Affiliations
Department of Medicine, Denver Health Medical Center
Department of Internal Medicine, University of Colorado School of Medicine
Given name(s)
Sarah A.
Family name
Stella
Degrees
MD

Barriers to Early Hospital Discharge: A Cross-Sectional Study at Five Academic Hospitals

Article Type
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Wed, 01/09/2019 - 10:00

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 am and 12 pm. In this survey, the physicians were asked to reassess the patients previously classified as definite and possible discharges for changes in status and/or barriers and to identify patients who had become definite or possible discharges since the earlier survey. Newly identified possible or definite discharges were evaluated in a similar manner as the initial survey.

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 am (7:51 am, 9:12 am), 11:45 am (11:30 am, 12:17 pm), and 3:20 pm (3:00 pm, 4:06 pm).

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 am (10:35 am, 1:45 pm).

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.

 

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References

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
<--pagebreak-->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

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Related Articles

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 am and 12 pm. In this survey, the physicians were asked to reassess the patients previously classified as definite and possible discharges for changes in status and/or barriers and to identify patients who had become definite or possible discharges since the earlier survey. Newly identified possible or definite discharges were evaluated in a similar manner as the initial survey.

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 am (7:51 am, 9:12 am), 11:45 am (11:30 am, 12:17 pm), and 3:20 pm (3:00 pm, 4:06 pm).

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 am (10:35 am, 1:45 pm).

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 am and 12 pm. In this survey, the physicians were asked to reassess the patients previously classified as definite and possible discharges for changes in status and/or barriers and to identify patients who had become definite or possible discharges since the earlier survey. Newly identified possible or definite discharges were evaluated in a similar manner as the initial survey.

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 am (7:51 am, 9:12 am), 11:45 am (11:30 am, 12:17 pm), and 3:20 pm (3:00 pm, 4:06 pm).

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 am (10:35 am, 1:45 pm).

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.

 

References

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
<--pagebreak-->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

References

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
<--pagebreak-->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

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Marisha Burden, MD, Division of Hospital Medicine, University of Colorado School of Medicine, 12401 East 17th Avenue, Mailstop F-782, Aurora, Colorado, 80045; Telephone: 720-848-4289; Fax: 720- 848-4293; E-mail: marisha.burden@ucdenver.edu
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HM16 Session Analysis: Infectious Disease Emergencies: Three Diagnoses You Can’t Afford to Miss

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HM16 Session Analysis: Infectious Disease Emergencies: Three Diagnoses You Can’t Afford to Miss

Presenter: Jim Pile, MD, Cleveland Clinic

Summary: The following three infectious diagnoses are relatively uncommon but important not to miss as they are associated with high mortality, especially when diagnosis and treatment are delayed. Remembering these key points can help you make the diagnosis:

  • Bacterial meningitis: Many patients do not have the classic triad—fever, nuchal rigidity, and altered mental status—but nearly all have at least one of these signs, and most have headache. The jolt accentuation test—horizontal movement of the head causing exacerbation of the headache—is more sensitive than nuchal rigidity in these cases. Diagnosis is confirmed by lumbar puncture. It appears safe to not to perform head CT in patients

  • Spinal epidural abscess: Risk factors include DM, IV drug use, hemodialysis, UTI, trauma, epidural anesthesia, trauma/surgery. Presentation is acute to indolent and usually consists of four stages: central back pain, radicular pain, neurologic deficits, paralysis; fever variable. Checking ESR can be helpful as it is elevated in most cases. MRI is imaging study of choice. Initial management includes antibiotics to coverage Staph Aureus and gram negative rods and surgery consultation.
  • Necrotizing soft tissue infection: Risk factors include DM, IV drug use, trauma/surgery, ETOH, immunosuppression (Type I); muscle trauma, skin integrity deficits (Type II). Clinical suspicion is paramount. Specific clues include: pain out of proportion, anesthesia, systemic toxicity, rapid progression, bullae/crepitus, and failure to respond to antibiotics. Initial management includes initiation of B-lactam/lactamase inhibitor or carbapenem plus clindamycin and MRSA coverage, imaging and prompt surgical consultation (as delayed/inadequate surgery associated with poor prognosis.

Key Takeaway

Clinical suspicion is key to diagnosis of bacterial meningitis, spinal epidural abscesses, and necrotizing soft tissue infections, and delays in diagnosis and treatment are associated with increased mortality.TH

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Presenter: Jim Pile, MD, Cleveland Clinic

Summary: The following three infectious diagnoses are relatively uncommon but important not to miss as they are associated with high mortality, especially when diagnosis and treatment are delayed. Remembering these key points can help you make the diagnosis:

  • Bacterial meningitis: Many patients do not have the classic triad—fever, nuchal rigidity, and altered mental status—but nearly all have at least one of these signs, and most have headache. The jolt accentuation test—horizontal movement of the head causing exacerbation of the headache—is more sensitive than nuchal rigidity in these cases. Diagnosis is confirmed by lumbar puncture. It appears safe to not to perform head CT in patients

  • Spinal epidural abscess: Risk factors include DM, IV drug use, hemodialysis, UTI, trauma, epidural anesthesia, trauma/surgery. Presentation is acute to indolent and usually consists of four stages: central back pain, radicular pain, neurologic deficits, paralysis; fever variable. Checking ESR can be helpful as it is elevated in most cases. MRI is imaging study of choice. Initial management includes antibiotics to coverage Staph Aureus and gram negative rods and surgery consultation.
  • Necrotizing soft tissue infection: Risk factors include DM, IV drug use, trauma/surgery, ETOH, immunosuppression (Type I); muscle trauma, skin integrity deficits (Type II). Clinical suspicion is paramount. Specific clues include: pain out of proportion, anesthesia, systemic toxicity, rapid progression, bullae/crepitus, and failure to respond to antibiotics. Initial management includes initiation of B-lactam/lactamase inhibitor or carbapenem plus clindamycin and MRSA coverage, imaging and prompt surgical consultation (as delayed/inadequate surgery associated with poor prognosis.

Key Takeaway

Clinical suspicion is key to diagnosis of bacterial meningitis, spinal epidural abscesses, and necrotizing soft tissue infections, and delays in diagnosis and treatment are associated with increased mortality.TH

Presenter: Jim Pile, MD, Cleveland Clinic

Summary: The following three infectious diagnoses are relatively uncommon but important not to miss as they are associated with high mortality, especially when diagnosis and treatment are delayed. Remembering these key points can help you make the diagnosis:

  • Bacterial meningitis: Many patients do not have the classic triad—fever, nuchal rigidity, and altered mental status—but nearly all have at least one of these signs, and most have headache. The jolt accentuation test—horizontal movement of the head causing exacerbation of the headache—is more sensitive than nuchal rigidity in these cases. Diagnosis is confirmed by lumbar puncture. It appears safe to not to perform head CT in patients

  • Spinal epidural abscess: Risk factors include DM, IV drug use, hemodialysis, UTI, trauma, epidural anesthesia, trauma/surgery. Presentation is acute to indolent and usually consists of four stages: central back pain, radicular pain, neurologic deficits, paralysis; fever variable. Checking ESR can be helpful as it is elevated in most cases. MRI is imaging study of choice. Initial management includes antibiotics to coverage Staph Aureus and gram negative rods and surgery consultation.
  • Necrotizing soft tissue infection: Risk factors include DM, IV drug use, trauma/surgery, ETOH, immunosuppression (Type I); muscle trauma, skin integrity deficits (Type II). Clinical suspicion is paramount. Specific clues include: pain out of proportion, anesthesia, systemic toxicity, rapid progression, bullae/crepitus, and failure to respond to antibiotics. Initial management includes initiation of B-lactam/lactamase inhibitor or carbapenem plus clindamycin and MRSA coverage, imaging and prompt surgical consultation (as delayed/inadequate surgery associated with poor prognosis.

Key Takeaway

Clinical suspicion is key to diagnosis of bacterial meningitis, spinal epidural abscesses, and necrotizing soft tissue infections, and delays in diagnosis and treatment are associated with increased mortality.TH

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HM16 Session Analysis: Hospital Quality, Patient Safety Update for 2015

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HM16 Session Analysis: Hospital Quality, Patient Safety Update for 2015

HM16 Presenters: Mangla Gulati, MD, FACP, FHM, and Ian Jenkins, MD

Summary: There is some evidence that hospital adverse events may be decreasing. The Agency for Healthcare Research and Quality (AHRQ) report of Partnership for Patients data found that there were 1.3 million hospital-acquired conditions prevented from 2011-2013, translating to 50,000 lives saved and $12 billion in savings. However, there is still much work to be done. Here are some major themes from the recent literature that may inform your own QI projects:

  • Electronic medical records (EMR): Increased adoption raises concerns for unintended consequences which could detract from patient safety. These are derived from case reports, claims databases, reports through patient safety organizations. Consider reviewing alerts, advisories, and hard-stops within your hospital’s EMR.
  • Improving diagnosis: Misdiagnosis affects 10% of Americans but measurement of this phenomenon remains difficult. Strategies to improve diagnosis include: focus on collaboration, teaching diagnostic skills, IT and algorithmic support, feedback and blame reduction, and reimbursement reform. There is growing recognition that hospitalists are well positioned to engage in these efforts.
  • Hand hygiene: Multiple culture and environmental contributors to poor compliance (i.e., 40% compliance rate) and few good intervention studies. Recent studies have focused on the development of cause-specific plans and targeted interventions. Consider applying a targeted solutions tool to a project at your hospital.
  • “Second victim”: Healthcare providers frequently experience emotional distress following patient safety events but there is a lack of resources to support providers after such an event. Consider implementing a “care for the caregiver” program at your hospital.
  • Sepsis: North Shore-LIJ hospital system (in collaboration with the IHI) received the 2014 Eisenberg Award for developing and implementing a Sepsis Recognition Program and tool-kit. Such collaborations may be useful for driving and magnifying your QI initiatives.
  • Patient experience: Recent studies emphasize the importance of patients being actively involved in their medical decisions and providing meaningful feedback. Consider implementing a patient/family council and getting involved with local patient experience initiatives.
  • Incident reporting systems (IRS): Widespread under-reporting raises concerns over the value of IRS. Consider modifying or minimizing IRS and/or giving reports to local units to ignite safety culture.
  • Discharge before noon (DBN): Delayed discharges may result in lost revenue and poor patient satisfaction. Wertheimer et al. reported on a successful intervention to increase DBN in the Journal of Hospital Medicine. Check out their intervention and consider something similar at your hospital.
  • Patient monitoring: Vital sign measurements are often poorly timed, integrated and/or communicated. Recent studies of wireless Electronic Physiologic Surveillance System devices in hospitals have shown positive outcomes.TH
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HM16 Presenters: Mangla Gulati, MD, FACP, FHM, and Ian Jenkins, MD

