . Gender differences in time spent on parenting and domestic responsibilities by high‐achieving young physician‐researchers. Ann Intern Med. 2014;160:344–353.
- , , , , . Stories from early‐career women physicians who have left academic medicine: a qualitative study at a single institution. Acad Med. 2011;86:752–758.
- , , , . The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30:193–201.
- , , , , . Experiencing the culture of academic medicine: gender matters, a national study. J Gen Intern Med. 2013;28:201–207.
- . Gender pay gaps in hospital medicine. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/gender‐pay‐gaps‐in‐hospital‐medicine. Published February 29, 2012. Accessed September 1, 2014.
- , , . Mentoring in academic medicine: a systematic review. JAMA. 2006;296:1103–1115.
- . Inequality quantified: mind the gender gap. Nature. 2013;495:22–24.
- , , , et al. Gender differences in academic advancement: patterns, causes, and potential solutions in one US College of Medicine. Acad Med. 2003;78:500–508.
- , . Why aren't there more women leaders in academic medicine? The views of clinical department chairs. Acad Med. 2001;76:453–465.
- . Gender factors in reviewer recommendations for manuscript publication. J Appl Behav Anal. 1990;23:539–543.
- , , , . Scientific impact of women in academic surgery. J Surg Res. 2008;148:13–16.
- Association of American Medical Colleges. Women in U.S. academic medicine and science: Statistics and benchmarking report. 2012. Available at: https://members.aamc.org/eweb/upload/Women%20in%20U%20S%20%20Academic%20Medicine%20Statistics%20and%20Benchmarking%20Report%202011-20123.pdf. Accessed September 1, 2014.
- , , , et al. Relation of family responsibilities and gender to the productivity and career satisfaction of medical faculty. Ann Intern Med. 1998;129:532–538.
- , , , et al. The “gender gap” in authorship of academic medical literature—a 35‐year perspective. N Engl J Med. 2006;355:281–287.
- , , , , , . Sex differences in academic advancement. Results of a national study of pediatricians. N Engl J Med. 1996;335:1282–1289.
- . Women physicians in academic medicine: new insights from cohort studies. N Engl J Med. 2000;342:399–405.
- , , , , . Gender differences in academic productivity and leadership appointments of physicians throughout academic careers. Acad Med. 2011;86:43–47.
- , , , . Promotion of women physicians in academic medicine. Glass ceiling or sticky floor? JAMA. 1995;273:1022–1025.
- , , , . Compensation and advancement of women in academic medicine: is there equity? Ann Intern Med. 2004;141:205–212.
- , , . Women physicians: choosing a career in academic medicine. Acad Med. 2012;87:105–114.
- , , , . The status of women at one academic medical center. Breaking through the glass ceiling. JAMA. 1990;264:1813–1817.
- , . Status of women in academic anesthesiology. Anesthesiology. 1986;64:496–500.
- , , . The generation and gender shifts in medicine: an exploratory survey of internal medicine physicians. BMC Health Serv Res. 2006;6:55.
- Pew Research Center. On pay gap, millenial women near parity—for now. December 2013. Available at: http://www.pewsocialtrends.org/files/2013/12/gender-and-work_final.pdf. Published December 11, 2013. Accessed February 5, 2015.
- , . The emerging role of "hospitalists" in the American health care system. N Engl J Med. 1996;335:514–517.
- , , , , , . Mentorship, productivity, and promotion among academic hospitalists. J Gen Intern Med. 2012;27:23–27.
- . The gender factor. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/the‐gender‐factor. Published March 1, 2006. Accessed September 1, 2014.
- Association of American Medical Colleges. Analysis in brief: Supplemental information for estimating the number and characteristics of hospitalist physicians in the United States and their possible workforce implications. Available at: https://www.aamc.org/download/300686/data/aibvol12_no3-supplemental.pdf. Published August 2012. Accessed September 1, 2014.
- , , , . Survey of US academic hospitalist leaders about mentorship and academic activities in hospitalist groups. J Hosp Med. 2011;6:5–9.
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- Today's Hospitalist Survey. Compensation and Career Survey Results. 2013. Available at: http://www.todayshospitalist.com/index.php?b=salary_survey_results. Accessed January 11, 2015.
- Association of American Medical Colleges. Women in U.S. Academic Medicine: Statistics and Benchmarking Report. 2009–2010. Available at: https://www.aamc.org/download/182674/data/gwims_stats_2009‐2010.pdf. Accessed September 1, 2014.
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- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
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- , , , . Representation of women as authors, reviewers, editors in chief, and editorial board members at 6 general medical journals in 2010 and 2011. JAMA Intern Med. 2014;174:633–635.
- , , , et al. Relationships of gender and career motivation to medical faculty members' production of academic publications. Acad Med. 1998;73:180–186.
- , , , et al. Faculty perceptions of gender discrimination and sexual harassment in academic medicine. Ann Intern Med. 2000;132:889–896.
- , , , . Attitudes of clinical faculty about career progress, career success and recognition, and commitment to academic medicine. Results of a survey. Arch Intern Med. 2000;160:2625–2629.
- , , , , . A "ton of feathers": gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018.
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- , , , . Career satisfaction of US women physicians: results from the Women Physicians' Health Study. Society of General Internal Medicine Career Satisfaction Study Group. Arch Intern Med. 1999;159:1417–1426.
- . Doing the same and earning less: male and female physicians in a new medical specialty. Inquiry. 2004;41:301–315.
- , , , , , . Gender differences in time spent on parenting and domestic responsibilities by high‐achieving young physician‐researchers. Ann Intern Med. 2014;160:344–353.
- , , , , . Stories from early‐career women physicians who have left academic medicine: a qualitative study at a single institution. Acad Med. 2011;86:752–758.
- , , , . The $16,819 pay gap for newly trained physicians: the unexplained trend of men earning more than women. Health Aff (Millwood). 2011;30:193–201.
- , , , , . Experiencing the culture of academic medicine: gender matters, a national study. J Gen Intern Med. 2013;28:201–207.
- . Gender pay gaps in hospital medicine. The Hospitalist. Available at: http://www.the‐hospitalist.org/article/gender‐pay‐gaps‐in‐hospital‐medicine. Published February 29, 2012. Accessed September 1, 2014.
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- . Inequality quantified: mind the gender gap. Nature. 2013;495:22–24.
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- , , , . Scientific impact of women in academic surgery. J Surg Res. 2008;148:13–16.
© 2015 Society of Hospital Medicine
Study of Antimicrobial Scrubs
Healthcare workers' (HCWs) attire becomes contaminated with bacterial pathogens during the course of the workday,[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] and Munoz‐Price et al.[13] recently demonstrated that finding bacterial pathogens on HCWs' white coats correlated with finding the same pathogens on their hands. Because of concern for an association between attire colonization and nosocomial infection, governmental agencies in England and Scotland banned HCWs from wearing white coats or long‐sleeve garments,[14, 15] despite evidence that such an approach does not reduce contamination.[12]
Newly developed antimicrobial textiles have been incorporated into HCW scrubs,[16, 17, 18, 19, 20] and commercial Web sites and product inserts report that these products can reduce bacterial contamination by 80.9% at 8 hours to greater than 99% under laboratory conditions depending on the product and microbe studied.[16, 17, 19] Because there are limited clinical data pertaining to the effectiveness of antimicrobial scrubs, we performed a prospective study designed to determine whether wearing these products reduced bacterial contamination of HCWs' scrubs or skin at the end of an 8‐hour workday.
METHODS
Design
The study was a prospective, unblinded, randomized, controlled trial that was approved by the Colorado Multiple Institutional Review Board and conducted at Denver Health, a university‐affiliated public safety net hospital. No protocol changes occurred during the study.
Participants
Participants included hospitalist physicians, internal medicine residents, physician assistants, nurse practitioners, and nurses who directly cared for patients hospitalized on internal medicine units between March 12, 2012 and August 28, 2012. Participants known to be pregnant or those who refused to participate in the study were excluded.
Intervention
Standard scrubs issued by the hospital were tested along with 2 different antimicrobial scrubs (scrub A and scrub B). Scrub A was made with a polyester microfiber material embedded with a proprietary antimicrobial chemical. Scrub B was a polyestercotton blend scrub that included 2 proprietary antimicrobial chemicals and silver embedded into the fabric. The standard scrub was made of a polyestercotton blend with no antimicrobial properties. All scrubs consisted of pants and a short‐sleeved shirt, with either a pocket at the left breast or lower front surface, and all were tested new prior to any washing or wear. Preliminary cultures were done on 2 scrubs in each group to assess the extent of preuse contamination. All providers were instructed not to wear white coats at any time during the day that they were wearing the scrubs. Providers were not told the type of scrub they received, but the antimicrobial scrubs had a different appearance and texture than the standard scrubs, so blinding was not possible.
Outcomes
The primary end point was the total bacterial colony count of samples obtained from the breast or lower front pocket, the sleeve cuff of the dominant hand, and the pant leg at the midthigh of the dominant leg on all scrubs after an 8‐hour workday. Secondary outcomes were the bacterial colony counts of cultures obtained from the volar surface of the wrists of the HCWs' dominant arm, and the colony counts of methicillin‐resistant Staphylococcus aureus (MRSA), vancomycin‐resistant enterococci (VRE), and resistant Gram‐negative bacteria on the 3 scrub types, all obtained after the 8‐hour workday.
Cultures were collected using a standardized RODAC imprint method[21] with BBL RODAC plates containing blood agar (Becton Dickinson, Sparks, MD). Cultures were incubated in ambient air at 35 to 37C for 18 to 22 hours. After incubation, visible colonies were counted using a dissecting microscope to a maximum of 200 colonies as recommended by the manufacturer. Colonies morphologically consistent with Staphylococcus species were subsequently tested for coagulase using a BactiStaph rapid latex agglutination test (Remel, Lenexa, KS). If positive, these colonies were subcultured to sheep blood agar (Remel) and BBL MRSA CHROMagar (Becton Dickinson) and incubated for an additional 18 to 24 hours. Characteristic growth on blood agar that also produced mauve‐colored colonies on CHROMagar was taken to indicate MRSA. Colonies morphologically suspicious for being VRE were identified and confirmed as VRE using a positive identification and susceptibility panel (Microscan; Siemens, Deerfield, IL). A negative combination panel (Microscan, Siemens) was also used to identify and confirm resistant Gram‐negative rods.
Each participant completed a survey that included questions that identified their occupation, whether they had had contact with patients who were known to be colonized or infected with MRSA, VRE, or resistant Gram‐negative rods during the testing period, and whether they experienced any adverse events that might relate to wearing the uniform.
Sample Size
We assumed that cultures taken from the sleeve of the control scrubs would have a mean ( standard deviation) colony count of 69 (67) based on data from our previous study.[12] Although the companies making the antimicrobial scrubs indicated that their respective products provided between 80.9% at 8 hours and >99% reduction in bacterial colony counts in laboratory settings, we assumed that a 70% decrease in colony count compared with standard scrubs could be clinically important. After adjusting for multiple comparisons and accounting for using nonparametric analyses with an unknown distribution, we estimated a need to recruit 35 subjects in each of 3 groups.
Randomization
The principal investigator and coinvestigators enrolled and consented participants. After obtaining consent, block randomization, stratified by occupation, occurred 1 day prior to the study using a computer‐generated table of random numbers.
Statistics
Data were collected and managed using REDCap (Research Electronic Data Capture; Vanderbilt UniversityThe Institute for Medicine and Public Health, Nashville, TN) electronic data capture tools hosted at Denver Health. REDCap is a secure Web‐based application designed to support data collection for research studies, providing: (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.[22]
Colony counts were compared using a Kruskal‐Wallis 1‐way analysis of variance by ranks. Bonferroni's correction for multiple comparisons resulted in a P<0.01 as indicating statistical significance. Proportions were compared using [2] analysis. All data are presented as medians with interquartile range (IQR) or proportions.
RESULTS
We screened 118 HCWs for participation and randomized 109, 37 in the control and antimicrobial scrub group A, and 35 in antimicrobial scrub group B (during the course of the study we neglected to culture the pockets of 2 participants in the standard scrub group and 2 in antimicrobial scrub group A). Because our primary end point was total colony count from cultures taken from 3 sites, data from these 4 subjects could not be used, and all the data from these 4 subjects were excluded from the primary analysis; 4 additional subjects were subsequently recruited allowing us to meet our block enrollment target (Figure 1). The first and last participants were studied on March 12, 2012 and August 28, 2012, respectively. The trial ended once the defined number of participants was enrolled. The occupations of the 105 participants are summarized in Table 1.
| All Subjects, N=105 | Standard Scrub, n=35 | Antimicrobial Scrub A, n=35 | Antimicrobial Scrub B, n=35 | |
|---|---|---|---|---|
| Healthcare worker type, n (%) | ||||
| Attending physician | 11 (10) | 5 (14) | 3 (9) | 3 (9) |
| Intern/resident | 51 (49) | 17 (49) | 16 (46) | 18 (51) |
| Midlevels | 6 (6) | 2 (6) | 2 (6) | 2 (6) |
| Nurse | 37 (35) | 11 (31) | 14 (40) | 12 (34) |
| Cared for colonized or infected patient with antibiotic resistant organism, n (%) | 55 (52) | 16 (46) | 20 (57) | 19 (54) |
| Number of colonized or infected patients cared for, n (%) | ||||
| 1 | 37 (67) | 10 (63) | 13 (65) | 14 (74) |
| 2 | 11 (20) | 4 (25) | 6 (30) | 1 (5) |
| 3 or more | 6 (11) | 2 (12) | 1 (5) | 3 (16) |
| Unknown | 1 (2) | 0 (0) | 0 (0) | 1 (5) |
Colony counts of all scrubs cultured prior to use never exceeded 10 colonies. The median (IQR) total colony counts from all sites on the scrubs was 99 (66182) for standard scrubs, 137 (84289) for antimicrobial scrub type A, and 138 (62274) for antimicrobial scrub type B (P=0.36). We found no significant differences between the colony counts cultured from any of the individual sites among the 3 groups, regardless of occupation (Table 2). No significant difference was observed with respect to colony counts cultured from the wrist among the 3 study groups (Table 2). Comparisons between groups were planned a priori if a difference across all groups was found. Given the nonsignificant P values across all scrub groups, no further comparisons were made.
