User login
Late Discharges from a Medical Ward
In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.
How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.
Patients and Methods
Setting
The setting was the Hospitalist Unit of a single teaching hospital in Baltimore (The Johns Hopkins Hospital) from January 1, 2005 to April 30, 2005. Patients entered the cohort upon initiation of the discharge process by the hospitalist team on the Hospitalist Unit, and were followed until they were discharged alive from the hospital.
There were no published data on which to base firm a priori sample size calculations. Based on pilot data, we estimated that a sample size of about 170 would yield precise estimates for means and standard deviations, giving us 80% to 90% power to determine differences in time intervals across categories, with alpha set to 0.05. We estimated that we would need 4 months of data collection to achieve this sample size.
During the period of study, the 16‐bed unit was staffed with in‐house hospitalist attending physicians without house‐staff, from 7 AM (weekdays) or 8 AM (weekends) to 10 PM (Monday to Thursday) or 8 PM (Friday to Sunday). The hospitalist unit had 24‐hour physician coverage, but attending physicians provided overnight coverage from home (backed up by in‐house residents for patient care emergencies). Handoffs of patient care from one attending physician to another typically occurred on Friday afternoon or Monday morning. The unit had 1 dedicated social worker and a nurse clinician who provided part‐time assistance with discharge planning.
Outcome Measurements
We defined the start of the discharge process as the time the patient's last medically necessary test was needed by his or her attending physician. Specifically, physicians were asked when the results of this test first would have been useful in clearing the patient for discharge. In the remainder of this work, we will refer to the start of the discharge process as time decisive test needed.
The end of the discharge process was called the discharge time, and defined as the time the unit clerk saw the patient leave the unit. We defined early discharges as those occurring before the median hour of discharge (3:00 PM), and late discharges as those occurring at or after this hour.
A focus group composed of nurses, physicians, social worker, unit clerks, and support associates (group responsible for cleaning patient rooms) volunteered to map out the discharge process. Based on these discussions, durations in the discharge process were defined as follows: (1) duration 1: time decisive test needed, until time the attending physician was aware of test results; (2) duration 2: time the physician was aware of test results until discharge paperwork was complete; (3) duration 3: time discharge paperwork complete until patient leaves unit; and (4) total discharge duration: time decisive test needed until patient leaves unit.
Exposure Measurements
We categorized exposures into 5 groups: (1) demographics (age, gender, race, source of patient such as outside hospital versus emergency department versus other, and payer on discharge); (2) clinical characteristics (length of stay, any psychiatric diagnosis, any substance abuse diagnosis, and severity of illness); (3) system characteristics (departmental occupancy defined as proportion of hospital beds designated for Department of Medicine patients that were occupied on the day of discharge); (4) last test characteristics (physical exam, laboratory test, procedure, and consult); and (5) discharge characteristics (discharged to home versus not discharged to home, prescriptions filled in hospital pharmacy prior to discharge, and ambulance required for transport).
Psychiatric diagnosis was defined as the presence of any of the following International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes: 290319 (any fourth or fifth digits).27 Substance abuse diagnosis was defined as the presence of any of the following ICD‐9‐CM codes: 303305 (any fourth or fifth digits). Substance abuse codes encompassed drug dependence and abuse, including alcohol dependence and abuse.
The all patient refined diagnosis related group relative weight (APRDRGwt) is a unitless number that estimates the total cost of care for inpatients, based on clinical and demographic characteristics.28 A patient with a relative APRDRGwt of 1 is predicted to have the same cost of care as the national average for inpatients. A patient with a score of 2 is predicted to be twice as costly as the average. In this study, we used APRDRGwt as a gross proxy for severity of illness.
Adjusted length of stay was measured as length of stay minus discharge duration. This adjustment was made to avoid including the exposure (length of stay) in the outcome (discharge duration). Unadjusted length of stay was used when the outcome was discharge time.
Data Sources
We created a separate 4‐item to 9‐item paper questionnaire (included in the Appendix) for each of 4 functional groups participating directly in the discharge process: nurses, physicians, social worker, and unit clerks. Questions were based on staff feedback about the sequence of steps in the discharge process, and potential reasons for delay. The surveys were piloted for several weeks to further refine the wording of questions, and to ensure that the length and location of the surveys minimized workflow interruptions. The questionnaires captured information about the timing of routine events not recorded in existing databases.
Physicians were asked to identify the last test/procedure/consult needed prior to the patient being medically ready for discharge. They were asked when the test results first could have cleared the patient for discharge (time decisive test needed), and when they actually received the test results (time test results back). Nursing and social work surveys provided information on whether or not prescriptions were filled prior to discharge, and the type of transportation used on discharge. Unit clerks documented when the patient left the unit.
Response rates were: nurses (97%), physicians (97%), social worker (99%), and unit clerks (94%). All 4 surveys were completed for 88% of the 209 included patients (prior to 8 exclusions for missing data or extreme outlier observations). Group response rates were tallied at the end of each month and posted on the unit. We did not track how soon after discharge the surveys were completed. However, we reviewed survey responses frequently (often daily, at most every 4 days) and if surveys were incomplete we personally approached staff members to complete the survey.
We supplemented and cross‐checked data from the questionnaire with information from existing hospital databases. These databases were: (1) the patient's medical record for time patient arrived on the floor, and completion time for consults/procedures; (2) the Electronic Bedboard (EBB) for time patient left the unit (as recorded by unit clerk); (3) the Patient Order Entry System for time discharge papers were completed by the physician, and ordering time for select tests; (4) the Electronic Patient Record for demographic information and completion time for select tests; and (5) Datamart, the hospital's administrative/billing database, for information such as length of stay, diagnosis, patient demographics, and insurance status.