Summary: There is some evidence that hospital adverse events may be decreasing. The Agency for Healthcare Research and Quality (AHRQ) report of Partnership for Patients data found that there were 1.3 million hospital-acquired conditions prevented from 2011-2013, translating to 50,000 lives saved and $12 billion in savings. However, there is still much work to be done. Here are some major themes from the recent literature that may inform your own QI projects:

  • Electronic medical records (EMR): Increased adoption raises concerns for unintended consequences which could detract from patient safety. These are derived from case reports, claims databases, reports through patient safety organizations. Consider reviewing alerts, advisories, and hard-stops within your hospital’s EMR.
  • Improving diagnosis: Misdiagnosis affects 10% of Americans but measurement of this phenomenon remains difficult. Strategies to improve diagnosis include: focus on collaboration, teaching diagnostic skills, IT and algorithmic support, feedback and blame reduction, and reimbursement reform. There is growing recognition that hospitalists are well positioned to engage in these efforts.
  • Hand hygiene: Multiple culture and environmental contributors to poor compliance (i.e., 40% compliance rate) and few good intervention studies. Recent studies have focused on the development of cause-specific plans and targeted interventions. Consider applying a targeted solutions tool to a project at your hospital.
  • “Second victim”: Healthcare providers frequently experience emotional distress following patient safety events but there is a lack of resources to support providers after such an event. Consider implementing a “care for the caregiver” program at your hospital.
  • Sepsis: North Shore-LIJ hospital system (in collaboration with the IHI) received the 2014 Eisenberg Award for developing and implementing a Sepsis Recognition Program and tool-kit. Such collaborations may be useful for driving and magnifying your QI initiatives.
  • Patient experience: Recent studies emphasize the importance of patients being actively involved in their medical decisions and providing meaningful feedback. Consider implementing a patient/family council and getting involved with local patient experience initiatives.
  • Incident reporting systems (IRS): Widespread under-reporting raises concerns over the value of IRS. Consider modifying or minimizing IRS and/or giving reports to local units to ignite safety culture.
  • Discharge before noon (DBN): Delayed discharges may result in lost revenue and poor patient satisfaction. Wertheimer et al. reported on a successful intervention to increase DBN in the Journal of Hospital Medicine. Check out their intervention and consider something similar at your hospital.
  • Patient monitoring: Vital sign measurements are often poorly timed, integrated and/or communicated. Recent studies of wireless Electronic Physiologic Surveillance System devices in hospitals have shown positive outcomes.TH

HM16 Presenters: Mangla Gulati, MD, FACP, FHM, and Ian Jenkins, MD

Summary: There is some evidence that hospital adverse events may be decreasing. The Agency for Healthcare Research and Quality (AHRQ) report of Partnership for Patients data found that there were 1.3 million hospital-acquired conditions prevented from 2011-2013, translating to 50,000 lives saved and $12 billion in savings. However, there is still much work to be done. Here are some major themes from the recent literature that may inform your own QI projects:

  • Electronic medical records (EMR): Increased adoption raises concerns for unintended consequences which could detract from patient safety. These are derived from case reports, claims databases, reports through patient safety organizations. Consider reviewing alerts, advisories, and hard-stops within your hospital’s EMR.
  • Improving diagnosis: Misdiagnosis affects 10% of Americans but measurement of this phenomenon remains difficult. Strategies to improve diagnosis include: focus on collaboration, teaching diagnostic skills, IT and algorithmic support, feedback and blame reduction, and reimbursement reform. There is growing recognition that hospitalists are well positioned to engage in these efforts.
  • Hand hygiene: Multiple culture and environmental contributors to poor compliance (i.e., 40% compliance rate) and few good intervention studies. Recent studies have focused on the development of cause-specific plans and targeted interventions. Consider applying a targeted solutions tool to a project at your hospital.
  • “Second victim”: Healthcare providers frequently experience emotional distress following patient safety events but there is a lack of resources to support providers after such an event. Consider implementing a “care for the caregiver” program at your hospital.
  • Sepsis: North Shore-LIJ hospital system (in collaboration with the IHI) received the 2014 Eisenberg Award for developing and implementing a Sepsis Recognition Program and tool-kit. Such collaborations may be useful for driving and magnifying your QI initiatives.
  • Patient experience: Recent studies emphasize the importance of patients being actively involved in their medical decisions and providing meaningful feedback. Consider implementing a patient/family council and getting involved with local patient experience initiatives.
  • Incident reporting systems (IRS): Widespread under-reporting raises concerns over the value of IRS. Consider modifying or minimizing IRS and/or giving reports to local units to ignite safety culture.
  • Discharge before noon (DBN): Delayed discharges may result in lost revenue and poor patient satisfaction. Wertheimer et al. reported on a successful intervention to increase DBN in the Journal of Hospital Medicine. Check out their intervention and consider something similar at your hospital.
  • Patient monitoring: Vital sign measurements are often poorly timed, integrated and/or communicated. Recent studies of wireless Electronic Physiologic Surveillance System devices in hospitals have shown positive outcomes.TH
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Problems Identified by Advice Line Calls

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Postdischarge problems identified by telephone calls to an advice line

The period immediately following hospital discharge is particularly hazardous for patients.[1, 2, 3, 4, 5] Problems occurring after discharge may result in high rates of rehospitalization and unscheduled visits to healthcare providers.[6, 7, 8, 9, 10] Numerous investigators have tried to identify patients who are at increased risk for rehospitalizations within 30 days of discharge, and many studies have examined whether various interventions could decrease these adverse events (summarized in Hansen et al.[11]). An increasing fraction of patients discharged by medicine and surgery services have some or all of their care supervised by hospitalists. Thus, hospitals increasingly look to hospitalists for ways to reduce rehospitalizations.

Patients discharged from our hospital are instructed to call an advice line (AL) if and when questions or concerns arise. Accordingly, we examined when these calls were made and what issues were raised, with the idea that the information collected might identify aspects of our discharge processes that needed improvement.

METHODS

Study Design

We conducted a prospective study of a cohort consisting of all unduplicated patients with a matching medical record number in our data warehouse who called our AL between September 1, 2011 and September 1, 2012, and reported being hospitalized or having surgery (inpatient or outpatient) within 30 days preceding their call. We excluded patients who were incarcerated, those who were transferred from other hospitals, those admitted for routine chemotherapy or emergent dialysis, and those discharged to a skilled nursing facility or hospice. The study involved no intervention. It was approved by the Colorado Multiple Institutional Review Board.

Setting

The study was conducted at Denver Health Medical Center, a 525‐bed, university‐affiliated, public safety‐net hospital. At the time of discharge, all patients were given paperwork that listed the telephone number of the AL and written instructions in English or Spanish telling them to call the AL or their primary care physician if they had any of a list of symptoms that was selected by their discharging physician as being relevant to that specific patient's condition(s).

The AL was established in 1997 to provide medical triage to patients of Denver Health. It operates 24 hours a day, 7 days per week, and receives approximately 100,000 calls per year. A language line service is used with nonEnglish‐speaking callers. Calls are handled by a nurse who, with the assistance of a commercial software program (E‐Centaurus; LVM Systems, Phoenix, AZ) containing clinical algorithms (Schmitt‐Thompson Clinical Content, Windsor, CO), makes a triage recommendation. Nurses rarely contact hospital or clinic physicians to assist with triage decisions.

Variables Assessed

We categorized the nature of the callers' reported problem(s) to the AL using the taxonomy summarized in the online appendix (see Supporting Appendix in the online version of this article). We then queried our data warehouse for each patient's demographic information, patient‐level comorbidities, discharging service, discharge date and diagnoses, hospital length of stay, discharge disposition, and whether they had been hospitalized or sought care in our urgent care center or emergency department within 30 days of discharge. The same variables were collected for all unduplicated patients who met the same inclusion and exclusion criteria and were discharged from Denver Health during the same time period but did not call the AL.

Statistics

Data were analyzed using SAS Enterprise Guide 4.1 (SAS Institute, Inc., Cary, NC). Because we made multiple statistical comparisons, we applied the Bonferroni correction when comparing patients calling the AL with those who did not, such that P<0.004 indicated statistical significance. A Student t test or a Wilcoxon rank sum test was used to compare continuous variables depending on results of normality tests. 2 tests were used to compare categorical variables. The intervals between hospital discharge and the call to the AL for patients discharged from medicine versus surgery services were compared using a log‐rank test, with P<0.05 indicating statistical significance.

RESULTS

During the 1‐year study period, 19,303 unique patients were discharged home with instructions regarding the use of the AL. A total of 310 patients called the AL and reported being hospitalized or having surgery within the preceding 30 days. Of these, 2 were excluded (1 who was incarcerated and 1 who was discharged to a skilled nursing facility), leaving 308 patients in the cohort. This represented 1.5% of the total number of unduplicated patients discharged during this same time period (minus the exclusions described above). The large majority of the calls (277/308, 90%) came directly from patients. The remaining 10% came from a proxy, usually a patient's family member. Compared with patients who were discharged during the same time period who did not call the AL, those who called were more likely to speak English, less likely to speak Spanish, more likely to be medically indigent, had slightly longer lengths of stays for their index hospitalization, and were more likely to be discharged from surgery than medicine services (particularly following inpatient surgery) (Table 1).

Patient Characteristics
Patient CharacteristicsPatients Calling Advice Line After Discharge, N=308Patients Not Calling Advice Line After Discharge, N=18,995P Valuea
  • NOTE: Abbreviations: IQR, interquartile range; SD, standard deviation.

  • Bonferroni correction for multiple comparisons was applied, with a P<0.004 indicating significance.

  • Defined as uninsured, ineligible for Medicaid, and unable to purchase private insurance.