| Total (From All Sites on Scrubs) | Sleeve Cuff | Thigh | Wrist | ||
|---|---|---|---|---|---|
| |||||
| All subjects, N=105 | |||||
| Standard scrub | 99 (66182) | 41 (2070) | 20 (944) | 32 (2161) | 16 (540) |
| Antimicrobial scrub A | 137 (84289) | 65 (35117) | 33 (16124) | 41 (1586) | 23 (442) |
| Antimicrobial scrub B | 138 (62274) | 41 (2299) | 21 (941) | 40 (18107) | 15 (654) |
| P value | 0.36 | 0.17 | 0.07 | 0.57 | 0.92 |
| Physicians and midlevels, n=68 | |||||
| Standard scrub | 115.5 (72.5173.5) | 44.5 (2270.5) | 27.5 (10.538.5) | 35 (2362.5) | 24.5 (755) |
| Antimicrobial scrub A | 210 (114289) | 86 (64120) | 39 (18129) | 49 (2486) | 24 (342) |
| Antimicrobial scrub B | 149 (68295) | 52 (26126) | 21 (1069) | 37 (18141) | 19 (872) |
| P value | 0.21 | 0.08 | 0.19 | 0.85 | 0.76 |
| Nurses, n=37 | |||||
| Standard scrub | 89 (31236) | 37 (1348) | 13 (552) | 28 (1342) | 9 (321) |
| Antimicrobial scrub A | 105 (43256) | 45.5 (2258) | 21.5 (1654) | 38.5 (1268) | 17 (643) |
| Antimicrobial scrub B | 91.5 (60174.5) | 27 (1340) | 16 (7.526) | 51 (2186.5) | 10 (3.543.5) |
| P value | 0.86 | 0.39 | 0.19 | 0.49 | 0.41 |
Fifty‐five participants (52%) reported caring for patients who were known to be colonized or infected with an antibiotic‐resistant organism, 16 (46%) randomized to wear standard scrubs, and 20 (57%) and 19 (54%) randomized to wear antimicrobial scrub A or B, respectively (P=0.61). Of these, however, antibiotic‐resistant organisms were only cultured from the scrubs of 2 providers (1 with 1 colony of MRSA from the breast pocket of antimicrobial scrub A, 1 with 1 colony of MRSA cultured from the pocket of antimicrobial scrub B [P=0.55]), and from the wrist of only 1 provider (a multiresistant Gram‐negative rod who wore antimicrobial scrub B).
Adverse Events
Six subjects (5.7%) reported adverse events, all of whom were wearing antimicrobial scrubs (P=0.18). For participants wearing antimicrobial scrub A, 1 (3%) reported itchiness and 2 (6%) reported heaviness or poor breathability. For participants wearing antimicrobial scrub B, 1 (3%) reported redness, 1 (3%) reported itchiness, and 1 (3%) reported heaviness or poor breathability.
DISCUSSION
The important findings of this study are that we found no evidence indicating that either of the 2 antimicrobial scrubs tested reduced bacterial contamination or antibiotic‐resistant contamination on HCWs' scrubs or wrists compared with standard scrubs at the end of an 8‐hour workday, and that despite many HCWs being exposed to patients who were colonized or infected with antibiotic‐resistant bacteria, these organisms were only rarely cultured from their uniforms.
We found that HCWs in all 3 arms of the study had bacterial contamination on their scrubs and skin, consistent with previous studies showing that HCWs' uniforms are frequently contaminated with bacteria, including MRSA, VRE, and other pathogens.[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] We previously found that bacterial contamination of HCWs' uniforms occurs within hours of putting on newly laundered uniforms.[12]
Literature on the effectiveness of antimicrobial HCW uniforms when tested in clinical settings is limited. Bearman and colleagues[23] recently published the results of a study of 31 subjects who wore either standard or antimicrobial scrubs, crossing over every 4 weeks for 4 months, with random culturing done weekly at the beginning and end of a work shift. Scrubs were laundered an average of 1.5 times/week, but the timing of the laundering relative to when cultures were obtained was not reported. Very few isolates of MRSA, Gram‐negative rods, or VRE were found (only 3.9%, 0.4%, and 0.05% of the 2000 samples obtained, respectively), and no differences were observed with respect to the number of HCWs who had antibiotic‐resistant organisms cultured when they were wearing standard versus antimicrobial scrubs. Those who had MRSA cultured, however, had lower mean log colony counts when they were wearing the antimicrobial scrubs. The small number of samples with positive isolates, together with differences in the extent of before‐shift contamination among groups complicates interpreting these data. The authors concluded that a prospective trial was needed. We attempted to include the scrub studied by Bearman and colleagues[23] in our study, but the company had insufficient stock available at the time we tried to purchase the product.
Gross and colleagues[24] found no difference in the mean colony counts of cultures taken from silver‐impregnated versus standard scrubs in a pilot crossover study done with 10 HCWs (although there were trends toward higher colony counts when the subjects wore antimicrobial scrubs).
Antibiotic‐resistant bacteria were only cultured from 3 participants (2.9%) in our current study, compared to 16% of those randomized to wearing white coats in our previous study and 20% of those randomized to wearing standard scrubs.[12] This difference may be explained by several recent studies reporting that rates of MRSA infections in hospitals are decreasing.[25, 26] The rate of hospital‐acquired MRSA infection or colonization at our own institution decreased 80% from 2007 to 2012. At the times of our previous and current studies, providers were expected to wear gowns and gloves when caring for patients as per standard contact precautions. Rates of infection and colonization of VRE and resistant Gram‐negative rods have remained low at our hospital, and our data are consistent with the rates reported on HCWs' uniforms in other studies.[2, 5, 10]
Only 6 of our subjects reported adverse reactions, but all were wearing antimicrobial scrubs (P=0.18). Several of the participants described that the fabrics of the 2 antimicrobial scrubs were heavier and less breathable than the standard scrubs. We believe this difference is more likely to explain the adverse reactions reported than is any type of reaction to the specific chemicals in the fabrics.
Our study has several limitations. Because it was conducted on the general internal medicine units of a single university‐affiliated public hospital, the results may not generalize to other types of institutions or other inpatient services.
As we previously described,[12] the RODAC imprint method only samples a small area of HCWs' uniforms and thus does not represent total bacterial contamination.[21] We specifically cultured areas that are known to be highly contaminated (ie, sleeve cuffs and pockets). Although imprint methods have limitations (as do other methods for culturing clothing), they have been commonly utilized in studies assessing bacterial contamination of HCW clothing.[2, 3, 5]
Although some of the bacterial load we cultured could have come from the providers themselves, previous studies have shown that 80% to 90% of the resistant bacteria cultured from HCWs' attire come from other sources.[1, 2]
Because our sample size was calculated on the basis of being able to detect a difference of 70% in total bacterial colony count, our study was not large enough to exclude a lower level of effectiveness. However, we saw no trends suggesting the antimicrobial products might have a lower level of effectiveness.
We did not observe the hand‐washing practices of the participants, and accordingly, cannot confirm that these practices were the same in each of our 3 study groups. Intermittent, surreptitious monitoring of hand‐washing practices on our internal medicine units over the last several years has found compliance with hand hygiene recommendations varying from 70% to 90%.
Although the participants in our study were not explicitly told to which scrub they were randomized, the colors, appearances, and textures of the antimicrobial fabrics were different from the standard scrubs such that blinding was impossible. Participants wearing antimicrobial scrubs could have changed their hand hygiene practices (ie, less careful hand hygiene). Lack of blinding could also have led to over‐reporting of adverse events by the subjects randomized to wear the antimicrobial scrubs.
In an effort to treat all the scrubs in the same fashion, all were tested new, prior to being washed or previously worn. Studying the scrubs prior to washing or wearing could have increased the reports of adverse effects, as the fabrics could have been stiffer and more uncomfortable than they might have been at a later stage in their use.
Our study also has some strengths. Our participants included physicians, residents, nurses, nurse practitioners, and physician assistants. Accordingly, our results should be generalizable to most HCWs. We also confirmed that the scrubs that were tested were nearly sterile prior to use.
In conclusion, we found no evidence suggesting that either of 2 antimicrobial scrubs tested decreased bacterial contamination of HCWs' scrubs or skin after an 8‐hour workday compared to standard scrubs. We also found that, although HCWs are frequently exposed to patients harboring antibiotic‐resistant bacteria, these bacteria were only rarely cultured from HCWs' scrubs or skin.
- , , , . Contamination of nurses' uniforms with Staphylococcus aureus. Lancet. 1969;2:233–235.
- , , . Contamination of protective clothing and nurses' uniforms in an isolation ward. J Hosp Infect. 1983;4:149–157.
- , , . Microbial flora on doctors' white coats. BMJ. 1991;303:1602–1604.
- . Bacterial contamination of nurses' uniforms: a study. Nursing Stand. 1998;13:37–42.
- , , . Bacterial flora on the white coats of medical students. J Hosp Infect. 2000;45:65–68.
- , , . Bacterial contamination of uniforms. J Hosp Infect. 2001;48:238–241.
- , , , et al. Significance of methicillin‐resistant Staphylococcus aureus (MRSA) survey in a university teaching hospital. J Infect Chemother. 2003;9:172–177.
- . Environmental contamination makes an important contribution to hospital infection. J Hosp Infect. 2007;65(suppl 2):50–54.
- , , , et al. Detection of methicillin‐resistant Staphylococcus aureus and vancomycin‐resistant enterococci on the gowns and gloves of healthcare workers. Infect Control Hosp Epidemiol. 2008;29:583–589.
- , , , , , . Bacterial contamination of health care workers' white coats. Am J Infect Control. 2009;37:101–105.
- , , , , , . Nursing and physician attire as possible source of nosocomial infections. Am J Infect Control. 2011;39:555–559.
- , , , , , . Newly cleaned physician uniforms and infrequently washed white coats have similar rates of bacterial contamination after an 8‐hour workday: a randomized controlled trial. J Hosp Med. 2011;6:177–182.
- , , , et al. Associations between bacterial contamination of health care workers' hands and contamination of white coats and scrubs. Am J Infect Control. 2012;40:e245–e248.
- Department of Health. Uniforms and workwear: an evidence base for developing local policy. National Health Service, 17 September 2007. Available at: http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/Publicationspolicyandguidance/DH_078433. Accessed January 29, 2010.
- Scottish Government Health Directorates. NHS Scotland dress code. Available at: http://www.sehd.scot.nhs.uk/mels/CEL2008_53.pdf. Accessed February 10, 2010.
- Bio Shield Tech Web site. Bio Gardz–unisex scrub top–antimicrobial treatment. Available at: http://www.bioshieldtech.com/Bio_Gardz_Unisex_Scrub_Top_Antimicrobial_Tre_p/sbt01‐r‐p.htm. Accessed January 9, 2013.
- Doc Froc Web site and informational packet. Available at: http://www.docfroc.com. Accessed July 22, 2011.
- Vestagen Web site and informational packet. Available at: http://www.vestagen.com. Accessed July 22, 2011.
- Under Scrub apparel Web site. Testing. Available at: http://underscrub.com/testing. Accessed March 21, 2013.
- MediThreads Web site. Microban FAQ's. Available at: http://medithreads.com/faq/microban‐faqs. Accessed March 21, 2013.
- , , , , . Comparison of the Rodac imprint method to selective enrichment broth for recovery of vancomycin‐resistant enterococci and drug‐resistant Enterobacteriaceae from environmental surfaces. J Clin Microbiol. 2000;38:4646–4648.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
- , , , et al. A crossover trial of antimicrobial scrubs to reduce methicillin‐resistant Staphylococcus aureus burden on healthcare worker apparel. Infect Control Hosp Epidemiol. 2012;33:268–275.
- , , , , . Pilot study on the microbial contamination of conventional vs. silver‐impregnated uniforms worn by ambulance personnel during one week of emergency medical service. GMS Krankenhhyg Interdiszip. 2010;5.pii: Doc09.
- , , , et al. Epidemiology of Staphylococcus aureus blood and skin and soft tissue infections in the US military health system, 2005–2010. JAMA. 2012;308:50–59.
- , , , et al. Health care‐associated invasive MRSA infections, 2005–2008. JAMA. 2010;304:641–648.
Healthcare workers' (HCWs) attire becomes contaminated with bacterial pathogens during the course of the workday,[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] and Munoz‐Price et al.[13] recently demonstrated that finding bacterial pathogens on HCWs' white coats correlated with finding the same pathogens on their hands. Because of concern for an association between attire colonization and nosocomial infection, governmental agencies in England and Scotland banned HCWs from wearing white coats or long‐sleeve garments,[14, 15] despite evidence that such an approach does not reduce contamination.[12]
Newly developed antimicrobial textiles have been incorporated into HCW scrubs,[16, 17, 18, 19, 20] and commercial Web sites and product inserts report that these products can reduce bacterial contamination by 80.9% at 8 hours to greater than 99% under laboratory conditions depending on the product and microbe studied.[16, 17, 19] Because there are limited clinical data pertaining to the effectiveness of antimicrobial scrubs, we performed a prospective study designed to determine whether wearing these products reduced bacterial contamination of HCWs' scrubs or skin at the end of an 8‐hour workday.
METHODS
Design
The study was a prospective, unblinded, randomized, controlled trial that was approved by the Colorado Multiple Institutional Review Board and conducted at Denver Health, a university‐affiliated public safety net hospital. No protocol changes occurred during the study.
Participants
Participants included hospitalist physicians, internal medicine residents, physician assistants, nurse practitioners, and nurses who directly cared for patients hospitalized on internal medicine units between March 12, 2012 and August 28, 2012. Participants known to be pregnant or those who refused to participate in the study were excluded.
Intervention
Standard scrubs issued by the hospital were tested along with 2 different antimicrobial scrubs (scrub A and scrub B). Scrub A was made with a polyester microfiber material embedded with a proprietary antimicrobial chemical. Scrub B was a polyestercotton blend scrub that included 2 proprietary antimicrobial chemicals and silver embedded into the fabric. The standard scrub was made of a polyestercotton blend with no antimicrobial properties. All scrubs consisted of pants and a short‐sleeved shirt, with either a pocket at the left breast or lower front surface, and all were tested new prior to any washing or wear. Preliminary cultures were done on 2 scrubs in each group to assess the extent of preuse contamination. All providers were instructed not to wear white coats at any time during the day that they were wearing the scrubs. Providers were not told the type of scrub they received, but the antimicrobial scrubs had a different appearance and texture than the standard scrubs, so blinding was not possible.