Cross‐checking of data and calculation of durations 1, 2, and 3 identified areas of disagreement that were addressed in the following way. Discharge time was provided by 3 sources: social worker and nurses as an ad hoc addition to each of their surveys, unit clerks as a mandatory question on their survey, and unit clerks as entered in the EBB. We used EBB data for discharge time, as this was the most complete and accurate single source of data. However, survey results and knowledge about the sequential process for discharge, suggested that in 20 cases EBB data did not provide the most accurate time. In these cases, discharge time was provided by the unit clerk survey (16 cases), the social work survey (3 cases), and the nursing survey (1 case).
In 28 cases (14%), discharge paperwork was completed before decisive test results were back. And in 8 cases (4%) test results were received earlier than needed. As these were a minority of cases, these negative durations were converted to zero for analysis.
Statistical Analysis
The unit of analysis was the unique hospital discharge. For patients who were discharged from the Hospitalist Unit more than once during the 4‐month study period, each discharge was treated as a separate unit of analysis.
We defined patients discharged before the median discharge time as early discharges, and all others as late discharges. We then categorized patients with discharge durations less than 24 hours as short discharges, and all others as long discharges.
We described the characteristics of 2 groups of patients: early and short discharges versus all others. We used the chi square statistic and Fisher's exact test (when frequency 5 in 1 or both groups) to test the null hypothesis that there was no association between the 2 groups and select patient characteristics. When comparing medians, we used the nonparametric equality of medians test.
For each step in the discharge process, we identified a median time of occurrence. For the first point in the processtime decisive test neededwe also used 1‐way analysis of variance and the F‐test to assess whether or not timing varied significantly by physician.
Because our primary goal was to quantify in hours the association between various factors and discharge time or duration, we used bivariate linear regression models to identify factors associated with time of discharge (primary analysis) and total duration of the discharge process (secondary analysis). We then used multivariate linear regression to identify factors associated with both outcomes. We used forward and backward selection methods to choose the final models for the multivariate analyses, after forcing in the variables for race, sex, and age. Both methods of selection produced identical results. We assessed for colinearity using variance inflation factors.29
Sensitivity Analyses
For both discharge time and discharge duration, we performed regression diagnostics including leverage, Studentized residuals, and influence. Excluding outliers for influence slightly altered the results of our multivariate analyses. However, all variables that were significant at the P < 0.05 level remained significant in the models without outliers.
We chose to include outliers for influence in our final data set after verifying the data as accurate. For discharge time, the number of outliers (3; 1.5%) for influence was in the range expected for a normally distributed data set.
We also tested for normality of the 2 outcome variables. Discharge time was normally distributed, but discharge duration was not. Because of this, we used 2 additional methods to assess the robustness of our results for discharge duration.
First, we log‐transformed the outcome and repeated the analysis. Variables significant in the non‐log‐transformed model remained significant after log‐transformation. Second, we applied bootstrapping30 with 1,000 repetitions for the bivariate and multivariate analyses. The 95% confidence intervals (CIs) (using the bias‐corrected confidence intervals) were modestly altered (some narrowed, some widened), but our conclusions remained the same except for a single variable with borderline significance (payer on discharge) in bivariate analysis. The final reported confidence intervals for discharge duration are based on our analysis without bootstrapping.
Results
Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.
The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.
| Early and Short Discharges (n = 75)* | All Other Discharges (n = 126) | P Value | |
|---|---|---|---|
| |||
| Demographics | |||
| Age (years) | |||
| Median | 55 | 55 | 0.73 |
| Range | (19, 94) | (20, 90) | |
| Gender (%) | |||
| Female | 61.3 | 61.1 | 0.98 |
| Race (%) | 0.08 | ||
| African American | 76.0 | 60.3 | |
| Caucasian | 21.3 | 34.9 | |
| Other | 2.7 | 4.8 | |
| Payor on discharge (%) | 0.29 | ||
| Medicaid | 32.0 | 20.6 | |
| Medicare | 32.0 | 38.9 | |
| Self‐pay | 12.0 | 16.7 | |
| Other | 24.0 | 23.8 | |
| Clinical characteristics | |||
| Adjusted length of stay (days) | |||
| Median | 3 | 3 | 0.19 |
| Range | (<1, 20) | (<1, 138) | |
| Substance abuse (%) | 41.3 | 37.3 | 0.57 |
| Psychiatric diagnosis (%) | 20.0 | 25.4 | 0.38 |
| Last test characteristics | |||
| Test type (%) | <0.001 | ||
| Exam | 42.7 | 26.2 | |
| Laboratory test | 38.7 | 13.5 | |
| Procedure | 10.7 | 35.7 | |
| Consult | 8.0 | 24.6 | |
| Discharge characteristics | |||
| Discharged to home (%) | 93.3 | 71.4 | <0.001 |
| Prescriptions filled prior to discharge (%) | 10.7 | 19.8 | 0.09 |
| Ambulance required for transport (%) | 8.0 | 21.4 | 0.01 |
Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.
The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).
For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).
Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).
Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.
Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.