  • Defined as 1 or more visits to a primary care provider within 3 years of index hospitalization.

Age, y (meanSD)421739210.0210
Gender, female, n (%)162 (53)10,655 (56) 
Race/ethnicity, n (%)  0.1208
Hispanic/Latino/Spanish129 (42)8,896 (47) 
African American44 (14)2,674 (14) 
White125 (41)6,569 (35) 
Language, n (%)  <0.0001
English273 (89)14,236 (79) 
Spanish32 (10)3,744 (21) 
Payer, n (%)   
Medicare45 (15)3,013 (16) 
Medicaid105 (34)7,777 (41)0.0152
Commercial49 (16)2,863 (15) 
Medically indigentb93 (30)3,442 (18)<0.0001
Self‐pay5 (1)1,070 (5) 
Primary care provider, n (%)c168 (55)10,136 (53)0.6794
Psychiatric comorbidity, n (%)81 (26)4,528 (24)0.3149
Alcohol or substance abuse comorbidity, n (%)65 (21)3,178 (17)0.0417
Discharging service, n (%)  <0.0001
Surgery193 (63)7,247 (38) 
Inpatient123 (40)3,425 (18) 
Ambulatory70 (23)3,822 (20) 
Medicine93 (30)6,038 (32) 
Pediatric4 (1)1,315 (7) 
Obstetric11 (4)3,333 (18) 
Length of stay, median (IQR)2 (04.5)1 (03)0.0003
Inpatient medicine4 (26)3 (15)0.0020
Inpatient surgery3 (16)2 (14)0.0019
Charlson Comorbidity Index, median (IQR)
Inpatient medicine1 (04)1 (02)0.0435
Inpatient surgery0 (01)0 (01)0.0240

The median time from hospital discharge to the call was 3 days (interquartile range [IQR], 16), but 31% and 47% of calls occurred within 24 or 48 hours of discharge, respectively. Ten percent of patients called the AL the same day of discharge (Figure 1). We found no difference in timing of the calls as a function of discharging service.

Figure 1
Timing of calls relative to discharge.

The 308 patients reported a total of 612 problems or concerns (meanstandard deviation number of complaints per caller=21), the large majority of which (71%) were symptom‐related (Table 2). The most common symptom was uncontrolled pain, reported by 33% and 40% of patients discharged from medicine and surgery services, respectively. The next most common symptoms related to the gastrointestinal system and to surgical site issues in medicine and surgery patients, respectively (data not shown).

Frequency of Patient‐Reported Concerns
 Total Cohort, n (%)Patients Discharged From Medicine, n (%)Patients Discharged From Surgery, n (%)
PatientsComplaintsPatientsComplaintsPatientsComplaints
Symptom related280 (91)433 (71)89 (96)166 (77)171 (89)234 (66)
Discharge instructions65 (21)81 (13)18 (19)21 (10)43 (22)56 (16)
Medication related65 (21)87 (14)19 (20)25 (11)39 (20)54 (15)
Other10 (3)11 (2)4 (4)4 (2)6 (3)7 (2)
Total 612 (100) 216 (100) 351 (100)

Sixty‐five patients, representing 21% of the cohort, reported 81 problems understanding or executing discharge instructions. No difference was observed between the fraction of these problems reported by patients from medicine versus surgery (19% and 22%, respectively, P=0.54).

Sixty‐five patients, again representing 21% of the cohort, reported 87 medication‐related problems, 20% from both the medicine and surgery services (P=0.99). Medicine patients more frequently reported difficulties understanding their medication instructions, whereas surgery patients more frequently reported lack of efficacy of medications, particularly with respect to pain control (data not shown).

Thirty percent of patients who called the AL were advised by the nurse to go to the emergency department immediately. Medicine patients were more likely to be triaged to the emergency department compared with surgery patients (45% vs 22%, P<0.0001).

The 30‐day readmission rates and the rates of unscheduled urgent or emergent care visits were higher for patients calling the AL compared with those who did not call (46/308, 15% vs 706/18,995, 4%, and 92/308, 30% vs 1303/18,995, 7%, respectively, both P<0.0001). Similar differences were found for patients discharged from medicine or surgery services who called the AL compared with those who did not (data not shown, both P<0.0001). The median number of days between AL call and rehospitalization was 0 (IQR, 02) and 1 (IQR, 08) for medicine and surgery patients, respectively. Ninety‐three percent of rehospitalizations were related to the index hospitalization, and 78% of patients who were readmitted had no outpatient encounter in the interim between discharge and rehospitalization.

DISCUSSION

We investigated the source and nature of patient telephone calls to an AL following a hospitalization or surgery, and our data revealed the following important findings: (1) nearly one‐half of the calls to the AL occurred within the first 48 hours following discharge; (2) the majority of the calls came from surgery patients, and a greater fraction of patients discharged from surgery services called the AL than patients discharged from medicine services; (3) the most common issues were uncontrolled pain, questions about medications, and problems understanding or executing aftercare instructions (particularly pertaining to the care of surgical wounds); and (4) patients calling the AL had higher rates of 30‐day rehospitalization and of unscheduled urgent or emergent care visits.

The utilization of our patient‐initiated call line was only 1.5%, which was on the low end of the 1% to 10% reported in the literature.[7, 12] This can be attributed to a number of issues that are specific to our system. First, the discharge instructions provided to our patients stated that they should call their primary care provider or the AL if they had questions. Accordingly, because approximately 50% of our patients had a primary care provider in our system, some may have preferentially contacted their primary care provider rather than the AL. Second, the instructions stated that the patients should call if they were experiencing the symptoms listed on the instruction sheet, so those with other problems/complaints may not have called. Third, AL personnel identified patients as being in our cohort by asking if they had been discharged or underwent a surgical procedure within 30‐days of their call. This may have resulted in the under‐reporting of patients who were hospitalized or had outpatient surgical procedures. Fourth, there may have been a number of characteristics specific to patients in our system that reduced the frequency with which they utilized the AL (eg, access to telephones or other community providers).

Most previous studies of patient‐initiated call lines have included them as part of multi‐intervention pre‐ and/or postdischarge strategies.[7, 8, 9, 10, 11, 12, 13] One prior small study compared the information reported by 37 patients who called an AL with that elicited by nurse‐initiated patient contact.[12] The most frequently reported problems in this study were medication‐related issues (43%). However, this study only included medicine patients and did not document the proportion of calls occurring at various time intervals.

The problems we identified (in both medicine and surgery patients) have previously been described,[2, 3, 4, 13, 14, 15, 16] but all of the studies reporting these problems utilized calls that were initiated by health care providers to patients at various fixed intervals following discharge (ie, 730 days). Most of these used a scripted approach seeking responses to specific questions or outcomes, and the specific timing at which the problems arose was not addressed. In contrast, we examined unsolicited concerns expressed by patients calling an AL following discharge whenever they felt sufficient urgency to address whatever problems or questions arose. We found that a large fraction of calls occurred on the day of or within the first 48 hours following discharge, much earlier than when provider‐initiated calls in the studies cited above occurred. Accordingly, our results cannot be used to compare the utility of patient‐ versus provider‐initiated calls, or to suggest that other hospitals should create an AL system. Rather, we suggest that our findings might be complementary to those reported in studies of provider‐initiated calls and only propose that by examining calls placed by patients to ALs, problems with hospital discharge processes (some of which may result in increased rates of readmission) may be discovered.

The observation that such a large fraction of calls to our AL occurred within the first 48 hours following discharge, together with the fact that many of the questions asked or concerns raised pertained to issues that should have been discussed during the discharge process (eg, pain control, care of surgical wounds), suggests that suboptimal patient education was occurring prior to discharge as was suggested by Henderson and Zernike.[17] This finding has led us to expand our patient education processes prior to discharge on both medicine and surgery services. Because our hospitalists care for approximately 90% of the patients admitted to medicine services and are increasingly involved in the care of patients on surgery services, they are integrally involved with such quality improvement initiatives.

To our knowledge this is the first study in the literature that describes both medicine and surgery patients who call an AL because of problems or questions following hospital discharge, categorizes these problems, determines when the patients called following their discharge, and identifies those who called as being at increased risk for early rehospitalizations and unscheduled urgent or emergent care visits. Given the financial penalties issued to hospitals with high 30‐day readmission rates, these patients may warrant more attention than is customarily available from telephone call lines or during routine outpatient follow‐up. The majority of patients who called our AL had Medicare, Medicaid, or a commercial insurance, and, accordingly, may have been eligible for additional services such as home visits and/or expedited follow‐up appointments.

Our study has a number of limitations. First, it is a single‐center study, so the results might not generalize to other institutions. Second, because the study was performed in a university‐affiliated, public safety‐net hospital, patient characteristics and the rates and types of postdischarge concerns that we observed might differ from those encountered in different types of hospitals and/or from those in nonteaching institutions. We would suggest, however, that the idea of using concerns raised by patients discharged from any type of hospital in calls to ALs may similarly identify problems with that specific hospital's discharge processes. Third, the information collected from the AL came from summaries provided by nurses answering the calls rather than from actual transcripts. This could have resulted in insufficient or incorrect information pertaining to some of the variables assessed in Table 2. The information presented in Table 1, however, was obtained from our data warehouse after matching medical record numbers. Fourth, we could have underestimated the number of patients who had 30‐day rehospitalizations and/or unplanned for urgent or emergent care visits if patients sought care at other hospitals. Fifth, the number of patients calling the AL was too small to allow us to do any type of robust matching or multivariable analysis. Accordingly, the differences that appeared between patients who called and those who did not (ie, English speakers, being medically indigent, the length of stay for the index hospitalization and the discharging service) could be the result of inadequate matching or interactions among the variables. Although matching or multivariate analysis might have yielded different associations between patients who called the AL versus those who did not, those who called the AL still had an increased risk of readmission and urgent or emergent visits and may still benefit from targeted interventions. Finally, the fact that only 1.5% of unique patients who were discharged called the AL could have biased our results. Because only 55% and 53% of the patients who did or did not call the AL, respectively, saw primary care physicians within our system within the 3 years prior to their index hospitalization (P=0.679), the frequency of calls to the AL that we observed could have underestimated the frequency with which patients had contact with other care providers in the community.