Outcomes
The primary end point was the total bacterial colony count of samples obtained from the breast or lower front pocket, the sleeve cuff of the dominant hand, and the pant leg at the midthigh of the dominant leg on all scrubs after an 8‐hour workday. Secondary outcomes were the bacterial colony counts of cultures obtained from the volar surface of the wrists of the HCWs' dominant arm, and the colony counts of methicillin‐resistant Staphylococcus aureus (MRSA), vancomycin‐resistant enterococci (VRE), and resistant Gram‐negative bacteria on the 3 scrub types, all obtained after the 8‐hour workday.
Cultures were collected using a standardized RODAC imprint method[21] with BBL RODAC plates containing blood agar (Becton Dickinson, Sparks, MD). Cultures were incubated in ambient air at 35 to 37C for 18 to 22 hours. After incubation, visible colonies were counted using a dissecting microscope to a maximum of 200 colonies as recommended by the manufacturer. Colonies morphologically consistent with Staphylococcus species were subsequently tested for coagulase using a BactiStaph rapid latex agglutination test (Remel, Lenexa, KS). If positive, these colonies were subcultured to sheep blood agar (Remel) and BBL MRSA CHROMagar (Becton Dickinson) and incubated for an additional 18 to 24 hours. Characteristic growth on blood agar that also produced mauve‐colored colonies on CHROMagar was taken to indicate MRSA. Colonies morphologically suspicious for being VRE were identified and confirmed as VRE using a positive identification and susceptibility panel (Microscan; Siemens, Deerfield, IL). A negative combination panel (Microscan, Siemens) was also used to identify and confirm resistant Gram‐negative rods.
Each participant completed a survey that included questions that identified their occupation, whether they had had contact with patients who were known to be colonized or infected with MRSA, VRE, or resistant Gram‐negative rods during the testing period, and whether they experienced any adverse events that might relate to wearing the uniform.
Sample Size
We assumed that cultures taken from the sleeve of the control scrubs would have a mean ( standard deviation) colony count of 69 (67) based on data from our previous study.[12] Although the companies making the antimicrobial scrubs indicated that their respective products provided between 80.9% at 8 hours and >99% reduction in bacterial colony counts in laboratory settings, we assumed that a 70% decrease in colony count compared with standard scrubs could be clinically important. After adjusting for multiple comparisons and accounting for using nonparametric analyses with an unknown distribution, we estimated a need to recruit 35 subjects in each of 3 groups.
Randomization
The principal investigator and coinvestigators enrolled and consented participants. After obtaining consent, block randomization, stratified by occupation, occurred 1 day prior to the study using a computer‐generated table of random numbers.
Statistics
Data were collected and managed using REDCap (Research Electronic Data Capture; Vanderbilt UniversityThe Institute for Medicine and Public Health, Nashville, TN) electronic data capture tools hosted at Denver Health. REDCap is a secure Web‐based application designed to support data collection for research studies, providing: (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.[22]
Colony counts were compared using a Kruskal‐Wallis 1‐way analysis of variance by ranks. Bonferroni's correction for multiple comparisons resulted in a P<0.01 as indicating statistical significance. Proportions were compared using [2] analysis. All data are presented as medians with interquartile range (IQR) or proportions.
RESULTS
We screened 118 HCWs for participation and randomized 109, 37 in the control and antimicrobial scrub group A, and 35 in antimicrobial scrub group B (during the course of the study we neglected to culture the pockets of 2 participants in the standard scrub group and 2 in antimicrobial scrub group A). Because our primary end point was total colony count from cultures taken from 3 sites, data from these 4 subjects could not be used, and all the data from these 4 subjects were excluded from the primary analysis; 4 additional subjects were subsequently recruited allowing us to meet our block enrollment target (Figure 1). The first and last participants were studied on March 12, 2012 and August 28, 2012, respectively. The trial ended once the defined number of participants was enrolled. The occupations of the 105 participants are summarized in Table 1.
| All Subjects, N=105 | Standard Scrub, n=35 | Antimicrobial Scrub A, n=35 | Antimicrobial Scrub B, n=35 | |
|---|---|---|---|---|
| Healthcare worker type, n (%) | ||||
| Attending physician | 11 (10) | 5 (14) | 3 (9) | 3 (9) |
| Intern/resident | 51 (49) | 17 (49) | 16 (46) | 18 (51) |
| Midlevels | 6 (6) | 2 (6) | 2 (6) | 2 (6) |
| Nurse | 37 (35) | 11 (31) | 14 (40) | 12 (34) |
| Cared for colonized or infected patient with antibiotic resistant organism, n (%) | 55 (52) | 16 (46) | 20 (57) | 19 (54) |
| Number of colonized or infected patients cared for, n (%) | ||||
| 1 | 37 (67) | 10 (63) | 13 (65) | 14 (74) |
| 2 | 11 (20) | 4 (25) | 6 (30) | 1 (5) |
| 3 or more | 6 (11) | 2 (12) | 1 (5) | 3 (16) |
| Unknown | 1 (2) | 0 (0) | 0 (0) | 1 (5) |
Colony counts of all scrubs cultured prior to use never exceeded 10 colonies. The median (IQR) total colony counts from all sites on the scrubs was 99 (66182) for standard scrubs, 137 (84289) for antimicrobial scrub type A, and 138 (62274) for antimicrobial scrub type B (P=0.36). We found no significant differences between the colony counts cultured from any of the individual sites among the 3 groups, regardless of occupation (Table 2). No significant difference was observed with respect to colony counts cultured from the wrist among the 3 study groups (Table 2). Comparisons between groups were planned a priori if a difference across all groups was found. Given the nonsignificant P values across all scrub groups, no further comparisons were made.
| Total (From All Sites on Scrubs) | Sleeve Cuff | Thigh | Wrist | ||
|---|---|---|---|---|---|
| |||||
| All subjects, N=105 | |||||
| Standard scrub | 99 (66182) | 41 (2070) | 20 (944) | 32 (2161) | 16 (540) |
| Antimicrobial scrub A | 137 (84289) | 65 (35117) | 33 (16124) | 41 (1586) | 23 (442) |
| Antimicrobial scrub B | 138 (62274) | 41 (2299) | 21 (941) | 40 (18107) | 15 (654) |
| P value | 0.36 | 0.17 | 0.07 | 0.57 | 0.92 |
| Physicians and midlevels, n=68 | |||||
| Standard scrub | 115.5 (72.5173.5) | 44.5 (2270.5) | 27.5 (10.538.5) | 35 (2362.5) | 24.5 (755) |
| Antimicrobial scrub A | 210 (114289) | 86 (64120) | 39 (18129) | 49 (2486) | 24 (342) |
| Antimicrobial scrub B | 149 (68295) | 52 (26126) | 21 (1069) | 37 (18141) | 19 (872) |
| P value | 0.21 | 0.08 | 0.19 | 0.85 | 0.76 |
| Nurses, n=37 | |||||
| Standard scrub | 89 (31236) | 37 (1348) | 13 (552) | 28 (1342) | 9 (321) |
| Antimicrobial scrub A | 105 (43256) | 45.5 (2258) | 21.5 (1654) | 38.5 (1268) | 17 (643) |
| Antimicrobial scrub B | 91.5 (60174.5) | 27 (1340) | 16 (7.526) | 51 (2186.5) | 10 (3.543.5) |
| P value | 0.86 | 0.39 | 0.19 | 0.49 | 0.41 |
Fifty‐five participants (52%) reported caring for patients who were known to be colonized or infected with an antibiotic‐resistant organism, 16 (46%) randomized to wear standard scrubs, and 20 (57%) and 19 (54%) randomized to wear antimicrobial scrub A or B, respectively (P=0.61). Of these, however, antibiotic‐resistant organisms were only cultured from the scrubs of 2 providers (1 with 1 colony of MRSA from the breast pocket of antimicrobial scrub A, 1 with 1 colony of MRSA cultured from the pocket of antimicrobial scrub B [P=0.55]), and from the wrist of only 1 provider (a multiresistant Gram‐negative rod who wore antimicrobial scrub B).
Adverse Events
Six subjects (5.7%) reported adverse events, all of whom were wearing antimicrobial scrubs (P=0.18). For participants wearing antimicrobial scrub A, 1 (3%) reported itchiness and 2 (6%) reported heaviness or poor breathability. For participants wearing antimicrobial scrub B, 1 (3%) reported redness, 1 (3%) reported itchiness, and 1 (3%) reported heaviness or poor breathability.
DISCUSSION
The important findings of this study are that we found no evidence indicating that either of the 2 antimicrobial scrubs tested reduced bacterial contamination or antibiotic‐resistant contamination on HCWs' scrubs or wrists compared with standard scrubs at the end of an 8‐hour workday, and that despite many HCWs being exposed to patients who were colonized or infected with antibiotic‐resistant bacteria, these organisms were only rarely cultured from their uniforms.
We found that HCWs in all 3 arms of the study had bacterial contamination on their scrubs and skin, consistent with previous studies showing that HCWs' uniforms are frequently contaminated with bacteria, including MRSA, VRE, and other pathogens.[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] We previously found that bacterial contamination of HCWs' uniforms occurs within hours of putting on newly laundered uniforms.[12]
Literature on the effectiveness of antimicrobial HCW uniforms when tested in clinical settings is limited. Bearman and colleagues[23] recently published the results of a study of 31 subjects who wore either standard or antimicrobial scrubs, crossing over every 4 weeks for 4 months, with random culturing done weekly at the beginning and end of a work shift. Scrubs were laundered an average of 1.5 times/week, but the timing of the laundering relative to when cultures were obtained was not reported. Very few isolates of MRSA, Gram‐negative rods, or VRE were found (only 3.9%, 0.4%, and 0.05% of the 2000 samples obtained, respectively), and no differences were observed with respect to the number of HCWs who had antibiotic‐resistant organisms cultured when they were wearing standard versus antimicrobial scrubs. Those who had MRSA cultured, however, had lower mean log colony counts when they were wearing the antimicrobial scrubs. The small number of samples with positive isolates, together with differences in the extent of before‐shift contamination among groups complicates interpreting these data. The authors concluded that a prospective trial was needed. We attempted to include the scrub studied by Bearman and colleagues[23] in our study, but the company had insufficient stock available at the time we tried to purchase the product.
Gross and colleagues[24] found no difference in the mean colony counts of cultures taken from silver‐impregnated versus standard scrubs in a pilot crossover study done with 10 HCWs (although there were trends toward higher colony counts when the subjects wore antimicrobial scrubs).
Antibiotic‐resistant bacteria were only cultured from 3 participants (2.9%) in our current study, compared to 16% of those randomized to wearing white coats in our previous study and 20% of those randomized to wearing standard scrubs.[12] This difference may be explained by several recent studies reporting that rates of MRSA infections in hospitals are decreasing.[25, 26] The rate of hospital‐acquired MRSA infection or colonization at our own institution decreased 80% from 2007 to 2012. At the times of our previous and current studies, providers were expected to wear gowns and gloves when caring for patients as per standard contact precautions. Rates of infection and colonization of VRE and resistant Gram‐negative rods have remained low at our hospital, and our data are consistent with the rates reported on HCWs' uniforms in other studies.[2, 5, 10]
Only 6 of our subjects reported adverse reactions, but all were wearing antimicrobial scrubs (P=0.18). Several of the participants described that the fabrics of the 2 antimicrobial scrubs were heavier and less breathable than the standard scrubs. We believe this difference is more likely to explain the adverse reactions reported than is any type of reaction to the specific chemicals in the fabrics.
Our study has several limitations. Because it was conducted on the general internal medicine units of a single university‐affiliated public hospital, the results may not generalize to other types of institutions or other inpatient services.
As we previously described,[12] the RODAC imprint method only samples a small area of HCWs' uniforms and thus does not represent total bacterial contamination.[21] We specifically cultured areas that are known to be highly contaminated (ie, sleeve cuffs and pockets). Although imprint methods have limitations (as do other methods for culturing clothing), they have been commonly utilized in studies assessing bacterial contamination of HCW clothing.[2, 3, 5]
Although some of the bacterial load we cultured could have come from the providers themselves, previous studies have shown that 80% to 90% of the resistant bacteria cultured from HCWs' attire come from other sources.[1, 2]
Because our sample size was calculated on the basis of being able to detect a difference of 70% in total bacterial colony count, our study was not large enough to exclude a lower level of effectiveness. However, we saw no trends suggesting the antimicrobial products might have a lower level of effectiveness.
We did not observe the hand‐washing practices of the participants, and accordingly, cannot confirm that these practices were the same in each of our 3 study groups. Intermittent, surreptitious monitoring of hand‐washing practices on our internal medicine units over the last several years has found compliance with hand hygiene recommendations varying from 70% to 90%.
Although the participants in our study were not explicitly told to which scrub they were randomized, the colors, appearances, and textures of the antimicrobial fabrics were different from the standard scrubs such that blinding was impossible. Participants wearing antimicrobial scrubs could have changed their hand hygiene practices (ie, less careful hand hygiene). Lack of blinding could also have led to over‐reporting of adverse events by the subjects randomized to wear the antimicrobial scrubs.
In an effort to treat all the scrubs in the same fashion, all were tested new, prior to being washed or previously worn. Studying the scrubs prior to washing or wearing could have increased the reports of adverse effects, as the fabrics could have been stiffer and more uncomfortable than they might have been at a later stage in their use.
Our study also has some strengths. Our participants included physicians, residents, nurses, nurse practitioners, and physician assistants. Accordingly, our results should be generalizable to most HCWs. We also confirmed that the scrubs that were tested were nearly sterile prior to use.
In conclusion, we found no evidence suggesting that either of 2 antimicrobial scrubs tested decreased bacterial contamination of HCWs' scrubs or skin after an 8‐hour workday compared to standard scrubs. We also found that, although HCWs are frequently exposed to patients harboring antibiotic‐resistant bacteria, these bacteria were only rarely cultured from HCWs' scrubs or skin.