The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.
| Adjusted Coefficient, Discharge Time as Outcome in Hours (95% CI)* | P Value | Adjusted Coefficient, Discharge Duration as Outcome in Hours (95% CI) | P Value | |
|---|---|---|---|---|
| ||||
| Demographics | ||||
| Age in quartiles (years) | ||||
| 7294 | 0.5 (0.5, 1.6) | 0.33 | 0.6 (8.3, 7.2) | 0.88 |
| 5671 | 0.5 (0.6, 1.6) | 0.41 | 1.3 (9.2, 6.6) | 0.75 |
| 4455 | 0.2 (0.9, 1.3) | 0.74 | 1.2 (9.0, 6.5) | 0.76 |
| Male gender | 0.0 (0.8, 0.8) | 0.97 | 1.2 (6.8, 4.5) | 0.69 |
| African American race | 0.1 (0.7, 0.9) | 0.80 | 0.3 (5.6, 6.1) | 0.93 |
| Last test characteristics | ||||
| Test type | ||||
| Consult | 1.2 (0.1, 2.4) | 0.04 | 14.8 (6.5, 23.1) | 0.001 |
| Procedure | 1.1 (0.1, 2.1) | 0.03 | 13.4 (6.0, 20.7) | <0.001 |
| Laboratory test | 0.8 (1.8, 0.3) | 0.14 | 0.9 (8.4, 6.6) | 0.82 |
| Exam (reference) | ||||
| Discharge needs | ||||
| Prescriptions filled prior to discharge | 1.4 (0.3, 2.4) | 0.02 | ||
| Not discharged to home | 28.9 (21.9, 35.9) | <0.001 | ||
| Ambulance required for transport | 1.5 (0.4, 2.5) | 0.007 | ||
We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.
Conclusions
We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.
In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.
The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.
Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538
Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.
We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.
Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.
Acknowledgements
The authors thank the patients and staff of the Hospitalist Unit for their participation in this study. In addition, they sincerely thank Paul X. Allen, Richard O. Davis, Ronald R. Peterson, and Shuai Shao. They also thank the anonymous reviewers for their thoughtful comments.
- ,,,,.Emergency departments and crowding in United States teaching hospitals.Ann Emerg Med.1991;20:980–986.
- ,,,,,.A conceptual model of emergency department crowding.Ann Emerg Med.2003;42:173–180.
- ,,.Frequent overcrowding in U.S. emergency departments.Acad Emerg Med.2001;8:151–155.
- ,,,,.The effect of hospital occupancy on emergency department length of stay and patient disposition.Acad Emerg Med.2003;10:127–133.
- .Calculus, chaos, and other models of emergency department crowding.Ann Emerg Med.2003;42:181–184.
- ,.Increased health care costs associated with ED overcrowding.Am J Emerg Med.1994;12:265–266.
- ,,, et al.Decreased health care quality associated with emergency department overcrowding.Eur J Emerg Med.1999;6:105–107.
- ,,.Can good bed management solve the overcrowding in accident and emergency departments?Emerg Med J.2003;20:149–155.
- ,.Emergency department overcrowding in the United States: an emerging threat to patient safety and public health.Emerg Med J.2003;20:402–405.
- General Accounting Office. Hospital emergency departments: crowded conditions vary among hospitals and communities. 2003:GAO‐03–460. Available at: http://www.gao.gov/new.items/d03460.pdf. Accessed October.
- ,.Do you want to die?Time.1990;135:58–60+
- .The ER crisis.Ladies Home J.2001;118:70–79.
- .Placing emergency department crowding on the decision agenda.Nurs Econ.2005;23:14–24.
- .Crisis seen in nation's ER care. Washington Post. June 15,2006;A01.
- Committee on the Future of Emergency Care in the United States Health System.Hospital‐based Emergency Care: at the Breaking Point.Washington DC:National Academies Press;2006.
- ,,, et al.Waits to see an emergency department physician: U.S. trends and predictors, 1997–2004.Health Aff.2008;27:w84–w95.
- Advisory Board.Maximizing Hospital Capacity: Expediting Throughput in an Era of Shortage. Health Care Advisory Board research study.Washington DC:Advisory Board; September 12,2002.
- ,,,.Does U.S. hospital capacity need to be expanded?Health Aff.2003;22:40–54.
- Institute for Healthcare Improvement.Optimizing Patient Flow: Moving Patients Smoothly Through Acute Care Settings.Cambridge, MA:Institute for Healthcare Improvement;2003.
- ,. Leveraging six sigma to improve hospital bed availability. Available at: http://healthcare.isixsigma.com/library/content/c030708a.asp. Accessed October2008.
- University Health System Consortium. Managing patient flow implementation: 2005 benchmarking project field brief. Available at: http://www.musc.edu/medcenter/UHC‐CCP/PFMgmtFldBk.pdf.
- ,,,.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;1:CD000313.
- ,,, et al.Predictors of inappropriate hospital days in a department of internal medicine.Int J Epidemiol.1998;27:513–519.
- ,,.Utilisation of hospital beds by the elderly—a cohort study of admissions to a teaching hospital.Ir Med J.1995;88:124–126.
- ,,,,,.Demographic and clinical characteristics as predictors of length of hospitalization and readmission.J Clin Psychol.1977;33:1093–1099.
- ,,,.The epidemiology of delays in a teaching hospital: the development and use of a tool that detects unnecessary hospital days.Med Care.1989;27:112–129.
- American Medical Association.AMA Physician ICD‐9‐CM 2004, Vols 1 and 2.Eden Prairie, MN:Ingenix;2003.
- 3M Health Information Systems and the Maryland Health Care Payment System: frequently asked questions.2004. Available at: http://multimedia.mmm.com/mws/mediawebserver.dyn?6666660Zjcf6lVs6EVs66S7wwCOrrrrQ‐.
- .Statistics with STATA.Belmont, CA:Duxbury Press;2004.
- ,.Statistical data analysis in the computer age.Science.1991;253:390–395.
- ,,,,.Psychological comorbidity and length of stay in the general hospital.Am J Psychiatry.1991;148(3):324–329.
- ,,,,,.Psychiatric comorbidity and greater hospitalization risk, longer length of stay, and higher hospitalization costs in older adults with heart failure.J Am Geriatr Soc.2007;55:1585–1591.
- ,,,.Does psychiatric comorbidity increase the length of stay in general hospitals?Gen Hosp Psychiatry.2001;23:8–14.