In summary, information collected from patient‐initiated calls to our AL identified several aspects of our discharge processes that needed improvement. We concluded that our predischarge educational processes for both medicine and surgery services needed modification, especially with respect to pain management, which problems to expect after hospitalization or surgery, and how to deal with them. The high rates of 30‐day rehospitalization and of unscheduled urgent or emergent care visits among patients calling the AL identifies them as being at increased risk for these outcomes, although the likelihood of these events may be related to factors other than just calling the AL.

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References
  1. Parrish MM, O'Malley K, Adams RI, Adams SR, Coleman EA. Implementation of the care transitions intervention: sustainability and lessons learned. Prof Case Manag. 2009;14(6):282293.
  2. Arora VM, Prochaska ML, Farnan JM, et al. Problems after discharge and understanding of communication with their primary care physicians among hospitalized seniors: a mixed methods study. J Hosp Med. 2010;5(7):385391.
  3. Forster AJ, Clark HD, Menard A, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345349.
  4. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161167.
  5. Misky GJ, Wald HL, Coleman EA. Post‐hospitalization transitions: examining the effects of timing of primary care provider follow‐up. J Hosp Med. 2010;5(7):392397.
  6. Bostrom J, Caldwell J, McGuire K, Everson D. Telephone follow‐up after discharge from the hospital: does it make a difference? Appl Nurs Res. 1996;9(2) 4752.
  7. Sorknaes AD, Bech M, Madsen H, et al. The effect of real‐time teleconsultations between hospital‐based nurses and patients with severe COPD discharged after an exacerbation. J Telemed Telecare. 2013;19(8):466474.
  8. Kwok T, Lum CM, Chan HS, Ma HM, Lee D, Woo J. A randomized, controlled trial of an intensive community nurse‐supported discharge program in preventing hospital readmissions of older patients with chronic lung disease. J Am Geriatr Soc. 2004;52(8):12401246.
  9. Jaarsma T, Halfens R, Huijer Abu‐Saad H, et al. Effects of education and support on self‐care and resource utilization in patients with heart failure. Eur Heart J. 1999;20(9):673682.
  10. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow‐up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613620.
  11. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520528.
  12. Rennke S, Kesh S, Neeman N, Sehgal NL. Complementary telephone strategies to improve postdischarge communication. Am J Med. 2012;125(1):2830.
  13. Shu CC, Hsu NC, Lin YF, Wang JY, Lin JW, Ko WJ. Integrated postdischarge transitional care in a hospitalist system to improve discharge outcome: an experimental study. BMC Med. 2011;9:96.
  14. Hinami K, Bilimoria KY, Kallas PG, Simons YM, Christensen NP, Williams MV. Patient experiences after hospitalizations for elective surgery. Am J Surg. 2014;207(6):855862.
  15. Kable A, Gibberd R, Spigelman A. Complications after discharge for surgical patients. ANZ J Surg. 2004;74(3):9297.
  16. Visser A, Ubbink DT, Gouma DJ, Goslings JC. Surgeons are overlooking post‐discharge complications: a prospective cohort study. World J Surg. 2014;38(5):10191025.
  17. Henderson A, Zernike W. A study of the impact of discharge information for surgical patients. J Adv Nurs. 2001;35(3):435441.
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The period immediately following hospital discharge is particularly hazardous for patients.[1, 2, 3, 4, 5] Problems occurring after discharge may result in high rates of rehospitalization and unscheduled visits to healthcare providers.[6, 7, 8, 9, 10] Numerous investigators have tried to identify patients who are at increased risk for rehospitalizations within 30 days of discharge, and many studies have examined whether various interventions could decrease these adverse events (summarized in Hansen et al.[11]). An increasing fraction of patients discharged by medicine and surgery services have some or all of their care supervised by hospitalists. Thus, hospitals increasingly look to hospitalists for ways to reduce rehospitalizations.

Patients discharged from our hospital are instructed to call an advice line (AL) if and when questions or concerns arise. Accordingly, we examined when these calls were made and what issues were raised, with the idea that the information collected might identify aspects of our discharge processes that needed improvement.

METHODS

Study Design

We conducted a prospective study of a cohort consisting of all unduplicated patients with a matching medical record number in our data warehouse who called our AL between September 1, 2011 and September 1, 2012, and reported being hospitalized or having surgery (inpatient or outpatient) within 30 days preceding their call. We excluded patients who were incarcerated, those who were transferred from other hospitals, those admitted for routine chemotherapy or emergent dialysis, and those discharged to a skilled nursing facility or hospice. The study involved no intervention. It was approved by the Colorado Multiple Institutional Review Board.

Setting

The study was conducted at Denver Health Medical Center, a 525‐bed, university‐affiliated, public safety‐net hospital. At the time of discharge, all patients were given paperwork that listed the telephone number of the AL and written instructions in English or Spanish telling them to call the AL or their primary care physician if they had any of a list of symptoms that was selected by their discharging physician as being relevant to that specific patient's condition(s).

The AL was established in 1997 to provide medical triage to patients of Denver Health. It operates 24 hours a day, 7 days per week, and receives approximately 100,000 calls per year. A language line service is used with nonEnglish‐speaking callers. Calls are handled by a nurse who, with the assistance of a commercial software program (E‐Centaurus; LVM Systems, Phoenix, AZ) containing clinical algorithms (Schmitt‐Thompson Clinical Content, Windsor, CO), makes a triage recommendation. Nurses rarely contact hospital or clinic physicians to assist with triage decisions.

Variables Assessed

We categorized the nature of the callers' reported problem(s) to the AL using the taxonomy summarized in the online appendix (see Supporting Appendix in the online version of this article). We then queried our data warehouse for each patient's demographic information, patient‐level comorbidities, discharging service, discharge date and diagnoses, hospital length of stay, discharge disposition, and whether they had been hospitalized or sought care in our urgent care center or emergency department within 30 days of discharge. The same variables were collected for all unduplicated patients who met the same inclusion and exclusion criteria and were discharged from Denver Health during the same time period but did not call the AL.

Statistics

Data were analyzed using SAS Enterprise Guide 4.1 (SAS Institute, Inc., Cary, NC). Because we made multiple statistical comparisons, we applied the Bonferroni correction when comparing patients calling the AL with those who did not, such that P<0.004 indicated statistical significance. A Student t test or a Wilcoxon rank sum test was used to compare continuous variables depending on results of normality tests. 2 tests were used to compare categorical variables. The intervals between hospital discharge and the call to the AL for patients discharged from medicine versus surgery services were compared using a log‐rank test, with P<0.05 indicating statistical significance.

RESULTS

During the 1‐year study period, 19,303 unique patients were discharged home with instructions regarding the use of the AL. A total of 310 patients called the AL and reported being hospitalized or having surgery within the preceding 30 days. Of these, 2 were excluded (1 who was incarcerated and 1 who was discharged to a skilled nursing facility), leaving 308 patients in the cohort. This represented 1.5% of the total number of unduplicated patients discharged during this same time period (minus the exclusions described above). The large majority of the calls (277/308, 90%) came directly from patients. The remaining 10% came from a proxy, usually a patient's family member. Compared with patients who were discharged during the same time period who did not call the AL, those who called were more likely to speak English, less likely to speak Spanish, more likely to be medically indigent, had slightly longer lengths of stays for their index hospitalization, and were more likely to be discharged from surgery than medicine services (particularly following inpatient surgery) (Table 1).

Patient Characteristics
Patient CharacteristicsPatients Calling Advice Line After Discharge, N=308Patients Not Calling Advice Line After Discharge, N=18,995P Valuea
  • NOTE: Abbreviations: IQR, interquartile range; SD, standard deviation.

  • Bonferroni correction for multiple comparisons was applied, with a P<0.004 indicating significance.

  • Defined as uninsured, ineligible for Medicaid, and unable to purchase private insurance.

  • Defined as 1 or more visits to a primary care provider within 3 years of index hospitalization.

Age, y (meanSD)421739210.0210
Gender, female, n (%)162 (53)10,655 (56) 
Race/ethnicity, n (%)  0.1208
Hispanic/Latino/Spanish129 (42)8,896 (47) 
African American44 (14)2,674 (14) 
White125 (41)6,569 (35) 
Language, n (%)  <0.0001
English273 (89)14,236 (79) 
Spanish32 (10)3,744 (21) 
Payer, n (%)   
Medicare45 (15)3,013 (16) 
Medicaid105 (34)7,777 (41)0.0152
Commercial49 (16)2,863 (15) 
Medically indigentb93 (30)3,442 (18)<0.0001
Self‐pay5 (1)1,070 (5) 
Primary care provider, n (%)c168 (55)10,136 (53)0.6794
Psychiatric comorbidity, n (%)81 (26)4,528 (24)0.3149
Alcohol or substance abuse comorbidity, n (%)65 (21)3,178 (17)0.0417
Discharging service, n (%)  <0.0001
Surgery193 (63)7,247 (38) 
Inpatient123 (40)3,425 (18) 
Ambulatory70 (23)3,822 (20) 
Medicine93 (30)6,038 (32) 
Pediatric4 (1)1,315 (7) 
Obstetric11 (4)3,333 (18) 
Length of stay, median (IQR)2 (04.5)1 (03)0.0003
Inpatient medicine4 (26)3 (15)0.0020
Inpatient surgery3 (16)2 (14)0.0019
Charlson Comorbidity Index, median (IQR)
Inpatient medicine1 (04)1 (02)0.0435
Inpatient surgery0 (01)0 (01)0.0240

The median time from hospital discharge to the call was 3 days (interquartile range [IQR], 16), but 31% and 47% of calls occurred within 24 or 48 hours of discharge, respectively. Ten percent of patients called the AL the same day of discharge (Figure 1). We found no difference in timing of the calls as a function of discharging service.

Figure 1
Timing of calls relative to discharge.

The 308 patients reported a total of 612 problems or concerns (meanstandard deviation number of complaints per caller=21), the large majority of which (71%) were symptom‐related (Table 2). The most common symptom was uncontrolled pain, reported by 33% and 40% of patients discharged from medicine and surgery services, respectively. The next most common symptoms related to the gastrointestinal system and to surgical site issues in medicine and surgery patients, respectively (data not shown).