Healthcare workers' (HCWs) attire becomes contaminated with bacterial pathogens during the course of the workday,[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] and Munoz‐Price et al.[13] recently demonstrated that finding bacterial pathogens on HCWs' white coats correlated with finding the same pathogens on their hands. Because of concern for an association between attire colonization and nosocomial infection, governmental agencies in England and Scotland banned HCWs from wearing white coats or long‐sleeve garments,[14, 15] despite evidence that such an approach does not reduce contamination.[12]
Newly developed antimicrobial textiles have been incorporated into HCW scrubs,[16, 17, 18, 19, 20] and commercial Web sites and product inserts report that these products can reduce bacterial contamination by 80.9% at 8 hours to greater than 99% under laboratory conditions depending on the product and microbe studied.[16, 17, 19] Because there are limited clinical data pertaining to the effectiveness of antimicrobial scrubs, we performed a prospective study designed to determine whether wearing these products reduced bacterial contamination of HCWs' scrubs or skin at the end of an 8‐hour workday.
METHODS
Design
The study was a prospective, unblinded, randomized, controlled trial that was approved by the Colorado Multiple Institutional Review Board and conducted at Denver Health, a university‐affiliated public safety net hospital. No protocol changes occurred during the study.
Participants
Participants included hospitalist physicians, internal medicine residents, physician assistants, nurse practitioners, and nurses who directly cared for patients hospitalized on internal medicine units between March 12, 2012 and August 28, 2012. Participants known to be pregnant or those who refused to participate in the study were excluded.
Intervention
Standard scrubs issued by the hospital were tested along with 2 different antimicrobial scrubs (scrub A and scrub B). Scrub A was made with a polyester microfiber material embedded with a proprietary antimicrobial chemical. Scrub B was a polyestercotton blend scrub that included 2 proprietary antimicrobial chemicals and silver embedded into the fabric. The standard scrub was made of a polyestercotton blend with no antimicrobial properties. All scrubs consisted of pants and a short‐sleeved shirt, with either a pocket at the left breast or lower front surface, and all were tested new prior to any washing or wear. Preliminary cultures were done on 2 scrubs in each group to assess the extent of preuse contamination. All providers were instructed not to wear white coats at any time during the day that they were wearing the scrubs. Providers were not told the type of scrub they received, but the antimicrobial scrubs had a different appearance and texture than the standard scrubs, so blinding was not possible.
Outcomes
The primary end point was the total bacterial colony count of samples obtained from the breast or lower front pocket, the sleeve cuff of the dominant hand, and the pant leg at the midthigh of the dominant leg on all scrubs after an 8‐hour workday. Secondary outcomes were the bacterial colony counts of cultures obtained from the volar surface of the wrists of the HCWs' dominant arm, and the colony counts of methicillin‐resistant Staphylococcus aureus (MRSA), vancomycin‐resistant enterococci (VRE), and resistant Gram‐negative bacteria on the 3 scrub types, all obtained after the 8‐hour workday.
Cultures were collected using a standardized RODAC imprint method[21] with BBL RODAC plates containing blood agar (Becton Dickinson, Sparks, MD). Cultures were incubated in ambient air at 35 to 37C for 18 to 22 hours. After incubation, visible colonies were counted using a dissecting microscope to a maximum of 200 colonies as recommended by the manufacturer. Colonies morphologically consistent with Staphylococcus species were subsequently tested for coagulase using a BactiStaph rapid latex agglutination test (Remel, Lenexa, KS). If positive, these colonies were subcultured to sheep blood agar (Remel) and BBL MRSA CHROMagar (Becton Dickinson) and incubated for an additional 18 to 24 hours. Characteristic growth on blood agar that also produced mauve‐colored colonies on CHROMagar was taken to indicate MRSA. Colonies morphologically suspicious for being VRE were identified and confirmed as VRE using a positive identification and susceptibility panel (Microscan; Siemens, Deerfield, IL). A negative combination panel (Microscan, Siemens) was also used to identify and confirm resistant Gram‐negative rods.
Each participant completed a survey that included questions that identified their occupation, whether they had had contact with patients who were known to be colonized or infected with MRSA, VRE, or resistant Gram‐negative rods during the testing period, and whether they experienced any adverse events that might relate to wearing the uniform.
Sample Size
We assumed that cultures taken from the sleeve of the control scrubs would have a mean ( standard deviation) colony count of 69 (67) based on data from our previous study.[12] Although the companies making the antimicrobial scrubs indicated that their respective products provided between 80.9% at 8 hours and >99% reduction in bacterial colony counts in laboratory settings, we assumed that a 70% decrease in colony count compared with standard scrubs could be clinically important. After adjusting for multiple comparisons and accounting for using nonparametric analyses with an unknown distribution, we estimated a need to recruit 35 subjects in each of 3 groups.
Randomization
The principal investigator and coinvestigators enrolled and consented participants. After obtaining consent, block randomization, stratified by occupation, occurred 1 day prior to the study using a computer‐generated table of random numbers.
Statistics
Data were collected and managed using REDCap (Research Electronic Data Capture; Vanderbilt UniversityThe Institute for Medicine and Public Health, Nashville, TN) electronic data capture tools hosted at Denver Health. REDCap is a secure Web‐based application designed to support data collection for research studies, providing: (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.[22]
Colony counts were compared using a Kruskal‐Wallis 1‐way analysis of variance by ranks. Bonferroni's correction for multiple comparisons resulted in a P<0.01 as indicating statistical significance. Proportions were compared using [2] analysis. All data are presented as medians with interquartile range (IQR) or proportions.
RESULTS
We screened 118 HCWs for participation and randomized 109, 37 in the control and antimicrobial scrub group A, and 35 in antimicrobial scrub group B (during the course of the study we neglected to culture the pockets of 2 participants in the standard scrub group and 2 in antimicrobial scrub group A). Because our primary end point was total colony count from cultures taken from 3 sites, data from these 4 subjects could not be used, and all the data from these 4 subjects were excluded from the primary analysis; 4 additional subjects were subsequently recruited allowing us to meet our block enrollment target (Figure 1). The first and last participants were studied on March 12, 2012 and August 28, 2012, respectively. The trial ended once the defined number of participants was enrolled. The occupations of the 105 participants are summarized in Table 1.
| All Subjects, N=105 | Standard Scrub, n=35 | Antimicrobial Scrub A, n=35 | Antimicrobial Scrub B, n=35 | |
|---|---|---|---|---|
| Healthcare worker type, n (%) | ||||
| Attending physician | 11 (10) | 5 (14) | 3 (9) | 3 (9) |
| Intern/resident | 51 (49) | 17 (49) | 16 (46) | 18 (51) |
| Midlevels | 6 (6) | 2 (6) | 2 (6) | 2 (6) |
| Nurse | 37 (35) | 11 (31) | 14 (40) | 12 (34) |
| Cared for colonized or infected patient with antibiotic resistant organism, n (%) | 55 (52) | 16 (46) | 20 (57) | 19 (54) |
| Number of colonized or infected patients cared for, n (%) | ||||
| 1 | 37 (67) | 10 (63) | 13 (65) | 14 (74) |
| 2 | 11 (20) | 4 (25) | 6 (30) | 1 (5) |
| 3 or more | 6 (11) | 2 (12) | 1 (5) | 3 (16) |
| Unknown | 1 (2) | 0 (0) | 0 (0) | 1 (5) |
Colony counts of all scrubs cultured prior to use never exceeded 10 colonies. The median (IQR) total colony counts from all sites on the scrubs was 99 (66182) for standard scrubs, 137 (84289) for antimicrobial scrub type A, and 138 (62274) for antimicrobial scrub type B (P=0.36). We found no significant differences between the colony counts cultured from any of the individual sites among the 3 groups, regardless of occupation (Table 2). No significant difference was observed with respect to colony counts cultured from the wrist among the 3 study groups (Table 2). Comparisons between groups were planned a priori if a difference across all groups was found. Given the nonsignificant P values across all scrub groups, no further comparisons were made.
| Total (From All Sites on Scrubs) | Sleeve Cuff | Thigh | Wrist | ||
|---|---|---|---|---|---|
| |||||
| All subjects, N=105 | |||||
| Standard scrub | 99 (66182) | 41 (2070) | 20 (944) | 32 (2161) | 16 (540) |
| Antimicrobial scrub A | 137 (84289) | 65 (35117) | 33 (16124) | 41 (1586) | 23 (442) |
| Antimicrobial scrub B | 138 (62274) | 41 (2299) | 21 (941) | 40 (18107) | 15 (654) |
| P value | 0.36 | 0.17 | 0.07 | 0.57 | 0.92 |
| Physicians and midlevels, n=68 | |||||
| Standard scrub | 115.5 (72.5173.5) | 44.5 (2270.5) | 27.5 (10.538.5) | 35 (2362.5) | 24.5 (755) |
| Antimicrobial scrub A | 210 (114289) | 86 (64120) | 39 (18129) | 49 (2486) | 24 (342) |
| Antimicrobial scrub B | 149 (68295) | 52 (26126) | 21 (1069) | 37 (18141) | 19 (872) |
| P value | 0.21 | 0.08 | 0.19 | 0.85 | 0.76 |
| Nurses, n=37 | |||||
| Standard scrub | 89 (31236) | 37 (1348) | 13 (552) | 28 (1342) | 9 (321) |
| Antimicrobial scrub A | 105 (43256) | 45.5 (2258) | 21.5 (1654) | 38.5 (1268) | 17 (643) |
| Antimicrobial scrub B | 91.5 (60174.5) | 27 (1340) | 16 (7.526) | 51 (2186.5) | 10 (3.543.5) |
| P value | 0.86 | 0.39 | 0.19 | 0.49 | 0.41 |
Fifty‐five participants (52%) reported caring for patients who were known to be colonized or infected with an antibiotic‐resistant organism, 16 (46%) randomized to wear standard scrubs, and 20 (57%) and 19 (54%) randomized to wear antimicrobial scrub A or B, respectively (P=0.61). Of these, however, antibiotic‐resistant organisms were only cultured from the scrubs of 2 providers (1 with 1 colony of MRSA from the breast pocket of antimicrobial scrub A, 1 with 1 colony of MRSA cultured from the pocket of antimicrobial scrub B [P=0.55]), and from the wrist of only 1 provider (a multiresistant Gram‐negative rod who wore antimicrobial scrub B).
Adverse Events
Six subjects (5.7%) reported adverse events, all of whom were wearing antimicrobial scrubs (P=0.18). For participants wearing antimicrobial scrub A, 1 (3%) reported itchiness and 2 (6%) reported heaviness or poor breathability. For participants wearing antimicrobial scrub B, 1 (3%) reported redness, 1 (3%) reported itchiness, and 1 (3%) reported heaviness or poor breathability.
DISCUSSION
The important findings of this study are that we found no evidence indicating that either of the 2 antimicrobial scrubs tested reduced bacterial contamination or antibiotic‐resistant contamination on HCWs' scrubs or wrists compared with standard scrubs at the end of an 8‐hour workday, and that despite many HCWs being exposed to patients who were colonized or infected with antibiotic‐resistant bacteria, these organisms were only rarely cultured from their uniforms.
We found that HCWs in all 3 arms of the study had bacterial contamination on their scrubs and skin, consistent with previous studies showing that HCWs' uniforms are frequently contaminated with bacteria, including MRSA, VRE, and other pathogens.[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] We previously found that bacterial contamination of HCWs' uniforms occurs within hours of putting on newly laundered uniforms.[12]
Literature on the effectiveness of antimicrobial HCW uniforms when tested in clinical settings is limited. Bearman and colleagues[23] recently published the results of a study of 31 subjects who wore either standard or antimicrobial scrubs, crossing over every 4 weeks for 4 months, with random culturing done weekly at the beginning and end of a work shift. Scrubs were laundered an average of 1.5 times/week, but the timing of the laundering relative to when cultures were obtained was not reported. Very few isolates of MRSA, Gram‐negative rods, or VRE were found (only 3.9%, 0.4%, and 0.05% of the 2000 samples obtained, respectively), and no differences were observed with respect to the number of HCWs who had antibiotic‐resistant organisms cultured when they were wearing standard versus antimicrobial scrubs. Those who had MRSA cultured, however, had lower mean log colony counts when they were wearing the antimicrobial scrubs. The small number of samples with positive isolates, together with differences in the extent of before‐shift contamination among groups complicates interpreting these data. The authors concluded that a prospective trial was needed. We attempted to include the scrub studied by Bearman and colleagues[23] in our study, but the company had insufficient stock available at the time we tried to purchase the product.
Gross and colleagues[24] found no difference in the mean colony counts of cultures taken from silver‐impregnated versus standard scrubs in a pilot crossover study done with 10 HCWs (although there were trends toward higher colony counts when the subjects wore antimicrobial scrubs).
Antibiotic‐resistant bacteria were only cultured from 3 participants (2.9%) in our current study, compared to 16% of those randomized to wearing white coats in our previous study and 20% of those randomized to wearing standard scrubs.[12] This difference may be explained by several recent studies reporting that rates of MRSA infections in hospitals are decreasing.[25, 26] The rate of hospital‐acquired MRSA infection or colonization at our own institution decreased 80% from 2007 to 2012. At the times of our previous and current studies, providers were expected to wear gowns and gloves when caring for patients as per standard contact precautions. Rates of infection and colonization of VRE and resistant Gram‐negative rods have remained low at our hospital, and our data are consistent with the rates reported on HCWs' uniforms in other studies.[2, 5, 10]
Only 6 of our subjects reported adverse reactions, but all were wearing antimicrobial scrubs (P=0.18). Several of the participants described that the fabrics of the 2 antimicrobial scrubs were heavier and less breathable than the standard scrubs. We believe this difference is more likely to explain the adverse reactions reported than is any type of reaction to the specific chemicals in the fabrics.
Our study has several limitations. Because it was conducted on the general internal medicine units of a single university‐affiliated public hospital, the results may not generalize to other types of institutions or other inpatient services.