- ,,.The impact of psychiatric comorbidity on length of stay of medical inpatients.Gen Hosp Psychiatry.2003;25:14–19.
- ,,,,,.The implications of regional variations in Medicare spending. Part 1: The content, quality, and accessibility of care.Ann Intern Med.2003;138:273–287.
- ,,,,,.The implications of regional variations in Medicare spending. Part 2: Health outcomes and satisfaction with care.Ann Intern Med.2003;138:288–298.
- ,,, et al.Is early too early? Effect of shorter stays after bypass surgery.Ann Thorac Surg.2007;83:100–107.
- ,.Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization.JAMA.2003;290:1868–1874.
- ,,, et al.Comprehensive discharge planning and home follow‐up of hospitalized elders, a randomized clinical trial.JAMA.1999;281:613–620.
- ,,,,,.Comprehensive discharge planning for the hospitalized elderly, a randomized clinical trial.Ann Intern Med.1994;120:999–1006.
- ,.Evaluating hospital discharge planning: a randomized clinical trial.Med Care.1993;31:358–370.
- ,,,,.Does a dedicated discharge coordinator improve the quality of hospital discharge?Qual Health Care.1996;5:89–96.
- ,,, et al.The effect of a hospitalist service with nurse discharge planner on patient care in an academic teaching hospital.Am J Med.2001;111:627–632.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291:1358–1367.
- ,,.Does increased access to primary care reduce hospital readmissions?N Engl J Med.1996;334:1441–1447.
In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.
How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.
Patients and Methods
Setting
The setting was the Hospitalist Unit of a single teaching hospital in Baltimore (The Johns Hopkins Hospital) from January 1, 2005 to April 30, 2005. Patients entered the cohort upon initiation of the discharge process by the hospitalist team on the Hospitalist Unit, and were followed until they were discharged alive from the hospital.
There were no published data on which to base firm a priori sample size calculations. Based on pilot data, we estimated that a sample size of about 170 would yield precise estimates for means and standard deviations, giving us 80% to 90% power to determine differences in time intervals across categories, with alpha set to 0.05. We estimated that we would need 4 months of data collection to achieve this sample size.
During the period of study, the 16‐bed unit was staffed with in‐house hospitalist attending physicians without house‐staff, from 7 AM (weekdays) or 8 AM (weekends) to 10 PM (Monday to Thursday) or 8 PM (Friday to Sunday). The hospitalist unit had 24‐hour physician coverage, but attending physicians provided overnight coverage from home (backed up by in‐house residents for patient care emergencies). Handoffs of patient care from one attending physician to another typically occurred on Friday afternoon or Monday morning. The unit had 1 dedicated social worker and a nurse clinician who provided part‐time assistance with discharge planning.
Outcome Measurements
We defined the start of the discharge process as the time the patient's last medically necessary test was needed by his or her attending physician. Specifically, physicians were asked when the results of this test first would have been useful in clearing the patient for discharge. In the remainder of this work, we will refer to the start of the discharge process as time decisive test needed.
The end of the discharge process was called the discharge time, and defined as the time the unit clerk saw the patient leave the unit. We defined early discharges as those occurring before the median hour of discharge (3:00 PM), and late discharges as those occurring at or after this hour.
A focus group composed of nurses, physicians, social worker, unit clerks, and support associates (group responsible for cleaning patient rooms) volunteered to map out the discharge process. Based on these discussions, durations in the discharge process were defined as follows: (1) duration 1: time decisive test needed, until time the attending physician was aware of test results; (2) duration 2: time the physician was aware of test results until discharge paperwork was complete; (3) duration 3: time discharge paperwork complete until patient leaves unit; and (4) total discharge duration: time decisive test needed until patient leaves unit.
Exposure Measurements
We categorized exposures into 5 groups: (1) demographics (age, gender, race, source of patient such as outside hospital versus emergency department versus other, and payer on discharge); (2) clinical characteristics (length of stay, any psychiatric diagnosis, any substance abuse diagnosis, and severity of illness); (3) system characteristics (departmental occupancy defined as proportion of hospital beds designated for Department of Medicine patients that were occupied on the day of discharge); (4) last test characteristics (physical exam, laboratory test, procedure, and consult); and (5) discharge characteristics (discharged to home versus not discharged to home, prescriptions filled in hospital pharmacy prior to discharge, and ambulance required for transport).
Psychiatric diagnosis was defined as the presence of any of the following International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes: 290319 (any fourth or fifth digits).27 Substance abuse diagnosis was defined as the presence of any of the following ICD‐9‐CM codes: 303305 (any fourth or fifth digits). Substance abuse codes encompassed drug dependence and abuse, including alcohol dependence and abuse.
The all patient refined diagnosis related group relative weight (APRDRGwt) is a unitless number that estimates the total cost of care for inpatients, based on clinical and demographic characteristics.28 A patient with a relative APRDRGwt of 1 is predicted to have the same cost of care as the national average for inpatients. A patient with a score of 2 is predicted to be twice as costly as the average. In this study, we used APRDRGwt as a gross proxy for severity of illness.
Adjusted length of stay was measured as length of stay minus discharge duration. This adjustment was made to avoid including the exposure (length of stay) in the outcome (discharge duration). Unadjusted length of stay was used when the outcome was discharge time.
Data Sources
We created a separate 4‐item to 9‐item paper questionnaire (included in the Appendix) for each of 4 functional groups participating directly in the discharge process: nurses, physicians, social worker, and unit clerks. Questions were based on staff feedback about the sequence of steps in the discharge process, and potential reasons for delay. The surveys were piloted for several weeks to further refine the wording of questions, and to ensure that the length and location of the surveys minimized workflow interruptions. The questionnaires captured information about the timing of routine events not recorded in existing databases.