Frequency of Patient‐Reported Concerns
 Total Cohort, n (%)Patients Discharged From Medicine, n (%)Patients Discharged From Surgery, n (%)
PatientsComplaintsPatientsComplaintsPatientsComplaints
Symptom related280 (91)433 (71)89 (96)166 (77)171 (89)234 (66)
Discharge instructions65 (21)81 (13)18 (19)21 (10)43 (22)56 (16)
Medication related65 (21)87 (14)19 (20)25 (11)39 (20)54 (15)
Other10 (3)11 (2)4 (4)4 (2)6 (3)7 (2)
Total 612 (100) 216 (100) 351 (100)

Sixty‐five patients, representing 21% of the cohort, reported 81 problems understanding or executing discharge instructions. No difference was observed between the fraction of these problems reported by patients from medicine versus surgery (19% and 22%, respectively, P=0.54).

Sixty‐five patients, again representing 21% of the cohort, reported 87 medication‐related problems, 20% from both the medicine and surgery services (P=0.99). Medicine patients more frequently reported difficulties understanding their medication instructions, whereas surgery patients more frequently reported lack of efficacy of medications, particularly with respect to pain control (data not shown).

Thirty percent of patients who called the AL were advised by the nurse to go to the emergency department immediately. Medicine patients were more likely to be triaged to the emergency department compared with surgery patients (45% vs 22%, P<0.0001).

The 30‐day readmission rates and the rates of unscheduled urgent or emergent care visits were higher for patients calling the AL compared with those who did not call (46/308, 15% vs 706/18,995, 4%, and 92/308, 30% vs 1303/18,995, 7%, respectively, both P<0.0001). Similar differences were found for patients discharged from medicine or surgery services who called the AL compared with those who did not (data not shown, both P<0.0001). The median number of days between AL call and rehospitalization was 0 (IQR, 02) and 1 (IQR, 08) for medicine and surgery patients, respectively. Ninety‐three percent of rehospitalizations were related to the index hospitalization, and 78% of patients who were readmitted had no outpatient encounter in the interim between discharge and rehospitalization.

DISCUSSION

We investigated the source and nature of patient telephone calls to an AL following a hospitalization or surgery, and our data revealed the following important findings: (1) nearly one‐half of the calls to the AL occurred within the first 48 hours following discharge; (2) the majority of the calls came from surgery patients, and a greater fraction of patients discharged from surgery services called the AL than patients discharged from medicine services; (3) the most common issues were uncontrolled pain, questions about medications, and problems understanding or executing aftercare instructions (particularly pertaining to the care of surgical wounds); and (4) patients calling the AL had higher rates of 30‐day rehospitalization and of unscheduled urgent or emergent care visits.

The utilization of our patient‐initiated call line was only 1.5%, which was on the low end of the 1% to 10% reported in the literature.[7, 12] This can be attributed to a number of issues that are specific to our system. First, the discharge instructions provided to our patients stated that they should call their primary care provider or the AL if they had questions. Accordingly, because approximately 50% of our patients had a primary care provider in our system, some may have preferentially contacted their primary care provider rather than the AL. Second, the instructions stated that the patients should call if they were experiencing the symptoms listed on the instruction sheet, so those with other problems/complaints may not have called. Third, AL personnel identified patients as being in our cohort by asking if they had been discharged or underwent a surgical procedure within 30‐days of their call. This may have resulted in the under‐reporting of patients who were hospitalized or had outpatient surgical procedures. Fourth, there may have been a number of characteristics specific to patients in our system that reduced the frequency with which they utilized the AL (eg, access to telephones or other community providers).

Most previous studies of patient‐initiated call lines have included them as part of multi‐intervention pre‐ and/or postdischarge strategies.[7, 8, 9, 10, 11, 12, 13] One prior small study compared the information reported by 37 patients who called an AL with that elicited by nurse‐initiated patient contact.[12] The most frequently reported problems in this study were medication‐related issues (43%). However, this study only included medicine patients and did not document the proportion of calls occurring at various time intervals.

The problems we identified (in both medicine and surgery patients) have previously been described,[2, 3, 4, 13, 14, 15, 16] but all of the studies reporting these problems utilized calls that were initiated by health care providers to patients at various fixed intervals following discharge (ie, 730 days). Most of these used a scripted approach seeking responses to specific questions or outcomes, and the specific timing at which the problems arose was not addressed. In contrast, we examined unsolicited concerns expressed by patients calling an AL following discharge whenever they felt sufficient urgency to address whatever problems or questions arose. We found that a large fraction of calls occurred on the day of or within the first 48 hours following discharge, much earlier than when provider‐initiated calls in the studies cited above occurred. Accordingly, our results cannot be used to compare the utility of patient‐ versus provider‐initiated calls, or to suggest that other hospitals should create an AL system. Rather, we suggest that our findings might be complementary to those reported in studies of provider‐initiated calls and only propose that by examining calls placed by patients to ALs, problems with hospital discharge processes (some of which may result in increased rates of readmission) may be discovered.

The observation that such a large fraction of calls to our AL occurred within the first 48 hours following discharge, together with the fact that many of the questions asked or concerns raised pertained to issues that should have been discussed during the discharge process (eg, pain control, care of surgical wounds), suggests that suboptimal patient education was occurring prior to discharge as was suggested by Henderson and Zernike.[17] This finding has led us to expand our patient education processes prior to discharge on both medicine and surgery services. Because our hospitalists care for approximately 90% of the patients admitted to medicine services and are increasingly involved in the care of patients on surgery services, they are integrally involved with such quality improvement initiatives.

To our knowledge this is the first study in the literature that describes both medicine and surgery patients who call an AL because of problems or questions following hospital discharge, categorizes these problems, determines when the patients called following their discharge, and identifies those who called as being at increased risk for early rehospitalizations and unscheduled urgent or emergent care visits. Given the financial penalties issued to hospitals with high 30‐day readmission rates, these patients may warrant more attention than is customarily available from telephone call lines or during routine outpatient follow‐up. The majority of patients who called our AL had Medicare, Medicaid, or a commercial insurance, and, accordingly, may have been eligible for additional services such as home visits and/or expedited follow‐up appointments.

Our study has a number of limitations. First, it is a single‐center study, so the results might not generalize to other institutions. Second, because the study was performed in a university‐affiliated, public safety‐net hospital, patient characteristics and the rates and types of postdischarge concerns that we observed might differ from those encountered in different types of hospitals and/or from those in nonteaching institutions. We would suggest, however, that the idea of using concerns raised by patients discharged from any type of hospital in calls to ALs may similarly identify problems with that specific hospital's discharge processes. Third, the information collected from the AL came from summaries provided by nurses answering the calls rather than from actual transcripts. This could have resulted in insufficient or incorrect information pertaining to some of the variables assessed in Table 2. The information presented in Table 1, however, was obtained from our data warehouse after matching medical record numbers. Fourth, we could have underestimated the number of patients who had 30‐day rehospitalizations and/or unplanned for urgent or emergent care visits if patients sought care at other hospitals. Fifth, the number of patients calling the AL was too small to allow us to do any type of robust matching or multivariable analysis. Accordingly, the differences that appeared between patients who called and those who did not (ie, English speakers, being medically indigent, the length of stay for the index hospitalization and the discharging service) could be the result of inadequate matching or interactions among the variables. Although matching or multivariate analysis might have yielded different associations between patients who called the AL versus those who did not, those who called the AL still had an increased risk of readmission and urgent or emergent visits and may still benefit from targeted interventions. Finally, the fact that only 1.5% of unique patients who were discharged called the AL could have biased our results. Because only 55% and 53% of the patients who did or did not call the AL, respectively, saw primary care physicians within our system within the 3 years prior to their index hospitalization (P=0.679), the frequency of calls to the AL that we observed could have underestimated the frequency with which patients had contact with other care providers in the community.

In summary, information collected from patient‐initiated calls to our AL identified several aspects of our discharge processes that needed improvement. We concluded that our predischarge educational processes for both medicine and surgery services needed modification, especially with respect to pain management, which problems to expect after hospitalization or surgery, and how to deal with them. The high rates of 30‐day rehospitalization and of unscheduled urgent or emergent care visits among patients calling the AL identifies them as being at increased risk for these outcomes, although the likelihood of these events may be related to factors other than just calling the AL.

The period immediately following hospital discharge is particularly hazardous for patients.[1, 2, 3, 4, 5] Problems occurring after discharge may result in high rates of rehospitalization and unscheduled visits to healthcare providers.[6, 7, 8, 9, 10] Numerous investigators have tried to identify patients who are at increased risk for rehospitalizations within 30 days of discharge, and many studies have examined whether various interventions could decrease these adverse events (summarized in Hansen et al.[11]). An increasing fraction of patients discharged by medicine and surgery services have some or all of their care supervised by hospitalists. Thus, hospitals increasingly look to hospitalists for ways to reduce rehospitalizations.

Patients discharged from our hospital are instructed to call an advice line (AL) if and when questions or concerns arise. Accordingly, we examined when these calls were made and what issues were raised, with the idea that the information collected might identify aspects of our discharge processes that needed improvement.

METHODS

Study Design

We conducted a prospective study of a cohort consisting of all unduplicated patients with a matching medical record number in our data warehouse who called our AL between September 1, 2011 and September 1, 2012, and reported being hospitalized or having surgery (inpatient or outpatient) within 30 days preceding their call. We excluded patients who were incarcerated, those who were transferred from other hospitals, those admitted for routine chemotherapy or emergent dialysis, and those discharged to a skilled nursing facility or hospice. The study involved no intervention. It was approved by the Colorado Multiple Institutional Review Board.

Setting

The study was conducted at Denver Health Medical Center, a 525‐bed, university‐affiliated, public safety‐net hospital. At the time of discharge, all patients were given paperwork that listed the telephone number of the AL and written instructions in English or Spanish telling them to call the AL or their primary care physician if they had any of a list of symptoms that was selected by their discharging physician as being relevant to that specific patient's condition(s).

The AL was established in 1997 to provide medical triage to patients of Denver Health. It operates 24 hours a day, 7 days per week, and receives approximately 100,000 calls per year. A language line service is used with nonEnglish‐speaking callers. Calls are handled by a nurse who, with the assistance of a commercial software program (E‐Centaurus; LVM Systems, Phoenix, AZ) containing clinical algorithms (Schmitt‐Thompson Clinical Content, Windsor, CO), makes a triage recommendation. Nurses rarely contact hospital or clinic physicians to assist with triage decisions.