As we previously described,[12] the RODAC imprint method only samples a small area of HCWs' uniforms and thus does not represent total bacterial contamination.[21] We specifically cultured areas that are known to be highly contaminated (ie, sleeve cuffs and pockets). Although imprint methods have limitations (as do other methods for culturing clothing), they have been commonly utilized in studies assessing bacterial contamination of HCW clothing.[2, 3, 5]
Although some of the bacterial load we cultured could have come from the providers themselves, previous studies have shown that 80% to 90% of the resistant bacteria cultured from HCWs' attire come from other sources.[1, 2]
Because our sample size was calculated on the basis of being able to detect a difference of 70% in total bacterial colony count, our study was not large enough to exclude a lower level of effectiveness. However, we saw no trends suggesting the antimicrobial products might have a lower level of effectiveness.
We did not observe the hand‐washing practices of the participants, and accordingly, cannot confirm that these practices were the same in each of our 3 study groups. Intermittent, surreptitious monitoring of hand‐washing practices on our internal medicine units over the last several years has found compliance with hand hygiene recommendations varying from 70% to 90%.
Although the participants in our study were not explicitly told to which scrub they were randomized, the colors, appearances, and textures of the antimicrobial fabrics were different from the standard scrubs such that blinding was impossible. Participants wearing antimicrobial scrubs could have changed their hand hygiene practices (ie, less careful hand hygiene). Lack of blinding could also have led to over‐reporting of adverse events by the subjects randomized to wear the antimicrobial scrubs.
In an effort to treat all the scrubs in the same fashion, all were tested new, prior to being washed or previously worn. Studying the scrubs prior to washing or wearing could have increased the reports of adverse effects, as the fabrics could have been stiffer and more uncomfortable than they might have been at a later stage in their use.
Our study also has some strengths. Our participants included physicians, residents, nurses, nurse practitioners, and physician assistants. Accordingly, our results should be generalizable to most HCWs. We also confirmed that the scrubs that were tested were nearly sterile prior to use.
In conclusion, we found no evidence suggesting that either of 2 antimicrobial scrubs tested decreased bacterial contamination of HCWs' scrubs or skin after an 8‐hour workday compared to standard scrubs. We also found that, although HCWs are frequently exposed to patients harboring antibiotic‐resistant bacteria, these bacteria were only rarely cultured from HCWs' scrubs or skin.
- , , , . Contamination of nurses' uniforms with Staphylococcus aureus. Lancet. 1969;2:233–235.
- , , . Contamination of protective clothing and nurses' uniforms in an isolation ward. J Hosp Infect. 1983;4:149–157.
- , , . Microbial flora on doctors' white coats. BMJ. 1991;303:1602–1604.
- . Bacterial contamination of nurses' uniforms: a study. Nursing Stand. 1998;13:37–42.
- , , . Bacterial flora on the white coats of medical students. J Hosp Infect. 2000;45:65–68.
- , , . Bacterial contamination of uniforms. J Hosp Infect. 2001;48:238–241.
- , , , et al. Significance of methicillin‐resistant Staphylococcus aureus (MRSA) survey in a university teaching hospital. J Infect Chemother. 2003;9:172–177.
- . Environmental contamination makes an important contribution to hospital infection. J Hosp Infect. 2007;65(suppl 2):50–54.
- , , , et al. Detection of methicillin‐resistant Staphylococcus aureus and vancomycin‐resistant enterococci on the gowns and gloves of healthcare workers. Infect Control Hosp Epidemiol. 2008;29:583–589.
- , , , , , . Bacterial contamination of health care workers' white coats. Am J Infect Control. 2009;37:101–105.
- , , , , , . Nursing and physician attire as possible source of nosocomial infections. Am J Infect Control. 2011;39:555–559.
- , , , , , . Newly cleaned physician uniforms and infrequently washed white coats have similar rates of bacterial contamination after an 8‐hour workday: a randomized controlled trial. J Hosp Med. 2011;6:177–182.
- , , , et al. Associations between bacterial contamination of health care workers' hands and contamination of white coats and scrubs. Am J Infect Control. 2012;40:e245–e248.
- Department of Health. Uniforms and workwear: an evidence base for developing local policy. National Health Service, 17 September 2007. Available at: http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/Publicationspolicyandguidance/DH_078433. Accessed January 29, 2010.
- Scottish Government Health Directorates. NHS Scotland dress code. Available at: http://www.sehd.scot.nhs.uk/mels/CEL2008_53.pdf. Accessed February 10, 2010.
- Bio Shield Tech Web site. Bio Gardz–unisex scrub top–antimicrobial treatment. Available at: http://www.bioshieldtech.com/Bio_Gardz_Unisex_Scrub_Top_Antimicrobial_Tre_p/sbt01‐r‐p.htm. Accessed January 9, 2013.
- Doc Froc Web site and informational packet. Available at: http://www.docfroc.com. Accessed July 22, 2011.
- Vestagen Web site and informational packet. Available at: http://www.vestagen.com. Accessed July 22, 2011.
- Under Scrub apparel Web site. Testing. Available at: http://underscrub.com/testing. Accessed March 21, 2013.
- MediThreads Web site. Microban FAQ's. Available at: http://medithreads.com/faq/microban‐faqs. Accessed March 21, 2013.
- , , , , . Comparison of the Rodac imprint method to selective enrichment broth for recovery of vancomycin‐resistant enterococci and drug‐resistant Enterobacteriaceae from environmental surfaces. J Clin Microbiol. 2000;38:4646–4648.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
- , , , et al. A crossover trial of antimicrobial scrubs to reduce methicillin‐resistant Staphylococcus aureus burden on healthcare worker apparel. Infect Control Hosp Epidemiol. 2012;33:268–275.
- , , , , . Pilot study on the microbial contamination of conventional vs. silver‐impregnated uniforms worn by ambulance personnel during one week of emergency medical service. GMS Krankenhhyg Interdiszip. 2010;5.pii: Doc09.
- , , , et al. Epidemiology of Staphylococcus aureus blood and skin and soft tissue infections in the US military health system, 2005–2010. JAMA. 2012;308:50–59.
- , , , et al. Health care‐associated invasive MRSA infections, 2005–2008. JAMA. 2010;304:641–648.
- , , , . Contamination of nurses' uniforms with Staphylococcus aureus. Lancet. 1969;2:233–235.
- , , . Contamination of protective clothing and nurses' uniforms in an isolation ward. J Hosp Infect. 1983;4:149–157.
- , , . Microbial flora on doctors' white coats. BMJ. 1991;303:1602–1604.
- . Bacterial contamination of nurses' uniforms: a study. Nursing Stand. 1998;13:37–42.
- , , . Bacterial flora on the white coats of medical students. J Hosp Infect. 2000;45:65–68.
- , , . Bacterial contamination of uniforms. J Hosp Infect. 2001;48:238–241.
- , , , et al. Significance of methicillin‐resistant Staphylococcus aureus (MRSA) survey in a university teaching hospital. J Infect Chemother. 2003;9:172–177.
- . Environmental contamination makes an important contribution to hospital infection. J Hosp Infect. 2007;65(suppl 2):50–54.
- , , , et al. Detection of methicillin‐resistant Staphylococcus aureus and vancomycin‐resistant enterococci on the gowns and gloves of healthcare workers. Infect Control Hosp Epidemiol. 2008;29:583–589.
- , , , , , . Bacterial contamination of health care workers' white coats. Am J Infect Control. 2009;37:101–105.
- , , , , , . Nursing and physician attire as possible source of nosocomial infections. Am J Infect Control. 2011;39:555–559.
- , , , , , . Newly cleaned physician uniforms and infrequently washed white coats have similar rates of bacterial contamination after an 8‐hour workday: a randomized controlled trial. J Hosp Med. 2011;6:177–182.
- , , , et al. Associations between bacterial contamination of health care workers' hands and contamination of white coats and scrubs. Am J Infect Control. 2012;40:e245–e248.
- Department of Health. Uniforms and workwear: an evidence base for developing local policy. National Health Service, 17 September 2007. Available at: http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/Publicationspolicyandguidance/DH_078433. Accessed January 29, 2010.
- Scottish Government Health Directorates. NHS Scotland dress code. Available at: http://www.sehd.scot.nhs.uk/mels/CEL2008_53.pdf. Accessed February 10, 2010.
- Bio Shield Tech Web site. Bio Gardz–unisex scrub top–antimicrobial treatment. Available at: http://www.bioshieldtech.com/Bio_Gardz_Unisex_Scrub_Top_Antimicrobial_Tre_p/sbt01‐r‐p.htm. Accessed January 9, 2013.
- Doc Froc Web site and informational packet. Available at: http://www.docfroc.com. Accessed July 22, 2011.
- Vestagen Web site and informational packet. Available at: http://www.vestagen.com. Accessed July 22, 2011.
- Under Scrub apparel Web site. Testing. Available at: http://underscrub.com/testing. Accessed March 21, 2013.
- MediThreads Web site. Microban FAQ's. Available at: http://medithreads.com/faq/microban‐faqs. Accessed March 21, 2013.
- , , , , . Comparison of the Rodac imprint method to selective enrichment broth for recovery of vancomycin‐resistant enterococci and drug‐resistant Enterobacteriaceae from environmental surfaces. J Clin Microbiol. 2000;38:4646–4648.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
- , , , et al. A crossover trial of antimicrobial scrubs to reduce methicillin‐resistant Staphylococcus aureus burden on healthcare worker apparel. Infect Control Hosp Epidemiol. 2012;33:268–275.
- , , , , . Pilot study on the microbial contamination of conventional vs. silver‐impregnated uniforms worn by ambulance personnel during one week of emergency medical service. GMS Krankenhhyg Interdiszip. 2010;5.pii: Doc09.
- , , , et al. Epidemiology of Staphylococcus aureus blood and skin and soft tissue infections in the US military health system, 2005–2010. JAMA. 2012;308:50–59.
- , , , et al. Health care‐associated invasive MRSA infections, 2005–2008. JAMA. 2010;304:641–648.
© 2013 Society of Hospital Medicine
Curbside vs Formal Consultation
A curbside consultation is an informal process whereby a consultant is asked to provide information or advice about a patient's care without doing a formal assessment of the patient.14 Curbside consultations are common in the practice of medicine2, 3, 5 and are frequently requested by physicians caring for hospitalized patients. Several surveys have documented the quantity of curbside consultations requested of various subspecialties, the types of questions asked, the time it takes to respond, and physicians' perceptions about the quality of the information exchanged.111 While curbside consultations have a number of advantages, physicians' perceptions are that the information conveyed may be inaccurate or incomplete and that the advice offered may be erroneous.13, 5, 10, 12, 13
Cartmill and White14 performed a random audit of 10% of the telephone referrals they received for neurosurgical consultation over a 1‐year period and noted discrepancies between the Glascow Coma Scores reported during the telephone referrals and those noted in the medical records, but the frequency of these discrepancies was not reported. To our knowledge, no studies have compared the quality of the information provided in curbside consultations with that obtained in formal consultations that included direct face‐to‐face patient evaluations and primary data collection, and whether the advice provided in curbside and formal consultations on the same patient differed.
We performed a prospective cohort study to compare the information received by hospitalists during curbside consultations on hospitalized patients, with that obtained from formal consultations done the same day on the same patients, by different hospitalists who were unaware of any details regarding the curbside consultation. We also compared the advice provided by the 2 hospitalists following their curbside and formal consultations. Our hypotheses were that the information received during curbside consultations was frequently inaccurate or incomplete, that the recommendations made after the formal consultation would frequently differ from those made in the curbside consultation, and that these differences would have important implications on patient care.
METHODS
This was a quality improvement study conducted at Denver Health, a 500‐bed university‐affiliated urban safety net hospital from January 10, 2011 to January 9, 2012. The study design was a prospective cohort that included all curbside consultations on hospitalized patients received between 7 AM and 3 PM, on intermittently selected weekdays, by the Internal Medicine Consultation Service that was staffed by 18 hospitalists. Data were collected intermittently based upon hospitalist availability and was done to limit potential alterations in the consulting practices of the providers requesting consultations.
Consultations were defined as being curbside when the consulting provider asked for advice, suggestions, or opinions about a patient's care but did not ask the hospitalist to see the patient.15, 15 Consultations pertaining to administrative issues (eg, whether a patient should be admitted to an intensive care bed as opposed to an acute care floor bed) or on patients who were already being followed by a hospitalist were excluded.
The hospitalist receiving the curbside consultation was allowed to ask questions as they normally would, but could not verify the accuracy of the information received (eg, could not review any portion of the patient's medical record, such as notes or lab data). A standardized data collection sheet was used to record the service and level of training of the requesting provider, the medical issue(s) of concern, all clinical data offered by the provider, the number of questions asked by the hospitalist of the provider, and whether, on the basis of the information provided, the hospitalist felt that the question(s) being asked was (were) of sufficient complexity that a formal consultation should occur. The hospitalist then offered advice based upon the information given during the curbside consultation.
After completing the curbside consultation, the hospitalist requested verbal permission from the requesting provider to perform a formal consultation. If the request was approved, the hospitalist performing the curbside consultation contacted a different hospitalist who performed the formal consultation within the next few hours. The only information given to the second hospitalist was the patient's identifiers and the clinical question(s) being asked. The formal consultation included a complete face‐to‐face history and physical examination, a review of the patient's medical record, documentation of the provider's findings, and recommendations for care.
Upon completion of the formal consultation, the hospitalists who performed the curbside and the formal consultations met to review the advice each gave to the requesting provider and the information on which this advice was based. The 2 hospitalists jointly determined the following: (a) whether the information received during the curbside consultation was correct and complete, (b) whether the advice provided in the formal consultation differed from that provided in the curbside consultation, (c) whether the advice provided in the formal consultation dealt with issues other than one(s) leading to the curbside consultation, (d) whether differences in the recommendations given in the curbside versus the formal consultation changed patient management in a meaningful way, and (e) whether the curbside consultation alone was felt to be sufficient.
Information obtained by the hospitalist performing the formal consultation that was different from, or not included in, the information recorded during the curbside consultation was considered to be incorrect or incomplete, respectively. A change in management was defined as an alteration in the direction or type of care that the patient would have received as a result of the advice being given. A pulmonary and critical care physician, with >35 years of experience in inpatient medicine, reviewed the information provided in the curbside and formal consultations, and independently assessed whether the curbside consultation alone would have been sufficient and whether the formal consultation changed management.