Physicians were asked to identify the last test/procedure/consult needed prior to the patient being medically ready for discharge. They were asked when the test results first could have cleared the patient for discharge (time decisive test needed), and when they actually received the test results (time test results back). Nursing and social work surveys provided information on whether or not prescriptions were filled prior to discharge, and the type of transportation used on discharge. Unit clerks documented when the patient left the unit.
Response rates were: nurses (97%), physicians (97%), social worker (99%), and unit clerks (94%). All 4 surveys were completed for 88% of the 209 included patients (prior to 8 exclusions for missing data or extreme outlier observations). Group response rates were tallied at the end of each month and posted on the unit. We did not track how soon after discharge the surveys were completed. However, we reviewed survey responses frequently (often daily, at most every 4 days) and if surveys were incomplete we personally approached staff members to complete the survey.
We supplemented and cross‐checked data from the questionnaire with information from existing hospital databases. These databases were: (1) the patient's medical record for time patient arrived on the floor, and completion time for consults/procedures; (2) the Electronic Bedboard (EBB) for time patient left the unit (as recorded by unit clerk); (3) the Patient Order Entry System for time discharge papers were completed by the physician, and ordering time for select tests; (4) the Electronic Patient Record for demographic information and completion time for select tests; and (5) Datamart, the hospital's administrative/billing database, for information such as length of stay, diagnosis, patient demographics, and insurance status.
Cross‐checking of data and calculation of durations 1, 2, and 3 identified areas of disagreement that were addressed in the following way. Discharge time was provided by 3 sources: social worker and nurses as an ad hoc addition to each of their surveys, unit clerks as a mandatory question on their survey, and unit clerks as entered in the EBB. We used EBB data for discharge time, as this was the most complete and accurate single source of data. However, survey results and knowledge about the sequential process for discharge, suggested that in 20 cases EBB data did not provide the most accurate time. In these cases, discharge time was provided by the unit clerk survey (16 cases), the social work survey (3 cases), and the nursing survey (1 case).
In 28 cases (14%), discharge paperwork was completed before decisive test results were back. And in 8 cases (4%) test results were received earlier than needed. As these were a minority of cases, these negative durations were converted to zero for analysis.
Statistical Analysis
The unit of analysis was the unique hospital discharge. For patients who were discharged from the Hospitalist Unit more than once during the 4‐month study period, each discharge was treated as a separate unit of analysis.
We defined patients discharged before the median discharge time as early discharges, and all others as late discharges. We then categorized patients with discharge durations less than 24 hours as short discharges, and all others as long discharges.
We described the characteristics of 2 groups of patients: early and short discharges versus all others. We used the chi square statistic and Fisher's exact test (when frequency 5 in 1 or both groups) to test the null hypothesis that there was no association between the 2 groups and select patient characteristics. When comparing medians, we used the nonparametric equality of medians test.
For each step in the discharge process, we identified a median time of occurrence. For the first point in the processtime decisive test neededwe also used 1‐way analysis of variance and the F‐test to assess whether or not timing varied significantly by physician.
Because our primary goal was to quantify in hours the association between various factors and discharge time or duration, we used bivariate linear regression models to identify factors associated with time of discharge (primary analysis) and total duration of the discharge process (secondary analysis). We then used multivariate linear regression to identify factors associated with both outcomes. We used forward and backward selection methods to choose the final models for the multivariate analyses, after forcing in the variables for race, sex, and age. Both methods of selection produced identical results. We assessed for colinearity using variance inflation factors.29
Sensitivity Analyses
For both discharge time and discharge duration, we performed regression diagnostics including leverage, Studentized residuals, and influence. Excluding outliers for influence slightly altered the results of our multivariate analyses. However, all variables that were significant at the P < 0.05 level remained significant in the models without outliers.
We chose to include outliers for influence in our final data set after verifying the data as accurate. For discharge time, the number of outliers (3; 1.5%) for influence was in the range expected for a normally distributed data set.
We also tested for normality of the 2 outcome variables. Discharge time was normally distributed, but discharge duration was not. Because of this, we used 2 additional methods to assess the robustness of our results for discharge duration.
First, we log‐transformed the outcome and repeated the analysis. Variables significant in the non‐log‐transformed model remained significant after log‐transformation. Second, we applied bootstrapping30 with 1,000 repetitions for the bivariate and multivariate analyses. The 95% confidence intervals (CIs) (using the bias‐corrected confidence intervals) were modestly altered (some narrowed, some widened), but our conclusions remained the same except for a single variable with borderline significance (payer on discharge) in bivariate analysis. The final reported confidence intervals for discharge duration are based on our analysis without bootstrapping.
Results
Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.
The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.
| Early and Short Discharges (n = 75)* | All Other Discharges (n = 126) | P Value | |
|---|---|---|---|
| |||
| Demographics | |||
| Age (years) | |||
| Median | 55 | 55 | 0.73 |
| Range | (19, 94) | (20, 90) | |
| Gender (%) | |||
| Female | 61.3 | 61.1 | 0.98 |
| Race (%) | 0.08 | ||
| African American | 76.0 | 60.3 | |
| Caucasian | 21.3 | 34.9 | |
| Other | 2.7 | 4.8 | |
| Payor on discharge (%) | 0.29 | ||
| Medicaid | 32.0 | 20.6 | |
| Medicare | 32.0 | 38.9 | |
| Self‐pay | 12.0 | 16.7 | |
| Other | 24.0 | 23.8 | |
| Clinical characteristics | |||
| Adjusted length of stay (days) | |||
| Median | 3 | 3 | 0.19 |
| Range | (<1, 20) | (<1, 138) | |
| Substance abuse (%) | 41.3 | 37.3 | 0.57 |
| Psychiatric diagnosis (%) | 20.0 | 25.4 | 0.38 |
| Last test characteristics | |||
| Test type (%) | <0.001 | ||
| Exam | 42.7 | 26.2 | |
| Laboratory test | 38.7 | 13.5 | |
| Procedure | 10.7 | 35.7 | |
| Consult | 8.0 | 24.6 | |
| Discharge characteristics | |||
| Discharged to home (%) | 93.3 | 71.4 | <0.001 |
| Prescriptions filled prior to discharge (%) | 10.7 | 19.8 | 0.09 |
| Ambulance required for transport (%) | 8.0 | 21.4 | 0.01 |
Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.