Variables Assessed

We categorized the nature of the callers' reported problem(s) to the AL using the taxonomy summarized in the online appendix (see Supporting Appendix in the online version of this article). We then queried our data warehouse for each patient's demographic information, patient‐level comorbidities, discharging service, discharge date and diagnoses, hospital length of stay, discharge disposition, and whether they had been hospitalized or sought care in our urgent care center or emergency department within 30 days of discharge. The same variables were collected for all unduplicated patients who met the same inclusion and exclusion criteria and were discharged from Denver Health during the same time period but did not call the AL.

Statistics

Data were analyzed using SAS Enterprise Guide 4.1 (SAS Institute, Inc., Cary, NC). Because we made multiple statistical comparisons, we applied the Bonferroni correction when comparing patients calling the AL with those who did not, such that P<0.004 indicated statistical significance. A Student t test or a Wilcoxon rank sum test was used to compare continuous variables depending on results of normality tests. 2 tests were used to compare categorical variables. The intervals between hospital discharge and the call to the AL for patients discharged from medicine versus surgery services were compared using a log‐rank test, with P<0.05 indicating statistical significance.

RESULTS

During the 1‐year study period, 19,303 unique patients were discharged home with instructions regarding the use of the AL. A total of 310 patients called the AL and reported being hospitalized or having surgery within the preceding 30 days. Of these, 2 were excluded (1 who was incarcerated and 1 who was discharged to a skilled nursing facility), leaving 308 patients in the cohort. This represented 1.5% of the total number of unduplicated patients discharged during this same time period (minus the exclusions described above). The large majority of the calls (277/308, 90%) came directly from patients. The remaining 10% came from a proxy, usually a patient's family member. Compared with patients who were discharged during the same time period who did not call the AL, those who called were more likely to speak English, less likely to speak Spanish, more likely to be medically indigent, had slightly longer lengths of stays for their index hospitalization, and were more likely to be discharged from surgery than medicine services (particularly following inpatient surgery) (Table 1).

Patient Characteristics
Patient CharacteristicsPatients Calling Advice Line After Discharge, N=308Patients Not Calling Advice Line After Discharge, N=18,995P Valuea
  • NOTE: Abbreviations: IQR, interquartile range; SD, standard deviation.

  • Bonferroni correction for multiple comparisons was applied, with a P<0.004 indicating significance.

  • Defined as uninsured, ineligible for Medicaid, and unable to purchase private insurance.

  • Defined as 1 or more visits to a primary care provider within 3 years of index hospitalization.

Age, y (meanSD)421739210.0210
Gender, female, n (%)162 (53)10,655 (56) 
Race/ethnicity, n (%)  0.1208
Hispanic/Latino/Spanish129 (42)8,896 (47) 
African American44 (14)2,674 (14) 
White125 (41)6,569 (35) 
Language, n (%)  <0.0001
English273 (89)14,236 (79) 
Spanish32 (10)3,744 (21) 
Payer, n (%)   
Medicare45 (15)3,013 (16) 
Medicaid105 (34)7,777 (41)0.0152
Commercial49 (16)2,863 (15) 
Medically indigentb93 (30)3,442 (18)<0.0001
Self‐pay5 (1)1,070 (5) 
Primary care provider, n (%)c168 (55)10,136 (53)0.6794
Psychiatric comorbidity, n (%)81 (26)4,528 (24)0.3149
Alcohol or substance abuse comorbidity, n (%)65 (21)3,178 (17)0.0417
Discharging service, n (%)  <0.0001
Surgery193 (63)7,247 (38) 
Inpatient123 (40)3,425 (18) 
Ambulatory70 (23)3,822 (20) 
Medicine93 (30)6,038 (32) 
Pediatric4 (1)1,315 (7) 
Obstetric11 (4)3,333 (18) 
Length of stay, median (IQR)2 (04.5)1 (03)0.0003
Inpatient medicine4 (26)3 (15)0.0020
Inpatient surgery3 (16)2 (14)0.0019
Charlson Comorbidity Index, median (IQR)
Inpatient medicine1 (04)1 (02)0.0435
Inpatient surgery0 (01)0 (01)0.0240

The median time from hospital discharge to the call was 3 days (interquartile range [IQR], 16), but 31% and 47% of calls occurred within 24 or 48 hours of discharge, respectively. Ten percent of patients called the AL the same day of discharge (Figure 1). We found no difference in timing of the calls as a function of discharging service.

Figure 1
Timing of calls relative to discharge.

The 308 patients reported a total of 612 problems or concerns (meanstandard deviation number of complaints per caller=21), the large majority of which (71%) were symptom‐related (Table 2). The most common symptom was uncontrolled pain, reported by 33% and 40% of patients discharged from medicine and surgery services, respectively. The next most common symptoms related to the gastrointestinal system and to surgical site issues in medicine and surgery patients, respectively (data not shown).

Frequency of Patient‐Reported Concerns
 Total Cohort, n (%)Patients Discharged From Medicine, n (%)Patients Discharged From Surgery, n (%)
PatientsComplaintsPatientsComplaintsPatientsComplaints
Symptom related280 (91)433 (71)89 (96)166 (77)171 (89)234 (66)
Discharge instructions65 (21)81 (13)18 (19)21 (10)43 (22)56 (16)
Medication related65 (21)87 (14)19 (20)25 (11)39 (20)54 (15)
Other10 (3)11 (2)4 (4)4 (2)6 (3)7 (2)
Total 612 (100) 216 (100) 351 (100)

Sixty‐five patients, representing 21% of the cohort, reported 81 problems understanding or executing discharge instructions. No difference was observed between the fraction of these problems reported by patients from medicine versus surgery (19% and 22%, respectively, P=0.54).

Sixty‐five patients, again representing 21% of the cohort, reported 87 medication‐related problems, 20% from both the medicine and surgery services (P=0.99). Medicine patients more frequently reported difficulties understanding their medication instructions, whereas surgery patients more frequently reported lack of efficacy of medications, particularly with respect to pain control (data not shown).

Thirty percent of patients who called the AL were advised by the nurse to go to the emergency department immediately. Medicine patients were more likely to be triaged to the emergency department compared with surgery patients (45% vs 22%, P<0.0001).

The 30‐day readmission rates and the rates of unscheduled urgent or emergent care visits were higher for patients calling the AL compared with those who did not call (46/308, 15% vs 706/18,995, 4%, and 92/308, 30% vs 1303/18,995, 7%, respectively, both P<0.0001). Similar differences were found for patients discharged from medicine or surgery services who called the AL compared with those who did not (data not shown, both P<0.0001). The median number of days between AL call and rehospitalization was 0 (IQR, 02) and 1 (IQR, 08) for medicine and surgery patients, respectively. Ninety‐three percent of rehospitalizations were related to the index hospitalization, and 78% of patients who were readmitted had no outpatient encounter in the interim between discharge and rehospitalization.

DISCUSSION

We investigated the source and nature of patient telephone calls to an AL following a hospitalization or surgery, and our data revealed the following important findings: (1) nearly one‐half of the calls to the AL occurred within the first 48 hours following discharge; (2) the majority of the calls came from surgery patients, and a greater fraction of patients discharged from surgery services called the AL than patients discharged from medicine services; (3) the most common issues were uncontrolled pain, questions about medications, and problems understanding or executing aftercare instructions (particularly pertaining to the care of surgical wounds); and (4) patients calling the AL had higher rates of 30‐day rehospitalization and of unscheduled urgent or emergent care visits.

The utilization of our patient‐initiated call line was only 1.5%, which was on the low end of the 1% to 10% reported in the literature.[7, 12] This can be attributed to a number of issues that are specific to our system. First, the discharge instructions provided to our patients stated that they should call their primary care provider or the AL if they had questions. Accordingly, because approximately 50% of our patients had a primary care provider in our system, some may have preferentially contacted their primary care provider rather than the AL. Second, the instructions stated that the patients should call if they were experiencing the symptoms listed on the instruction sheet, so those with other problems/complaints may not have called. Third, AL personnel identified patients as being in our cohort by asking if they had been discharged or underwent a surgical procedure within 30‐days of their call. This may have resulted in the under‐reporting of patients who were hospitalized or had outpatient surgical procedures. Fourth, there may have been a number of characteristics specific to patients in our system that reduced the frequency with which they utilized the AL (eg, access to telephones or other community providers).

Most previous studies of patient‐initiated call lines have included them as part of multi‐intervention pre‐ and/or postdischarge strategies.[7, 8, 9, 10, 11, 12, 13] One prior small study compared the information reported by 37 patients who called an AL with that elicited by nurse‐initiated patient contact.[12] The most frequently reported problems in this study were medication‐related issues (43%). However, this study only included medicine patients and did not document the proportion of calls occurring at various time intervals.

The problems we identified (in both medicine and surgery patients) have previously been described,[2, 3, 4, 13, 14, 15, 16] but all of the studies reporting these problems utilized calls that were initiated by health care providers to patients at various fixed intervals following discharge (ie, 730 days). Most of these used a scripted approach seeking responses to specific questions or outcomes, and the specific timing at which the problems arose was not addressed. In contrast, we examined unsolicited concerns expressed by patients calling an AL following discharge whenever they felt sufficient urgency to address whatever problems or questions arose. We found that a large fraction of calls occurred on the day of or within the first 48 hours following discharge, much earlier than when provider‐initiated calls in the studies cited above occurred. Accordingly, our results cannot be used to compare the utility of patient‐ versus provider‐initiated calls, or to suggest that other hospitals should create an AL system. Rather, we suggest that our findings might be complementary to those reported in studies of provider‐initiated calls and only propose that by examining calls placed by patients to ALs, problems with hospital discharge processes (some of which may result in increased rates of readmission) may be discovered.

The observation that such a large fraction of calls to our AL occurred within the first 48 hours following discharge, together with the fact that many of the questions asked or concerns raised pertained to issues that should have been discussed during the discharge process (eg, pain control, care of surgical wounds), suggests that suboptimal patient education was occurring prior to discharge as was suggested by Henderson and Zernike.[17] This finding has led us to expand our patient education processes prior to discharge on both medicine and surgery services. Because our hospitalists care for approximately 90% of the patients admitted to medicine services and are increasingly involved in the care of patients on surgery services, they are integrally involved with such quality improvement initiatives.