Curbside consultations were neither solicited nor discouraged during the course of the study. The provider requesting the curbside consultation was not informed or debriefed about the study in an attempt to avoid affecting future consultation practices from that provider or service.
Associations were sought between the frequency of inaccurate or incomplete data and the requesting service and provider, the consultative category and medical issue, the number of questions asked by the hospitalist during the curbside consultation, and whether the hospitalist doing the curbside consultation thought that formal consultation was needed. A chi‐square test was used to analyze all associations. A P value of <0.05 was considered significant. All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc, Cary, NC) software. The study was approved by the Colorado Multiple Institutional Review Board.
RESULTS
Fifty curbside consultations were requested on a total of 215 study days. The requesting service declined formal consultation in 3 instances, leaving 47 curbside consultations that had a formal consultation. Curbside consultations came from a variety of services and providers, and addressed a variety of issues and concerns (Table 1).
| Curbside Consultations, N (%) | |
|---|---|
| 47 (100) | |
| |
| Requesting service | |
| Psychiatry | 21 (45) |
| Emergency Department | 9 (19) |
| Obstetrics/Gynecology | 5 (11) |
| Neurology | 4 (8) |
| Other (Orthopedics, Anesthesia, General Surgery, Neurosurgery, and Interventional Radiology) | 8 (17) |
| Requesting provider | |
| Resident | 25 (53) |
| Intern | 8 (17) |
| Attending | 9 (19) |
| Other | 5 (11) |
| Consultative issue* | |
| Diagnosis | 10 (21) |
| Treatment | 29 (62) |
| Evaluation | 20 (43) |
| Discharge | 13 (28) |
| Lab interpretation | 4 (9) |
| Medical concern* | |
| Cardiac | 27 (57) |
| Endocrine | 17 (36) |
| Infectious disease | 9 (19) |
| Pulmonary | 8 (17) |
| Gastroenterology | 6 (13) |
| Fluid and electrolyte | 6 (13) |
| Others | 23 (49) |
The hospitalists asked 0 to 2 questions during 8/47 (17%) of the curbside consultations, 3 to 5 questions during 26/47 (55%) consultations, and more than 5 questions during 13/47 (28%). Based on the information received during the curbside consultations, the hospitalists thought that the curbside consultations were insufficient for 18/47 (38%) of patients. In all instances, the opinions of the 2 hospitalists concurred with respect to this conclusion, and the independent reviewer agreed with this assessment in 17 of these 18 (94%).
The advice rendered in the formal consultations differed from that provided in 26/47 (55%) of the curbside consultations, and the formal consultation was thought to have changed management for 28/47 (60%) of patients (Table 2). The independent reviewer thought that the advice provided in the formal consultations changed management in 29/47 (62%) of the cases, and in 24/28 cases (86%) where the hospitalist felt that the formal consult changed management.
| Curbside Consultations, N (%) | |||
|---|---|---|---|
| Total | Accurate and Complete | Inaccurate or Incomplete | |
| 47 (100) | 23 (49) | 24 (51) | |
| |||
| Advice in formal consultation differed from advice in curbside consultation | 26 (55) | 7 (30) | 19 (79)* |
| Formal consultation changed management | 28 (60) | 6 (26) | 22 (92) |
| Minor change | 18 (64) | 6 (100) | 12 (55) |
| Major change | 10 (36) | 0 (0) | 10 (45) |
| Curbside consultation insufficient | 18 (38) | 2 (9) | 16 (67) |
Information was felt to be inaccurate or incomplete in 24/47 (51%) of the curbside consultations (13/47 inaccurate, 16/47 incomplete, 5/47 both inaccurate and incomplete), and when inaccurate or incomplete information was obtained, the advice given in the formal consultations more commonly differed from that provided in the curbside consultation (19/24, 79% vs 7/23, 30%; P < 0.001), and was more commonly felt to change management (22/24, 92% vs 6/23, 26%; P < 0.0001) (Table 2). No association was found between whether the curbside consultation contained complete or accurate information and the consulting service from which the curbside originated, the consulting provider, the consultative aspect(s) or medical issue(s) addressed, the number of questions asked by the hospitalist during the curbside consultation, nor whether the hospitalists felt that a formal consultation was needed.
DISCUSSION
The important findings of this study are that (a) the recommendations made by hospitalists in curbside versus formal consultations on the same patient frequently differ, (b) these differences frequently result in changes in clinical management, (c) the information presented in curbside consultations by providers is frequently inaccurate or incomplete, regardless of the providers specialty or seniority, (d) when inaccurate or incomplete information is received, the recommendations made in curbside and formal consultations differ more frequently, and (e) we found no way to predict whether the information provided in a curbside consultation was likely to be inaccurate or incomplete.
Our hospitalists thought that 38% of the curbside consultations they received should have had formal consultations. Manian and McKinsey7 reported that as many as 53% of questions asked of infectious disease consultants were thought to be too complex to be addressed in an informal consultation. Others, however, report that only 11%33% of curbside consultations were thought to require formal consultation.1, 9, 10, 16 Our hospitalists asked 3 or more questions of the consulting providers in more than 80% of the curbside consultations, suggesting that the curbside consultations we received might have had a higher complexity than those seen by others.
Our finding that information provided in curbside consultation was frequently inaccurate or incomplete is consistent with a number of previous studies reporting physicians' perceptions of the accuracy of curbside consultations.2, 3 Hospital medicine is not likely to be the only discipline affected by inaccurate curbside consultation practices, as surveys of specialists in infectious disease, gynecology, and neurosurgery report that practitioners in these disciplines have similar concerns.1, 10, 14 In a survey returned by 34 physicians, Myers1 found that 50% thought the information exchanged during curbside consultations was inaccurate, leading him to conclude that inaccuracies presented during curbside consultations required further study.
We found no way of predicting whether curbside consultations were likely to include inaccurate or incomplete information. This observation is consistent with the results of Bergus et al16 who found that the frequency of curbside consultations being converted to formal consultations was independent of the training status of the consulting physician, and with the data of Myers1 who found no way of predicting the likelihood that a curbside consultation should be converted to a formal consultation.
We found that formal consultations resulted in management changes more often than differences in recommendations (ie, 60% vs 55%, respectively). This small difference occurred because, on occasion, the formal consultations found issues to address other than the one(s) for which the curbside consultation was requested. In the majority of these instances, the management changes were minor and the curbside consultation was still felt to be sufficient.
In some instances, the advice given after the curbside and the formal consultations differed to only a minor extent (eg, varying recommendations for oral diabetes management). In other instances, however, the advice differed substantially (eg, change in antibiotic management in a septic patient with a multidrug resistant organism, when the original curbside question was for when to order a follow‐up chest roentgenogram for hypoxia; see Supporting Information, Appendix, in the online version of this article). In 26 patients (55%), formal consultation resulted in different medications being started or stopped, additional tests being performed, or different decisions being made about admission versus discharge.
Our study has a number of strengths. First, while a number of reports document that physicians' perceptions are that curbside consultations frequently contain errors,2, 3, 5, 12 to our knowledge this is the first study that prospectively compared the information collected and advice given in curbside versus formal consultation. Second, while this study was conducted as a quality improvement project, thereby requiring us to conclude that the results are not generalizable, the data presented were collected by 18 different hospitalists, reducing the potential of bias from an individual provider's knowledge base or practice. Third, there was excellent agreement between the independent reviewer and the 2 hospitalists who performed the curbside and formal consultations regarding whether a curbside consultation would have been sufficient, and whether the formal consultation changed patient management. Fourth, the study was conducted over a 1‐year period, which should have reduced potential bias arising from the increasing experience of residents requesting consultations as their training progressed.
Our study has several limitations. First, the number of curbside consultations we received during the study period (50 over 215 days) was lower than anticipated, and lower than the rates of consultation reported by others.1, 7, 9 This likely relates to the fact that, prior to beginning the study, Denver Health hospitalists already provided mandatory consultations for several surgical services (thereby reducing the number of curbside consultations received from these services), because curbside consultations received during evenings, nights, and weekends were not included in the study for reasons of convenience, and because we excluded all administrative curbside consultations. Our hospitalist service also provides consultative services 24 hours a day, thereby reducing the number of consultations received during daytime hours. Second, the frequency with which curbside consultations included inaccurate or incomplete information might be higher than what occurs in other hospitals, as Denver Health is an urban, university‐affiliated public hospital and the patients encountered may be more complex and trainees may be less adept at recognizing the information that would facilitate accurate curbside consultations (although we found no difference in the frequency with which inaccurate or incomplete information was provided as a function of the seniority of the requesting physician). Third, the disparity between curbside and formal consultations that we observed could have been biased by the Hawthorne effect. We attempted to address this by not providing the hospitalists who did the formal consultation with any information collected by the hospitalist involved with the curbside consultation, and by comparing the conclusions reached by the hospitalists performing the curbside and formal consultations with those of a third party reviewer. Fourth, while we found no association between the frequency of curbside consultations in which information was inaccurate or incomplete and the consulting service, there could be a selection bias of the consulting service requesting the curbside consultations as a result of the mandatory consultations already provided by our hospitalists. Finally, our study was not designed or adequately powered to determine why curbside consultations frequently have inaccurate or incomplete information.
In summary, we found that the information provided to hospitalists during a curbside consultation was often inaccurate and incomplete, and that these problems with information exchange adversely affected the accuracy of the resulting recommendations. While there are a number of advantages to curbside consultations,1, 3, 7, 10, 12, 13 our findings indicate that the risk associated with this practice is substantial.
Acknowledgements
Disclosure: Nothing to report.
- .Curbside consultation in infectious diseases: a prospective study.J Infect Dis.1984;150:797–802.
- ,,.Physicians' experiences and beliefs regarding informal consultation.JAMA.1998;280:900–904.
- ,,.Curbside consultation practices and attitudes among primary care physicians and medical subspecialists.JAMA.1998;280:905–909.
- ,,,,,.The complexity, relative value, and financial worth of curbside consultations in an academic infectious diseases unit.Clin Infect Dis.2010;51:651–655.
- ,.Curbside consultations. A closer look at a common practice.JAMA.1996;275:145–147.
- ,,,.Informal advice‐ and information‐seeking between physicians.J Med Educ.1981;56;174–180.
- ,.A prospective study of 2,092 “curbside” questions asked of two infectious disease consultants in private practice in the midwest.Clin Infect Dis.1996;22:303–307.
- ,,,,.Curbside consultation in endocrine practice: a prospective observational study.Endocrinologist.1996;6:328–331.
- ,,.Informal consultations provided to general internists by the gastroenterology department of an HMO.J Gen Intern Med.1998;13:435–438.
- .“Curbside” consultations in gynecologic oncology: a closer look at a common practice.Gynecol Oncol.1999;74:456–459.
- ,,, et al.Informal consultations in infectious diseases and clinical microbiology practice.Clin Microbiol Infect.2003;9:724–726.
- .Curbside consultations and the viaduct effect.JAMA.1998;280:929–930.
- .What do we really need to know about consultation and referral?J Gen Intern Med.1998;13:497–498.
- ,.Telephone advice for neurosurgical referrals. Who assumes duty of care?Br J Neurosurg.2001;15:453–455.
- ,.Malpractice liability for informal consultations.Fam Med.2003;35:476–481.
- ,,,.Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?Arch Fam Med.2000;9:541–547.
A curbside consultation is an informal process whereby a consultant is asked to provide information or advice about a patient's care without doing a formal assessment of the patient.14 Curbside consultations are common in the practice of medicine2, 3, 5 and are frequently requested by physicians caring for hospitalized patients. Several surveys have documented the quantity of curbside consultations requested of various subspecialties, the types of questions asked, the time it takes to respond, and physicians' perceptions about the quality of the information exchanged.111 While curbside consultations have a number of advantages, physicians' perceptions are that the information conveyed may be inaccurate or incomplete and that the advice offered may be erroneous.13, 5, 10, 12, 13
Cartmill and White14 performed a random audit of 10% of the telephone referrals they received for neurosurgical consultation over a 1‐year period and noted discrepancies between the Glascow Coma Scores reported during the telephone referrals and those noted in the medical records, but the frequency of these discrepancies was not reported. To our knowledge, no studies have compared the quality of the information provided in curbside consultations with that obtained in formal consultations that included direct face‐to‐face patient evaluations and primary data collection, and whether the advice provided in curbside and formal consultations on the same patient differed.
We performed a prospective cohort study to compare the information received by hospitalists during curbside consultations on hospitalized patients, with that obtained from formal consultations done the same day on the same patients, by different hospitalists who were unaware of any details regarding the curbside consultation. We also compared the advice provided by the 2 hospitalists following their curbside and formal consultations. Our hypotheses were that the information received during curbside consultations was frequently inaccurate or incomplete, that the recommendations made after the formal consultation would frequently differ from those made in the curbside consultation, and that these differences would have important implications on patient care.
METHODS
This was a quality improvement study conducted at Denver Health, a 500‐bed university‐affiliated urban safety net hospital from January 10, 2011 to January 9, 2012. The study design was a prospective cohort that included all curbside consultations on hospitalized patients received between 7 AM and 3 PM, on intermittently selected weekdays, by the Internal Medicine Consultation Service that was staffed by 18 hospitalists. Data were collected intermittently based upon hospitalist availability and was done to limit potential alterations in the consulting practices of the providers requesting consultations.
Consultations were defined as being curbside when the consulting provider asked for advice, suggestions, or opinions about a patient's care but did not ask the hospitalist to see the patient.15, 15 Consultations pertaining to administrative issues (eg, whether a patient should be admitted to an intensive care bed as opposed to an acute care floor bed) or on patients who were already being followed by a hospitalist were excluded.
The hospitalist receiving the curbside consultation was allowed to ask questions as they normally would, but could not verify the accuracy of the information received (eg, could not review any portion of the patient's medical record, such as notes or lab data). A standardized data collection sheet was used to record the service and level of training of the requesting provider, the medical issue(s) of concern, all clinical data offered by the provider, the number of questions asked by the hospitalist of the provider, and whether, on the basis of the information provided, the hospitalist felt that the question(s) being asked was (were) of sufficient complexity that a formal consultation should occur. The hospitalist then offered advice based upon the information given during the curbside consultation.