The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).
For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).
Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).
Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.
Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.
The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.
| Adjusted Coefficient, Discharge Time as Outcome in Hours (95% CI)* | P Value | Adjusted Coefficient, Discharge Duration as Outcome in Hours (95% CI) | P Value | |
|---|---|---|---|---|
| ||||
| Demographics | ||||
| Age in quartiles (years) | ||||
| 7294 | 0.5 (0.5, 1.6) | 0.33 | 0.6 (8.3, 7.2) | 0.88 |
| 5671 | 0.5 (0.6, 1.6) | 0.41 | 1.3 (9.2, 6.6) | 0.75 |
| 4455 | 0.2 (0.9, 1.3) | 0.74 | 1.2 (9.0, 6.5) | 0.76 |
| Male gender | 0.0 (0.8, 0.8) | 0.97 | 1.2 (6.8, 4.5) | 0.69 |
| African American race | 0.1 (0.7, 0.9) | 0.80 | 0.3 (5.6, 6.1) | 0.93 |
| Last test characteristics | ||||
| Test type | ||||
| Consult | 1.2 (0.1, 2.4) | 0.04 | 14.8 (6.5, 23.1) | 0.001 |
| Procedure | 1.1 (0.1, 2.1) | 0.03 | 13.4 (6.0, 20.7) | <0.001 |
| Laboratory test | 0.8 (1.8, 0.3) | 0.14 | 0.9 (8.4, 6.6) | 0.82 |
| Exam (reference) | ||||
| Discharge needs | ||||
| Prescriptions filled prior to discharge | 1.4 (0.3, 2.4) | 0.02 | ||
| Not discharged to home | 28.9 (21.9, 35.9) | <0.001 | ||
| Ambulance required for transport | 1.5 (0.4, 2.5) | 0.007 | ||
We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.
Conclusions
We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.
In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.
The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.
Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538
Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.
We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.
Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.
Acknowledgements
The authors thank the patients and staff of the Hospitalist Unit for their participation in this study. In addition, they sincerely thank Paul X. Allen, Richard O. Davis, Ronald R. Peterson, and Shuai Shao. They also thank the anonymous reviewers for their thoughtful comments.
In the past 2 decades, emergency department (ED) overcrowding has become an issue large enough to receive coverage in the popular press, and to spawn research around its causes and effects.116 At the same time, nurses and physicians on the inpatient wards have been urged to shorten the length of stay for patients as health system leaders face an aging population but limited capital to build new beds or hire additional clinical staff. Capacity managementencompassing the flow of patients from ED triage to inpatient dischargehas become a shared concern of clinicians and hospital administrators alike.
How to achieve the goals of diagnosing and healing while ushering patients ever more quickly through the modern hospital is not yet entirely clear. Past research and work by business groups suggests that demand for inpatient beds starts early in the day, but discharges typically occur in the late afternoon.17 This creates a potential bottleneck in patient flow. Many hospitals have implemented measures to improve patient throughput.1821 However, formal research has focused on factors leading to an additional inpatient day.2226 We have found no peer‐reviewed publications that address the problem of same‐day delays by describing hour of day for each step in the discharge process and variables associated with late‐day discharges. To fill this gap, we conducted a prospective cohort study of 209 consecutive discharges from a general medical ward to: (1) describe the natural history of hospital discharge, (2) measure time of day and duration for each step, and (3) identify factors associated with discharges that occur later in the day. We hypothesized that time and duration of discharge would be associated with 5 factors: patient demographics and clinical characteristics, departmental occupancy, type of inpatient testing done immediately prior to discharge, and discharge characteristics such as discharge to a location other than home.
Patients and Methods
Setting
The setting was the Hospitalist Unit of a single teaching hospital in Baltimore (The Johns Hopkins Hospital) from January 1, 2005 to April 30, 2005. Patients entered the cohort upon initiation of the discharge process by the hospitalist team on the Hospitalist Unit, and were followed until they were discharged alive from the hospital.
There were no published data on which to base firm a priori sample size calculations. Based on pilot data, we estimated that a sample size of about 170 would yield precise estimates for means and standard deviations, giving us 80% to 90% power to determine differences in time intervals across categories, with alpha set to 0.05. We estimated that we would need 4 months of data collection to achieve this sample size.
During the period of study, the 16‐bed unit was staffed with in‐house hospitalist attending physicians without house‐staff, from 7 AM (weekdays) or 8 AM (weekends) to 10 PM (Monday to Thursday) or 8 PM (Friday to Sunday). The hospitalist unit had 24‐hour physician coverage, but attending physicians provided overnight coverage from home (backed up by in‐house residents for patient care emergencies). Handoffs of patient care from one attending physician to another typically occurred on Friday afternoon or Monday morning. The unit had 1 dedicated social worker and a nurse clinician who provided part‐time assistance with discharge planning.
Outcome Measurements
We defined the start of the discharge process as the time the patient's last medically necessary test was needed by his or her attending physician. Specifically, physicians were asked when the results of this test first would have been useful in clearing the patient for discharge. In the remainder of this work, we will refer to the start of the discharge process as time decisive test needed.