To our knowledge this is the first study in the literature that describes both medicine and surgery patients who call an AL because of problems or questions following hospital discharge, categorizes these problems, determines when the patients called following their discharge, and identifies those who called as being at increased risk for early rehospitalizations and unscheduled urgent or emergent care visits. Given the financial penalties issued to hospitals with high 30‐day readmission rates, these patients may warrant more attention than is customarily available from telephone call lines or during routine outpatient follow‐up. The majority of patients who called our AL had Medicare, Medicaid, or a commercial insurance, and, accordingly, may have been eligible for additional services such as home visits and/or expedited follow‐up appointments.

Our study has a number of limitations. First, it is a single‐center study, so the results might not generalize to other institutions. Second, because the study was performed in a university‐affiliated, public safety‐net hospital, patient characteristics and the rates and types of postdischarge concerns that we observed might differ from those encountered in different types of hospitals and/or from those in nonteaching institutions. We would suggest, however, that the idea of using concerns raised by patients discharged from any type of hospital in calls to ALs may similarly identify problems with that specific hospital's discharge processes. Third, the information collected from the AL came from summaries provided by nurses answering the calls rather than from actual transcripts. This could have resulted in insufficient or incorrect information pertaining to some of the variables assessed in Table 2. The information presented in Table 1, however, was obtained from our data warehouse after matching medical record numbers. Fourth, we could have underestimated the number of patients who had 30‐day rehospitalizations and/or unplanned for urgent or emergent care visits if patients sought care at other hospitals. Fifth, the number of patients calling the AL was too small to allow us to do any type of robust matching or multivariable analysis. Accordingly, the differences that appeared between patients who called and those who did not (ie, English speakers, being medically indigent, the length of stay for the index hospitalization and the discharging service) could be the result of inadequate matching or interactions among the variables. Although matching or multivariate analysis might have yielded different associations between patients who called the AL versus those who did not, those who called the AL still had an increased risk of readmission and urgent or emergent visits and may still benefit from targeted interventions. Finally, the fact that only 1.5% of unique patients who were discharged called the AL could have biased our results. Because only 55% and 53% of the patients who did or did not call the AL, respectively, saw primary care physicians within our system within the 3 years prior to their index hospitalization (P=0.679), the frequency of calls to the AL that we observed could have underestimated the frequency with which patients had contact with other care providers in the community.

In summary, information collected from patient‐initiated calls to our AL identified several aspects of our discharge processes that needed improvement. We concluded that our predischarge educational processes for both medicine and surgery services needed modification, especially with respect to pain management, which problems to expect after hospitalization or surgery, and how to deal with them. The high rates of 30‐day rehospitalization and of unscheduled urgent or emergent care visits among patients calling the AL identifies them as being at increased risk for these outcomes, although the likelihood of these events may be related to factors other than just calling the AL.

References
  1. Parrish MM, O'Malley K, Adams RI, Adams SR, Coleman EA. Implementation of the care transitions intervention: sustainability and lessons learned. Prof Case Manag. 2009;14(6):282293.
  2. Arora VM, Prochaska ML, Farnan JM, et al. Problems after discharge and understanding of communication with their primary care physicians among hospitalized seniors: a mixed methods study. J Hosp Med. 2010;5(7):385391.
  3. Forster AJ, Clark HD, Menard A, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345349.
  4. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161167.
  5. Misky GJ, Wald HL, Coleman EA. Post‐hospitalization transitions: examining the effects of timing of primary care provider follow‐up. J Hosp Med. 2010;5(7):392397.
  6. Bostrom J, Caldwell J, McGuire K, Everson D. Telephone follow‐up after discharge from the hospital: does it make a difference? Appl Nurs Res. 1996;9(2) 4752.
  7. Sorknaes AD, Bech M, Madsen H, et al. The effect of real‐time teleconsultations between hospital‐based nurses and patients with severe COPD discharged after an exacerbation. J Telemed Telecare. 2013;19(8):466474.
  8. Kwok T, Lum CM, Chan HS, Ma HM, Lee D, Woo J. A randomized, controlled trial of an intensive community nurse‐supported discharge program in preventing hospital readmissions of older patients with chronic lung disease. J Am Geriatr Soc. 2004;52(8):12401246.
  9. Jaarsma T, Halfens R, Huijer Abu‐Saad H, et al. Effects of education and support on self‐care and resource utilization in patients with heart failure. Eur Heart J. 1999;20(9):673682.
  10. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow‐up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613620.
  11. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520528.
  12. Rennke S, Kesh S, Neeman N, Sehgal NL. Complementary telephone strategies to improve postdischarge communication. Am J Med. 2012;125(1):2830.
  13. Shu CC, Hsu NC, Lin YF, Wang JY, Lin JW, Ko WJ. Integrated postdischarge transitional care in a hospitalist system to improve discharge outcome: an experimental study. BMC Med. 2011;9:96.
  14. Hinami K, Bilimoria KY, Kallas PG, Simons YM, Christensen NP, Williams MV. Patient experiences after hospitalizations for elective surgery. Am J Surg. 2014;207(6):855862.
  15. Kable A, Gibberd R, Spigelman A. Complications after discharge for surgical patients. ANZ J Surg. 2004;74(3):9297.
  16. Visser A, Ubbink DT, Gouma DJ, Goslings JC. Surgeons are overlooking post‐discharge complications: a prospective cohort study. World J Surg. 2014;38(5):10191025.
  17. Henderson A, Zernike W. A study of the impact of discharge information for surgical patients. J Adv Nurs. 2001;35(3):435441.
References
  1. Parrish MM, O'Malley K, Adams RI, Adams SR, Coleman EA. Implementation of the care transitions intervention: sustainability and lessons learned. Prof Case Manag. 2009;14(6):282293.
  2. Arora VM, Prochaska ML, Farnan JM, et al. Problems after discharge and understanding of communication with their primary care physicians among hospitalized seniors: a mixed methods study. J Hosp Med. 2010;5(7):385391.
  3. Forster AJ, Clark HD, Menard A, et al. Adverse events among medical patients after discharge from hospital. CMAJ. 2004;170(3):345349.
  4. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161167.
  5. Misky GJ, Wald HL, Coleman EA. Post‐hospitalization transitions: examining the effects of timing of primary care provider follow‐up. J Hosp Med. 2010;5(7):392397.
  6. Bostrom J, Caldwell J, McGuire K, Everson D. Telephone follow‐up after discharge from the hospital: does it make a difference? Appl Nurs Res. 1996;9(2) 4752.
  7. Sorknaes AD, Bech M, Madsen H, et al. The effect of real‐time teleconsultations between hospital‐based nurses and patients with severe COPD discharged after an exacerbation. J Telemed Telecare. 2013;19(8):466474.
  8. Kwok T, Lum CM, Chan HS, Ma HM, Lee D, Woo J. A randomized, controlled trial of an intensive community nurse‐supported discharge program in preventing hospital readmissions of older patients with chronic lung disease. J Am Geriatr Soc. 2004;52(8):12401246.
  9. Jaarsma T, Halfens R, Huijer Abu‐Saad H, et al. Effects of education and support on self‐care and resource utilization in patients with heart failure. Eur Heart J. 1999;20(9):673682.
  10. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow‐up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613620.
  11. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520528.
  12. Rennke S, Kesh S, Neeman N, Sehgal NL. Complementary telephone strategies to improve postdischarge communication. Am J Med. 2012;125(1):2830.
  13. Shu CC, Hsu NC, Lin YF, Wang JY, Lin JW, Ko WJ. Integrated postdischarge transitional care in a hospitalist system to improve discharge outcome: an experimental study. BMC Med. 2011;9:96.
  14. Hinami K, Bilimoria KY, Kallas PG, Simons YM, Christensen NP, Williams MV. Patient experiences after hospitalizations for elective surgery. Am J Surg. 2014;207(6):855862.
  15. Kable A, Gibberd R, Spigelman A. Complications after discharge for surgical patients. ANZ J Surg. 2004;74(3):9297.
  16. Visser A, Ubbink DT, Gouma DJ, Goslings JC. Surgeons are overlooking post‐discharge complications: a prospective cohort study. World J Surg. 2014;38(5):10191025.
  17. Henderson A, Zernike W. A study of the impact of discharge information for surgical patients. J Adv Nurs. 2001;35(3):435441.
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Address for correspondence and reprint requests: Sarah A. Stella, MD, Denver Health, 777 Bannock, MC 4000, Denver, CO 80204; Telephone: 303‐596‐1511; Fax: 303‐602‐5056; E‐mail: sarah.stella@dhha.org
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Curbside vs Formal Consultation

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Prospective comparison of curbside versus formal consultations

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).

Characteristics of Curbside Consultations (N = 47)
 Curbside Consultations, N (%)
 47 (100)
  • Consultations could be listed in more than one category; accordingly, the totals exceed 100%.

Requesting service 
Psychiatry21 (45)
Emergency Department9 (19)
Obstetrics/Gynecology5 (11)
Neurology4 (8)
Other (Orthopedics, Anesthesia, General Surgery, Neurosurgery, and Interventional Radiology)8 (17)
Requesting provider 
Resident25 (53)
Intern8 (17)
Attending9 (19)
Other5 (11)
Consultative issue* 
Diagnosis10 (21)
Treatment29 (62)
Evaluation20 (43)
Discharge13 (28)
Lab interpretation4 (9)
Medical concern* 
Cardiac27 (57)
Endocrine17 (36)
Infectious disease9 (19)
Pulmonary8 (17)
Gastroenterology6 (13)
Fluid and electrolyte6 (13)
Others23 (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 Consultation Assessment
 Curbside Consultations, N (%)
 TotalAccurate and CompleteInaccurate or Incomplete
47 (100)23 (49)24 (51)
  • P < 0.001

  • P < 0.0001.

Advice in formal consultation differed from advice in curbside consultation26 (55)7 (30)19 (79)*
Formal consultation changed management28 (60)6 (26)22 (92)
Minor change18 (64)6 (100)12 (55)
Major change10 (36)0 (0)10 (45)
Curbside consultation insufficient18 (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.