After completing the curbside consultation, the hospitalist requested verbal permission from the requesting provider to perform a formal consultation. If the request was approved, the hospitalist performing the curbside consultation contacted a different hospitalist who performed the formal consultation within the next few hours. The only information given to the second hospitalist was the patient's identifiers and the clinical question(s) being asked. The formal consultation included a complete face‐to‐face history and physical examination, a review of the patient's medical record, documentation of the provider's findings, and recommendations for care.
Upon completion of the formal consultation, the hospitalists who performed the curbside and the formal consultations met to review the advice each gave to the requesting provider and the information on which this advice was based. The 2 hospitalists jointly determined the following: (a) whether the information received during the curbside consultation was correct and complete, (b) whether the advice provided in the formal consultation differed from that provided in the curbside consultation, (c) whether the advice provided in the formal consultation dealt with issues other than one(s) leading to the curbside consultation, (d) whether differences in the recommendations given in the curbside versus the formal consultation changed patient management in a meaningful way, and (e) whether the curbside consultation alone was felt to be sufficient.
Information obtained by the hospitalist performing the formal consultation that was different from, or not included in, the information recorded during the curbside consultation was considered to be incorrect or incomplete, respectively. A change in management was defined as an alteration in the direction or type of care that the patient would have received as a result of the advice being given. A pulmonary and critical care physician, with >35 years of experience in inpatient medicine, reviewed the information provided in the curbside and formal consultations, and independently assessed whether the curbside consultation alone would have been sufficient and whether the formal consultation changed management.
Curbside consultations were neither solicited nor discouraged during the course of the study. The provider requesting the curbside consultation was not informed or debriefed about the study in an attempt to avoid affecting future consultation practices from that provider or service.
Associations were sought between the frequency of inaccurate or incomplete data and the requesting service and provider, the consultative category and medical issue, the number of questions asked by the hospitalist during the curbside consultation, and whether the hospitalist doing the curbside consultation thought that formal consultation was needed. A chi‐square test was used to analyze all associations. A P value of <0.05 was considered significant. All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc, Cary, NC) software. The study was approved by the Colorado Multiple Institutional Review Board.
RESULTS
Fifty curbside consultations were requested on a total of 215 study days. The requesting service declined formal consultation in 3 instances, leaving 47 curbside consultations that had a formal consultation. Curbside consultations came from a variety of services and providers, and addressed a variety of issues and concerns (Table 1).
| Curbside Consultations, N (%) | |
|---|---|
| 47 (100) | |
| |
| Requesting service | |
| Psychiatry | 21 (45) |
| Emergency Department | 9 (19) |
| Obstetrics/Gynecology | 5 (11) |
| Neurology | 4 (8) |
| Other (Orthopedics, Anesthesia, General Surgery, Neurosurgery, and Interventional Radiology) | 8 (17) |
| Requesting provider | |
| Resident | 25 (53) |
| Intern | 8 (17) |
| Attending | 9 (19) |
| Other | 5 (11) |
| Consultative issue* | |
| Diagnosis | 10 (21) |
| Treatment | 29 (62) |
| Evaluation | 20 (43) |
| Discharge | 13 (28) |
| Lab interpretation | 4 (9) |
| Medical concern* | |
| Cardiac | 27 (57) |
| Endocrine | 17 (36) |
| Infectious disease | 9 (19) |
| Pulmonary | 8 (17) |
| Gastroenterology | 6 (13) |
| Fluid and electrolyte | 6 (13) |
| Others | 23 (49) |
The hospitalists asked 0 to 2 questions during 8/47 (17%) of the curbside consultations, 3 to 5 questions during 26/47 (55%) consultations, and more than 5 questions during 13/47 (28%). Based on the information received during the curbside consultations, the hospitalists thought that the curbside consultations were insufficient for 18/47 (38%) of patients. In all instances, the opinions of the 2 hospitalists concurred with respect to this conclusion, and the independent reviewer agreed with this assessment in 17 of these 18 (94%).
The advice rendered in the formal consultations differed from that provided in 26/47 (55%) of the curbside consultations, and the formal consultation was thought to have changed management for 28/47 (60%) of patients (Table 2). The independent reviewer thought that the advice provided in the formal consultations changed management in 29/47 (62%) of the cases, and in 24/28 cases (86%) where the hospitalist felt that the formal consult changed management.
| Curbside Consultations, N (%) | |||
|---|---|---|---|
| Total | Accurate and Complete | Inaccurate or Incomplete | |
| 47 (100) | 23 (49) | 24 (51) | |
| |||
| Advice in formal consultation differed from advice in curbside consultation | 26 (55) | 7 (30) | 19 (79)* |
| Formal consultation changed management | 28 (60) | 6 (26) | 22 (92) |
| Minor change | 18 (64) | 6 (100) | 12 (55) |
| Major change | 10 (36) | 0 (0) | 10 (45) |
| Curbside consultation insufficient | 18 (38) | 2 (9) | 16 (67) |
Information was felt to be inaccurate or incomplete in 24/47 (51%) of the curbside consultations (13/47 inaccurate, 16/47 incomplete, 5/47 both inaccurate and incomplete), and when inaccurate or incomplete information was obtained, the advice given in the formal consultations more commonly differed from that provided in the curbside consultation (19/24, 79% vs 7/23, 30%; P < 0.001), and was more commonly felt to change management (22/24, 92% vs 6/23, 26%; P < 0.0001) (Table 2). No association was found between whether the curbside consultation contained complete or accurate information and the consulting service from which the curbside originated, the consulting provider, the consultative aspect(s) or medical issue(s) addressed, the number of questions asked by the hospitalist during the curbside consultation, nor whether the hospitalists felt that a formal consultation was needed.
DISCUSSION
The important findings of this study are that (a) the recommendations made by hospitalists in curbside versus formal consultations on the same patient frequently differ, (b) these differences frequently result in changes in clinical management, (c) the information presented in curbside consultations by providers is frequently inaccurate or incomplete, regardless of the providers specialty or seniority, (d) when inaccurate or incomplete information is received, the recommendations made in curbside and formal consultations differ more frequently, and (e) we found no way to predict whether the information provided in a curbside consultation was likely to be inaccurate or incomplete.
Our hospitalists thought that 38% of the curbside consultations they received should have had formal consultations. Manian and McKinsey7 reported that as many as 53% of questions asked of infectious disease consultants were thought to be too complex to be addressed in an informal consultation. Others, however, report that only 11%33% of curbside consultations were thought to require formal consultation.1, 9, 10, 16 Our hospitalists asked 3 or more questions of the consulting providers in more than 80% of the curbside consultations, suggesting that the curbside consultations we received might have had a higher complexity than those seen by others.
Our finding that information provided in curbside consultation was frequently inaccurate or incomplete is consistent with a number of previous studies reporting physicians' perceptions of the accuracy of curbside consultations.2, 3 Hospital medicine is not likely to be the only discipline affected by inaccurate curbside consultation practices, as surveys of specialists in infectious disease, gynecology, and neurosurgery report that practitioners in these disciplines have similar concerns.1, 10, 14 In a survey returned by 34 physicians, Myers1 found that 50% thought the information exchanged during curbside consultations was inaccurate, leading him to conclude that inaccuracies presented during curbside consultations required further study.
We found no way of predicting whether curbside consultations were likely to include inaccurate or incomplete information. This observation is consistent with the results of Bergus et al16 who found that the frequency of curbside consultations being converted to formal consultations was independent of the training status of the consulting physician, and with the data of Myers1 who found no way of predicting the likelihood that a curbside consultation should be converted to a formal consultation.
We found that formal consultations resulted in management changes more often than differences in recommendations (ie, 60% vs 55%, respectively). This small difference occurred because, on occasion, the formal consultations found issues to address other than the one(s) for which the curbside consultation was requested. In the majority of these instances, the management changes were minor and the curbside consultation was still felt to be sufficient.
In some instances, the advice given after the curbside and the formal consultations differed to only a minor extent (eg, varying recommendations for oral diabetes management). In other instances, however, the advice differed substantially (eg, change in antibiotic management in a septic patient with a multidrug resistant organism, when the original curbside question was for when to order a follow‐up chest roentgenogram for hypoxia; see Supporting Information, Appendix, in the online version of this article). In 26 patients (55%), formal consultation resulted in different medications being started or stopped, additional tests being performed, or different decisions being made about admission versus discharge.
Our study has a number of strengths. First, while a number of reports document that physicians' perceptions are that curbside consultations frequently contain errors,2, 3, 5, 12 to our knowledge this is the first study that prospectively compared the information collected and advice given in curbside versus formal consultation. Second, while this study was conducted as a quality improvement project, thereby requiring us to conclude that the results are not generalizable, the data presented were collected by 18 different hospitalists, reducing the potential of bias from an individual provider's knowledge base or practice. Third, there was excellent agreement between the independent reviewer and the 2 hospitalists who performed the curbside and formal consultations regarding whether a curbside consultation would have been sufficient, and whether the formal consultation changed patient management. Fourth, the study was conducted over a 1‐year period, which should have reduced potential bias arising from the increasing experience of residents requesting consultations as their training progressed.
Our study has several limitations. First, the number of curbside consultations we received during the study period (50 over 215 days) was lower than anticipated, and lower than the rates of consultation reported by others.1, 7, 9 This likely relates to the fact that, prior to beginning the study, Denver Health hospitalists already provided mandatory consultations for several surgical services (thereby reducing the number of curbside consultations received from these services), because curbside consultations received during evenings, nights, and weekends were not included in the study for reasons of convenience, and because we excluded all administrative curbside consultations. Our hospitalist service also provides consultative services 24 hours a day, thereby reducing the number of consultations received during daytime hours. Second, the frequency with which curbside consultations included inaccurate or incomplete information might be higher than what occurs in other hospitals, as Denver Health is an urban, university‐affiliated public hospital and the patients encountered may be more complex and trainees may be less adept at recognizing the information that would facilitate accurate curbside consultations (although we found no difference in the frequency with which inaccurate or incomplete information was provided as a function of the seniority of the requesting physician). Third, the disparity between curbside and formal consultations that we observed could have been biased by the Hawthorne effect. We attempted to address this by not providing the hospitalists who did the formal consultation with any information collected by the hospitalist involved with the curbside consultation, and by comparing the conclusions reached by the hospitalists performing the curbside and formal consultations with those of a third party reviewer. Fourth, while we found no association between the frequency of curbside consultations in which information was inaccurate or incomplete and the consulting service, there could be a selection bias of the consulting service requesting the curbside consultations as a result of the mandatory consultations already provided by our hospitalists. Finally, our study was not designed or adequately powered to determine why curbside consultations frequently have inaccurate or incomplete information.
In summary, we found that the information provided to hospitalists during a curbside consultation was often inaccurate and incomplete, and that these problems with information exchange adversely affected the accuracy of the resulting recommendations. While there are a number of advantages to curbside consultations,1, 3, 7, 10, 12, 13 our findings indicate that the risk associated with this practice is substantial.
Acknowledgements
Disclosure: Nothing to report.
A curbside consultation is an informal process whereby a consultant is asked to provide information or advice about a patient's care without doing a formal assessment of the patient.14 Curbside consultations are common in the practice of medicine2, 3, 5 and are frequently requested by physicians caring for hospitalized patients. Several surveys have documented the quantity of curbside consultations requested of various subspecialties, the types of questions asked, the time it takes to respond, and physicians' perceptions about the quality of the information exchanged.111 While curbside consultations have a number of advantages, physicians' perceptions are that the information conveyed may be inaccurate or incomplete and that the advice offered may be erroneous.13, 5, 10, 12, 13
Cartmill and White14 performed a random audit of 10% of the telephone referrals they received for neurosurgical consultation over a 1‐year period and noted discrepancies between the Glascow Coma Scores reported during the telephone referrals and those noted in the medical records, but the frequency of these discrepancies was not reported. To our knowledge, no studies have compared the quality of the information provided in curbside consultations with that obtained in formal consultations that included direct face‐to‐face patient evaluations and primary data collection, and whether the advice provided in curbside and formal consultations on the same patient differed.
We performed a prospective cohort study to compare the information received by hospitalists during curbside consultations on hospitalized patients, with that obtained from formal consultations done the same day on the same patients, by different hospitalists who were unaware of any details regarding the curbside consultation. We also compared the advice provided by the 2 hospitalists following their curbside and formal consultations. Our hypotheses were that the information received during curbside consultations was frequently inaccurate or incomplete, that the recommendations made after the formal consultation would frequently differ from those made in the curbside consultation, and that these differences would have important implications on patient care.
METHODS
This was a quality improvement study conducted at Denver Health, a 500‐bed university‐affiliated urban safety net hospital from January 10, 2011 to January 9, 2012. The study design was a prospective cohort that included all curbside consultations on hospitalized patients received between 7 AM and 3 PM, on intermittently selected weekdays, by the Internal Medicine Consultation Service that was staffed by 18 hospitalists. Data were collected intermittently based upon hospitalist availability and was done to limit potential alterations in the consulting practices of the providers requesting consultations.
Consultations were defined as being curbside when the consulting provider asked for advice, suggestions, or opinions about a patient's care but did not ask the hospitalist to see the patient.15, 15 Consultations pertaining to administrative issues (eg, whether a patient should be admitted to an intensive care bed as opposed to an acute care floor bed) or on patients who were already being followed by a hospitalist were excluded.
The hospitalist receiving the curbside consultation was allowed to ask questions as they normally would, but could not verify the accuracy of the information received (eg, could not review any portion of the patient's medical record, such as notes or lab data). A standardized data collection sheet was used to record the service and level of training of the requesting provider, the medical issue(s) of concern, all clinical data offered by the provider, the number of questions asked by the hospitalist of the provider, and whether, on the basis of the information provided, the hospitalist felt that the question(s) being asked was (were) of sufficient complexity that a formal consultation should occur. The hospitalist then offered advice based upon the information given during the curbside consultation.
After completing the curbside consultation, the hospitalist requested verbal permission from the requesting provider to perform a formal consultation. If the request was approved, the hospitalist performing the curbside consultation contacted a different hospitalist who performed the formal consultation within the next few hours. The only information given to the second hospitalist was the patient's identifiers and the clinical question(s) being asked. The formal consultation included a complete face‐to‐face history and physical examination, a review of the patient's medical record, documentation of the provider's findings, and recommendations for care.