The end of the discharge process was called the discharge time, and defined as the time the unit clerk saw the patient leave the unit. We defined early discharges as those occurring before the median hour of discharge (3:00 PM), and late discharges as those occurring at or after this hour.
A focus group composed of nurses, physicians, social worker, unit clerks, and support associates (group responsible for cleaning patient rooms) volunteered to map out the discharge process. Based on these discussions, durations in the discharge process were defined as follows: (1) duration 1: time decisive test needed, until time the attending physician was aware of test results; (2) duration 2: time the physician was aware of test results until discharge paperwork was complete; (3) duration 3: time discharge paperwork complete until patient leaves unit; and (4) total discharge duration: time decisive test needed until patient leaves unit.
Exposure Measurements
We categorized exposures into 5 groups: (1) demographics (age, gender, race, source of patient such as outside hospital versus emergency department versus other, and payer on discharge); (2) clinical characteristics (length of stay, any psychiatric diagnosis, any substance abuse diagnosis, and severity of illness); (3) system characteristics (departmental occupancy defined as proportion of hospital beds designated for Department of Medicine patients that were occupied on the day of discharge); (4) last test characteristics (physical exam, laboratory test, procedure, and consult); and (5) discharge characteristics (discharged to home versus not discharged to home, prescriptions filled in hospital pharmacy prior to discharge, and ambulance required for transport).
Psychiatric diagnosis was defined as the presence of any of the following International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes: 290319 (any fourth or fifth digits).27 Substance abuse diagnosis was defined as the presence of any of the following ICD‐9‐CM codes: 303305 (any fourth or fifth digits). Substance abuse codes encompassed drug dependence and abuse, including alcohol dependence and abuse.
The all patient refined diagnosis related group relative weight (APRDRGwt) is a unitless number that estimates the total cost of care for inpatients, based on clinical and demographic characteristics.28 A patient with a relative APRDRGwt of 1 is predicted to have the same cost of care as the national average for inpatients. A patient with a score of 2 is predicted to be twice as costly as the average. In this study, we used APRDRGwt as a gross proxy for severity of illness.
Adjusted length of stay was measured as length of stay minus discharge duration. This adjustment was made to avoid including the exposure (length of stay) in the outcome (discharge duration). Unadjusted length of stay was used when the outcome was discharge time.
Data Sources
We created a separate 4‐item to 9‐item paper questionnaire (included in the Appendix) for each of 4 functional groups participating directly in the discharge process: nurses, physicians, social worker, and unit clerks. Questions were based on staff feedback about the sequence of steps in the discharge process, and potential reasons for delay. The surveys were piloted for several weeks to further refine the wording of questions, and to ensure that the length and location of the surveys minimized workflow interruptions. The questionnaires captured information about the timing of routine events not recorded in existing databases.
Physicians were asked to identify the last test/procedure/consult needed prior to the patient being medically ready for discharge. They were asked when the test results first could have cleared the patient for discharge (time decisive test needed), and when they actually received the test results (time test results back). Nursing and social work surveys provided information on whether or not prescriptions were filled prior to discharge, and the type of transportation used on discharge. Unit clerks documented when the patient left the unit.
Response rates were: nurses (97%), physicians (97%), social worker (99%), and unit clerks (94%). All 4 surveys were completed for 88% of the 209 included patients (prior to 8 exclusions for missing data or extreme outlier observations). Group response rates were tallied at the end of each month and posted on the unit. We did not track how soon after discharge the surveys were completed. However, we reviewed survey responses frequently (often daily, at most every 4 days) and if surveys were incomplete we personally approached staff members to complete the survey.
We supplemented and cross‐checked data from the questionnaire with information from existing hospital databases. These databases were: (1) the patient's medical record for time patient arrived on the floor, and completion time for consults/procedures; (2) the Electronic Bedboard (EBB) for time patient left the unit (as recorded by unit clerk); (3) the Patient Order Entry System for time discharge papers were completed by the physician, and ordering time for select tests; (4) the Electronic Patient Record for demographic information and completion time for select tests; and (5) Datamart, the hospital's administrative/billing database, for information such as length of stay, diagnosis, patient demographics, and insurance status.
Cross‐checking of data and calculation of durations 1, 2, and 3 identified areas of disagreement that were addressed in the following way. Discharge time was provided by 3 sources: social worker and nurses as an ad hoc addition to each of their surveys, unit clerks as a mandatory question on their survey, and unit clerks as entered in the EBB. We used EBB data for discharge time, as this was the most complete and accurate single source of data. However, survey results and knowledge about the sequential process for discharge, suggested that in 20 cases EBB data did not provide the most accurate time. In these cases, discharge time was provided by the unit clerk survey (16 cases), the social work survey (3 cases), and the nursing survey (1 case).
In 28 cases (14%), discharge paperwork was completed before decisive test results were back. And in 8 cases (4%) test results were received earlier than needed. As these were a minority of cases, these negative durations were converted to zero for analysis.
Statistical Analysis
The unit of analysis was the unique hospital discharge. For patients who were discharged from the Hospitalist Unit more than once during the 4‐month study period, each discharge was treated as a separate unit of analysis.
We defined patients discharged before the median discharge time as early discharges, and all others as late discharges. We then categorized patients with discharge durations less than 24 hours as short discharges, and all others as long discharges.
We described the characteristics of 2 groups of patients: early and short discharges versus all others. We used the chi square statistic and Fisher's exact test (when frequency 5 in 1 or both groups) to test the null hypothesis that there was no association between the 2 groups and select patient characteristics. When comparing medians, we used the nonparametric equality of medians test.
For each step in the discharge process, we identified a median time of occurrence. For the first point in the processtime decisive test neededwe also used 1‐way analysis of variance and the F‐test to assess whether or not timing varied significantly by physician.