Files
References
  1. Myers JP.Curbside consultation in infectious diseases: a prospective study.J Infect Dis.1984;150:797802.
  2. Keating NL,Zaslavsky AM,Ayanian JZ.Physicians' experiences and beliefs regarding informal consultation.JAMA.1998;280:900904.
  3. Kuo D,Gifford DR,Stein MD.Curbside consultation practices and attitudes among primary care physicians and medical subspecialists.JAMA.1998;280:905909.
  4. Grace C,Alston WK,Ramundo M,Polish L,Kirkpatrick B,Huston C.The complexity, relative value, and financial worth of curbside consultations in an academic infectious diseases unit.Clin Infect Dis.2010;51:651655.
  5. Manian FA,Janssen DA.Curbside consultations. A closer look at a common practice.JAMA.1996;275:145147.
  6. Weinberg AD,Ullian L,Richards WD,Cooper P.Informal advice‐ and information‐seeking between physicians.J Med Educ.1981;56;174180.
  7. Manian FA,McKinsey DS.A prospective study of 2,092 “curbside” questions asked of two infectious disease consultants in private practice in the midwest.Clin Infect Dis.1996;22:303307.
  8. Findling JW,Shaker JL,Brickner RC,Riordan PR,Aron DC.Curbside consultation in endocrine practice: a prospective observational study.Endocrinologist.1996;6:328331.
  9. Pearson SD,Moreno R,Trnka Y.Informal consultations provided to general internists by the gastroenterology department of an HMO.J Gen Intern Med.1998;13:435438.
  10. Muntz HG.“Curbside” consultations in gynecologic oncology: a closer look at a common practice.Gynecol Oncol.1999;74:456459.
  11. Leblebicioglu H,Akbulut A,Ulusoy S, et al.Informal consultations in infectious diseases and clinical microbiology practice.Clin Microbiol Infect.2003;9:724726.
  12. Golub RM.Curbside consultations and the viaduct effect.JAMA.1998;280:929930.
  13. Borowsky SJ.What do we really need to know about consultation and referral?J Gen Intern Med.1998;13:497498.
  14. Cartmill M,White BD.Telephone advice for neurosurgical referrals. Who assumes duty of care?Br J Neurosurg.2001;15:453455.
  15. Olick RS,Bergus GR.Malpractice liability for informal consultations.Fam Med.2003;35:476481.
  16. Bergus GR,Randall CS,Sinift SD,Rosenthal DM.Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?Arch Fam Med.2000;9:541547.
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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).

Characteristics of Curbside Consultations (N = 47)
 Curbside Consultations, N (%)
 47 (100)
  • Consultations could be listed in more than one category; accordingly, the totals exceed 100%.

Requesting service 
Psychiatry21 (45)
Emergency Department9 (19)
Obstetrics/Gynecology5 (11)
Neurology4 (8)
Other (Orthopedics, Anesthesia, General Surgery, Neurosurgery, and Interventional Radiology)8 (17)
Requesting provider 
Resident25 (53)
Intern8 (17)
Attending9 (19)
Other5 (11)
Consultative issue* 
Diagnosis10 (21)
Treatment29 (62)
Evaluation20 (43)
Discharge13 (28)
Lab interpretation4 (9)
Medical concern* 
Cardiac27 (57)
Endocrine17 (36)
Infectious disease9 (19)
Pulmonary8 (17)
Gastroenterology6 (13)
Fluid and electrolyte6 (13)
Others23 (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 Consultation Assessment
 Curbside Consultations, N (%)
 TotalAccurate and CompleteInaccurate or Incomplete
47 (100)23 (49)24 (51)
  • P < 0.001

  • P < 0.0001.

Advice in formal consultation differed from advice in curbside consultation26 (55)7 (30)19 (79)*
Formal consultation changed management28 (60)6 (26)22 (92)
Minor change18 (64)6 (100)12 (55)
Major change10 (36)0 (0)10 (45)
Curbside consultation insufficient18 (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).

Characteristics of Curbside Consultations (N = 47)
 Curbside Consultations, N (%)
 47 (100)
  • Consultations could be listed in more than one category; accordingly, the totals exceed 100%.

Requesting service 
Psychiatry21 (45)
Emergency Department9 (19)
Obstetrics/Gynecology5 (11)
Neurology4 (8)
Other (Orthopedics, Anesthesia, General Surgery, Neurosurgery, and Interventional Radiology)8 (17)
Requesting provider 
Resident25 (53)
Intern8 (17)
Attending9 (19)
Other5 (11)
Consultative issue* 
Diagnosis10 (21)
Treatment29 (62)
Evaluation20 (43)
Discharge13 (28)
Lab interpretation4 (9)
Medical concern* 
Cardiac27 (57)
Endocrine17 (36)
Infectious disease9 (19)
Pulmonary8 (17)
Gastroenterology6 (13)
Fluid and electrolyte6 (13)
Others23 (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 Consultation Assessment
 Curbside Consultations, N (%)
 TotalAccurate and CompleteInaccurate or Incomplete
47 (100)23 (49)24 (51)
  • P < 0.001

  • P < 0.0001.

Advice in formal consultation differed from advice in curbside consultation26 (55)7 (30)19 (79)*
Formal consultation changed management28 (60)6 (26)22 (92)
Minor change18 (64)6 (100)12 (55)
Major change10 (36)0 (0)10 (45)
Curbside consultation insufficient18 (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.

References
  1. Myers JP.Curbside consultation in infectious diseases: a prospective study.J Infect Dis.1984;150:797802.
  2. Keating NL,Zaslavsky AM,Ayanian JZ.Physicians' experiences and beliefs regarding informal consultation.JAMA.1998;280:900904.
  3. Kuo D,Gifford DR,Stein MD.Curbside consultation practices and attitudes among primary care physicians and medical subspecialists.JAMA.1998;280:905909.
  4. Grace C,Alston WK,Ramundo M,Polish L,Kirkpatrick B,Huston C.The complexity, relative value, and financial worth of curbside consultations in an academic infectious diseases unit.Clin Infect Dis.2010;51:651655.
  5. Manian FA,Janssen DA.Curbside consultations. A closer look at a common practice.JAMA.1996;275:145147.
  6. Weinberg AD,Ullian L,Richards WD,Cooper P.Informal advice‐ and information‐seeking between physicians.J Med Educ.1981;56;174180.
  7. Manian FA,McKinsey DS.A prospective study of 2,092 “curbside” questions asked of two infectious disease consultants in private practice in the midwest.Clin Infect Dis.1996;22:303307.
  8. Findling JW,Shaker JL,Brickner RC,Riordan PR,Aron DC.Curbside consultation in endocrine practice: a prospective observational study.Endocrinologist.1996;6:328331.
  9. Pearson SD,Moreno R,Trnka Y.Informal consultations provided to general internists by the gastroenterology department of an HMO.J Gen Intern Med.1998;13:435438.
  10. Muntz HG.“Curbside” consultations in gynecologic oncology: a closer look at a common practice.Gynecol Oncol.1999;74:456459.
  11. Leblebicioglu H,Akbulut A,Ulusoy S, et al.Informal consultations in infectious diseases and clinical microbiology practice.Clin Microbiol Infect.2003;9:724726.
  12. Golub RM.Curbside consultations and the viaduct effect.JAMA.1998;280:929930.
  13. Borowsky SJ.What do we really need to know about consultation and referral?J Gen Intern Med.1998;13:497498.
  14. Cartmill M,White BD.Telephone advice for neurosurgical referrals. Who assumes duty of care?Br J Neurosurg.2001;15:453455.
  15. Olick RS,Bergus GR.Malpractice liability for informal consultations.Fam Med.2003;35:476481.
  16. Bergus GR,Randall CS,Sinift SD,Rosenthal DM.Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?Arch Fam Med.2000;9:541547.
References
  1. Myers JP.Curbside consultation in infectious diseases: a prospective study.J Infect Dis.1984;150:797802.
  2. Keating NL,Zaslavsky AM,Ayanian JZ.Physicians' experiences and beliefs regarding informal consultation.JAMA.1998;280:900904.
  3. Kuo D,Gifford DR,Stein MD.Curbside consultation practices and attitudes among primary care physicians and medical subspecialists.JAMA.1998;280:905909.
  4. Grace C,Alston WK,Ramundo M,Polish L,Kirkpatrick B,Huston C.The complexity, relative value, and financial worth of curbside consultations in an academic infectious diseases unit.Clin Infect Dis.2010;51:651655.
  5. Manian FA,Janssen DA.Curbside consultations. A closer look at a common practice.JAMA.1996;275:145147.
  6. Weinberg AD,Ullian L,Richards WD,Cooper P.Informal advice‐ and information‐seeking between physicians.J Med Educ.1981;56;174180.
  7. Manian FA,McKinsey DS.A prospective study of 2,092 “curbside” questions asked of two infectious disease consultants in private practice in the midwest.Clin Infect Dis.1996;22:303307.
  8. Findling JW,Shaker JL,Brickner RC,Riordan PR,Aron DC.Curbside consultation in endocrine practice: a prospective observational study.Endocrinologist.1996;6:328331.
  9. Pearson SD,Moreno R,Trnka Y.Informal consultations provided to general internists by the gastroenterology department of an HMO.J Gen Intern Med.1998;13:435438.
  10. Muntz HG.“Curbside” consultations in gynecologic oncology: a closer look at a common practice.Gynecol Oncol.1999;74:456459.
  11. Leblebicioglu H,Akbulut A,Ulusoy S, et al.Informal consultations in infectious diseases and clinical microbiology practice.Clin Microbiol Infect.2003;9:724726.
  12. Golub RM.Curbside consultations and the viaduct effect.JAMA.1998;280:929930.
  13. Borowsky SJ.What do we really need to know about consultation and referral?J Gen Intern Med.1998;13:497498.
  14. Cartmill M,White BD.Telephone advice for neurosurgical referrals. Who assumes duty of care?Br J Neurosurg.2001;15:453455.
  15. Olick RS,Bergus GR.Malpractice liability for informal consultations.Fam Med.2003;35:476481.
  16. Bergus GR,Randall CS,Sinift SD,Rosenthal DM.Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues?Arch Fam Med.2000;9:541547.
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Journal of Hospital Medicine - 8(1)
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Journal of Hospital Medicine - 8(1)
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