Upon completion of the formal consultation, the hospitalists who performed the curbside and the formal consultations met to review the advice each gave to the requesting provider and the information on which this advice was based. The 2 hospitalists jointly determined the following: (a) whether the information received during the curbside consultation was correct and complete, (b) whether the advice provided in the formal consultation differed from that provided in the curbside consultation, (c) whether the advice provided in the formal consultation dealt with issues other than one(s) leading to the curbside consultation, (d) whether differences in the recommendations given in the curbside versus the formal consultation changed patient management in a meaningful way, and (e) whether the curbside consultation alone was felt to be sufficient.
Information obtained by the hospitalist performing the formal consultation that was different from, or not included in, the information recorded during the curbside consultation was considered to be incorrect or incomplete, respectively. A change in management was defined as an alteration in the direction or type of care that the patient would have received as a result of the advice being given. A pulmonary and critical care physician, with >35 years of experience in inpatient medicine, reviewed the information provided in the curbside and formal consultations, and independently assessed whether the curbside consultation alone would have been sufficient and whether the formal consultation changed management.
Curbside consultations were neither solicited nor discouraged during the course of the study. The provider requesting the curbside consultation was not informed or debriefed about the study in an attempt to avoid affecting future consultation practices from that provider or service.
Associations were sought between the frequency of inaccurate or incomplete data and the requesting service and provider, the consultative category and medical issue, the number of questions asked by the hospitalist during the curbside consultation, and whether the hospitalist doing the curbside consultation thought that formal consultation was needed. A chi‐square test was used to analyze all associations. A P value of <0.05 was considered significant. All analyses were performed using SAS Enterprise Guide 4.3 (SAS Institute, Inc, Cary, NC) software. The study was approved by the Colorado Multiple Institutional Review Board.
RESULTS
Fifty curbside consultations were requested on a total of 215 study days. The requesting service declined formal consultation in 3 instances, leaving 47 curbside consultations that had a formal consultation. Curbside consultations came from a variety of services and providers, and addressed a variety of issues and concerns (Table 1).
| Curbside Consultations, N (%) | |
|---|---|
| 47 (100) | |
| |
| Requesting service | |
| Psychiatry | 21 (45) |
| Emergency Department | 9 (19) |
| Obstetrics/Gynecology | 5 (11) |
| Neurology | 4 (8) |
| Other (Orthopedics, Anesthesia, General Surgery, Neurosurgery, and Interventional Radiology) | 8 (17) |
| Requesting provider | |
| Resident | 25 (53) |
| Intern | 8 (17) |
| Attending | 9 (19) |
| Other | 5 (11) |
| Consultative issue* | |
| Diagnosis | 10 (21) |
| Treatment | 29 (62) |
| Evaluation | 20 (43) |
| Discharge | 13 (28) |
| Lab interpretation | 4 (9) |
| Medical concern* | |
| Cardiac | 27 (57) |
| Endocrine | 17 (36) |
| Infectious disease | 9 (19) |
| Pulmonary | 8 (17) |
| Gastroenterology | 6 (13) |
| Fluid and electrolyte | 6 (13) |
| Others | 23 (49) |
The hospitalists asked 0 to 2 questions during 8/47 (17%) of the curbside consultations, 3 to 5 questions during 26/47 (55%) consultations, and more than 5 questions during 13/47 (28%). Based on the information received during the curbside consultations, the hospitalists thought that the curbside consultations were insufficient for 18/47 (38%) of patients. In all instances, the opinions of the 2 hospitalists concurred with respect to this conclusion, and the independent reviewer agreed with this assessment in 17 of these 18 (94%).
The advice rendered in the formal consultations differed from that provided in 26/47 (55%) of the curbside consultations, and the formal consultation was thought to have changed management for 28/47 (60%) of patients (Table 2). The independent reviewer thought that the advice provided in the formal consultations changed management in 29/47 (62%) of the cases, and in 24/28 cases (86%) where the hospitalist felt that the formal consult changed management.
| Curbside Consultations, N (%) | |||
|---|---|---|---|
| Total | Accurate and Complete | Inaccurate or Incomplete | |
| 47 (100) | 23 (49) | 24 (51) | |
| |||
| Advice in formal consultation differed from advice in curbside consultation | 26 (55) | 7 (30) | 19 (79)* |
| Formal consultation changed management | 28 (60) | 6 (26) | 22 (92) |
| Minor change | 18 (64) | 6 (100) | 12 (55) |
| Major change | 10 (36) | 0 (0) | 10 (45) |
| Curbside consultation insufficient | 18 (38) | 2 (9) | 16 (67) |
Information was felt to be inaccurate or incomplete in 24/47 (51%) of the curbside consultations (13/47 inaccurate, 16/47 incomplete, 5/47 both inaccurate and incomplete), and when inaccurate or incomplete information was obtained, the advice given in the formal consultations more commonly differed from that provided in the curbside consultation (19/24, 79% vs 7/23, 30%; P < 0.001), and was more commonly felt to change management (22/24, 92% vs 6/23, 26%; P < 0.0001) (Table 2). No association was found between whether the curbside consultation contained complete or accurate information and the consulting service from which the curbside originated, the consulting provider, the consultative aspect(s) or medical issue(s) addressed, the number of questions asked by the hospitalist during the curbside consultation, nor whether the hospitalists felt that a formal consultation was needed.
DISCUSSION
The important findings of this study are that (a) the recommendations made by hospitalists in curbside versus formal consultations on the same patient frequently differ, (b) these differences frequently result in changes in clinical management, (c) the information presented in curbside consultations by providers is frequently inaccurate or incomplete, regardless of the providers specialty or seniority, (d) when inaccurate or incomplete information is received, the recommendations made in curbside and formal consultations differ more frequently, and (e) we found no way to predict whether the information provided in a curbside consultation was likely to be inaccurate or incomplete.
Our hospitalists thought that 38% of the curbside consultations they received should have had formal consultations. Manian and McKinsey7 reported that as many as 53% of questions asked of infectious disease consultants were thought to be too complex to be addressed in an informal consultation. Others, however, report that only 11%33% of curbside consultations were thought to require formal consultation.1, 9, 10, 16 Our hospitalists asked 3 or more questions of the consulting providers in more than 80% of the curbside consultations, suggesting that the curbside consultations we received might have had a higher complexity than those seen by others.
Our finding that information provided in curbside consultation was frequently inaccurate or incomplete is consistent with a number of previous studies reporting physicians' perceptions of the accuracy of curbside consultations.2, 3 Hospital medicine is not likely to be the only discipline affected by inaccurate curbside consultation practices, as surveys of specialists in infectious disease, gynecology, and neurosurgery report that practitioners in these disciplines have similar concerns.1, 10, 14 In a survey returned by 34 physicians, Myers1 found that 50% thought the information exchanged during curbside consultations was inaccurate, leading him to conclude that inaccuracies presented during curbside consultations required further study.
We found no way of predicting whether curbside consultations were likely to include inaccurate or incomplete information. This observation is consistent with the results of Bergus et al16 who found that the frequency of curbside consultations being converted to formal consultations was independent of the training status of the consulting physician, and with the data of Myers1 who found no way of predicting the likelihood that a curbside consultation should be converted to a formal consultation.
We found that formal consultations resulted in management changes more often than differences in recommendations (ie, 60% vs 55%, respectively). This small difference occurred because, on occasion, the formal consultations found issues to address other than the one(s) for which the curbside consultation was requested. In the majority of these instances, the management changes were minor and the curbside consultation was still felt to be sufficient.
In some instances, the advice given after the curbside and the formal consultations differed to only a minor extent (eg, varying recommendations for oral diabetes management). In other instances, however, the advice differed substantially (eg, change in antibiotic management in a septic patient with a multidrug resistant organism, when the original curbside question was for when to order a follow‐up chest roentgenogram for hypoxia; see Supporting Information, Appendix, in the online version of this article). In 26 patients (55%), formal consultation resulted in different medications being started or stopped, additional tests being performed, or different decisions being made about admission versus discharge.
Our study has a number of strengths. First, while a number of reports document that physicians' perceptions are that curbside consultations frequently contain errors,2, 3, 5, 12 to our knowledge this is the first study that prospectively compared the information collected and advice given in curbside versus formal consultation. Second, while this study was conducted as a quality improvement project, thereby requiring us to conclude that the results are not generalizable, the data presented were collected by 18 different hospitalists, reducing the potential of bias from an individual provider's knowledge base or practice. Third, there was excellent agreement between the independent reviewer and the 2 hospitalists who performed the curbside and formal consultations regarding whether a curbside consultation would have been sufficient, and whether the formal consultation changed patient management. Fourth, the study was conducted over a 1‐year period, which should have reduced potential bias arising from the increasing experience of residents requesting consultations as their training progressed.
Our study has several limitations. First, the number of curbside consultations we received during the study period (50 over 215 days) was lower than anticipated, and lower than the rates of consultation reported by others.1, 7, 9 This likely relates to the fact that, prior to beginning the study, Denver Health hospitalists already provided mandatory consultations for several surgical services (thereby reducing the number of curbside consultations received from these services), because curbside consultations received during evenings, nights, and weekends were not included in the study for reasons of convenience, and because we excluded all administrative curbside consultations. Our hospitalist service also provides consultative services 24 hours a day, thereby reducing the number of consultations received during daytime hours. Second, the frequency with which curbside consultations included inaccurate or incomplete information might be higher than what occurs in other hospitals, as Denver Health is an urban, university‐affiliated public hospital and the patients encountered may be more complex and trainees may be less adept at recognizing the information that would facilitate accurate curbside consultations (although we found no difference in the frequency with which inaccurate or incomplete information was provided as a function of the seniority of the requesting physician). Third, the disparity between curbside and formal consultations that we observed could have been biased by the Hawthorne effect. We attempted to address this by not providing the hospitalists who did the formal consultation with any information collected by the hospitalist involved with the curbside consultation, and by comparing the conclusions reached by the hospitalists performing the curbside and formal consultations with those of a third party reviewer. Fourth, while we found no association between the frequency of curbside consultations in which information was inaccurate or incomplete and the consulting service, there could be a selection bias of the consulting service requesting the curbside consultations as a result of the mandatory consultations already provided by our hospitalists. Finally, our study was not designed or adequately powered to determine why curbside consultations frequently have inaccurate or incomplete information.
In summary, we found that the information provided to hospitalists during a curbside consultation was often inaccurate and incomplete, and that these problems with information exchange adversely affected the accuracy of the resulting recommendations. While there are a number of advantages to curbside consultations,1, 3, 7, 10, 12, 13 our findings indicate that the risk associated with this practice is substantial.
Acknowledgements
Disclosure: Nothing to report.
- .Curbside consultation in infectious diseases: a prospective study.J Infect Dis.1984;150:797–802.
- ,,.Physicians' experiences and beliefs regarding informal consultation.JAMA.1998;280:900–904.
- ,,.Curbside consultation practices and attitudes among primary care physicians and medical subspecialists.JAMA.1998;280:905–909.
- ,,,,,.The complexity, relative value, and financial worth of curbside consultations in an academic infectious diseases unit.Clin Infect Dis.2010;51:651–655.
- ,.Curbside consultations. A closer look at a common practice.JAMA.1996;275:145–147.
- ,,,.Informal advice‐ and information‐seeking between physicians.J Med Educ.1981;56;174–180.
- ,.A prospective study of 2,092 “curbside” questions asked of two infectious disease consultants in private practice in the midwest.Clin Infect Dis.1996;22:303–307.
- ,,,,.Curbside consultation in endocrine practice: a prospective observational study.Endocrinologist.1996;6:328–331.
- ,,.Informal consultations provided to general internists by the gastroenterology department of an HMO.J Gen Intern Med.1998;13:435–438.
- .“Curbside” consultations in gynecologic oncology: a closer look at a common practice.Gynecol Oncol.1999;74:456–459.
- ,,, et al.Informal consultations in infectious diseases and clinical microbiology practice.Clin Microbiol Infect.2003;9:724–726.
- .Curbside consultations and the viaduct effect.JAMA.1998;280:929–930.
- .What do we really need to know about consultation and referral?J Gen Intern Med.1998;13:497–498.
- ,.Telephone advice for neurosurgical referrals. Who assumes duty of care?Br J Neurosurg.2001;15:453–455.
- ,.Malpractice liability for informal consultations.Fam Med.2003;35:476–481.
- ,,,.Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?Arch Fam Med.2000;9:541–547.
- .Curbside consultation in infectious diseases: a prospective study.J Infect Dis.1984;150:797–802.
- ,,.Physicians' experiences and beliefs regarding informal consultation.JAMA.1998;280:900–904.
- ,,.Curbside consultation practices and attitudes among primary care physicians and medical subspecialists.JAMA.1998;280:905–909.
- ,,,,,.The complexity, relative value, and financial worth of curbside consultations in an academic infectious diseases unit.Clin Infect Dis.2010;51:651–655.
- ,.Curbside consultations. A closer look at a common practice.JAMA.1996;275:145–147.
- ,,,.Informal advice‐ and information‐seeking between physicians.J Med Educ.1981;56;174–180.
- ,.A prospective study of 2,092 “curbside” questions asked of two infectious disease consultants in private practice in the midwest.Clin Infect Dis.1996;22:303–307.
- ,,,,.Curbside consultation in endocrine practice: a prospective observational study.Endocrinologist.1996;6:328–331.
- ,,.Informal consultations provided to general internists by the gastroenterology department of an HMO.J Gen Intern Med.1998;13:435–438.
- .“Curbside” consultations in gynecologic oncology: a closer look at a common practice.Gynecol Oncol.1999;74:456–459.
- ,,, et al.Informal consultations in infectious diseases and clinical microbiology practice.Clin Microbiol Infect.2003;9:724–726.
- .Curbside consultations and the viaduct effect.JAMA.1998;280:929–930.
- .What do we really need to know about consultation and referral?J Gen Intern Med.1998;13:497–498.
- ,.Telephone advice for neurosurgical referrals. Who assumes duty of care?Br J Neurosurg.2001;15:453–455.
- ,.Malpractice liability for informal consultations.Fam Med.2003;35:476–481.
- ,,,.Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?Arch Fam Med.2000;9:541–547.
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