Because our primary goal was to quantify in hours the association between various factors and discharge time or duration, we used bivariate linear regression models to identify factors associated with time of discharge (primary analysis) and total duration of the discharge process (secondary analysis). We then used multivariate linear regression to identify factors associated with both outcomes. We used forward and backward selection methods to choose the final models for the multivariate analyses, after forcing in the variables for race, sex, and age. Both methods of selection produced identical results. We assessed for colinearity using variance inflation factors.29
Sensitivity Analyses
For both discharge time and discharge duration, we performed regression diagnostics including leverage, Studentized residuals, and influence. Excluding outliers for influence slightly altered the results of our multivariate analyses. However, all variables that were significant at the P < 0.05 level remained significant in the models without outliers.
We chose to include outliers for influence in our final data set after verifying the data as accurate. For discharge time, the number of outliers (3; 1.5%) for influence was in the range expected for a normally distributed data set.
We also tested for normality of the 2 outcome variables. Discharge time was normally distributed, but discharge duration was not. Because of this, we used 2 additional methods to assess the robustness of our results for discharge duration.
First, we log‐transformed the outcome and repeated the analysis. Variables significant in the non‐log‐transformed model remained significant after log‐transformation. Second, we applied bootstrapping30 with 1,000 repetitions for the bivariate and multivariate analyses. The 95% confidence intervals (CIs) (using the bias‐corrected confidence intervals) were modestly altered (some narrowed, some widened), but our conclusions remained the same except for a single variable with borderline significance (payer on discharge) in bivariate analysis. The final reported confidence intervals for discharge duration are based on our analysis without bootstrapping.
Results
Data were collected on 216 patients. Seven patients were excluded from the study, because they were discharged against medical advice. Since these patients left before their decisive test was completed, there was no way to assess duration of the discharge process. Of the remaining 209 patients, 6 patients lacked necessary data to complete analysis (5 without survey data; 1 without administrative data). Two additional patients were eliminated from the final analysis because they bypassed the normal discharge process and were extreme outliers in either discharge time (1 discharged at 1 AM), or discharge duration (1 with discharge duration of 400+ hours). A total of 201 patients were included in the final analyses.
The hospitalist program primarily serves an indigent, local adult population with general medical problems, and this is reflected in the patient characteristics (Table 1). We compared the characteristics of patients discharged early and quickly (discharged prior to median hour of 3:00 PM, and discharge process lasting less than 24 hours) to all other discharges, to identify factors associated with later and longer discharges.
| Early and Short Discharges (n = 75)* | All Other Discharges (n = 126) | P Value | |
|---|---|---|---|
| |||
| Demographics | |||
| Age (years) | |||
| Median | 55 | 55 | 0.73 |
| Range | (19, 94) | (20, 90) | |
| Gender (%) | |||
| Female | 61.3 | 61.1 | 0.98 |
| Race (%) | 0.08 | ||
| African American | 76.0 | 60.3 | |
| Caucasian | 21.3 | 34.9 | |
| Other | 2.7 | 4.8 | |
| Payor on discharge (%) | 0.29 | ||
| Medicaid | 32.0 | 20.6 | |
| Medicare | 32.0 | 38.9 | |
| Self‐pay | 12.0 | 16.7 | |
| Other | 24.0 | 23.8 | |
| Clinical characteristics | |||
| Adjusted length of stay (days) | |||
| Median | 3 | 3 | 0.19 |
| Range | (<1, 20) | (<1, 138) | |
| Substance abuse (%) | 41.3 | 37.3 | 0.57 |
| Psychiatric diagnosis (%) | 20.0 | 25.4 | 0.38 |
| Last test characteristics | |||
| Test type (%) | <0.001 | ||
| Exam | 42.7 | 26.2 | |
| Laboratory test | 38.7 | 13.5 | |
| Procedure | 10.7 | 35.7 | |
| Consult | 8.0 | 24.6 | |
| Discharge characteristics | |||
| Discharged to home (%) | 93.3 | 71.4 | <0.001 |
| Prescriptions filled prior to discharge (%) | 10.7 | 19.8 | 0.09 |
| Ambulance required for transport (%) | 8.0 | 21.4 | 0.01 |
Overall, 81% of patients were admitted from the ED, and 40% of all patients were insured by Medicaid or were self‐pay at time of discharge. Median expected charges were similar to the national average, as demonstrated by the median APRDRGwt of 1.0. Patients stayed an average of 6 days (median = 3 days). Patients with the longest adjusted lengths of stay (>20 days) were never early and short discharges. The most common discharge diagnoses were: congestive heart failure, chest pain or myocardial infarction, pneumonia, asthma/chronic obstructive pulmonary disease, and sickle‐cell disease. Thirty‐nine percent of all patients carried the diagnosis of alcohol or drug dependence or abuse, although for most this was not their discharge diagnosis. None of these demographic or clinical factors were associated with a late or long discharge.
The types of tests patients required on discharge were categorized into 4 groups: consults (18.4%), laboratory tests (22.9%), procedures (26.4%), and physical exam (32.3%). Distribution differed significantly between early and short discharges, and all other discharges (P < 0.001). Procedures and consults were less frequent among early and short discharges (procedures: 10.7% versus 35.7%; consults: 8.0% versus 24.6%).
For all patients, there was fragmentation within the consult and procedure categories. Within the consult group, there were 12 different types of consults ordered, with the dominant category (35.1%) being other. The next highest volume consult was physical/occupational therapy (27.0%). Within the procedure group, there were 11 different types of procedures, with the most common being stress echocardiograms (28.3%). Non‐MRI radiology procedures made up the next largest category (20.8%) and the third was other (18.9%).
Many patients had immediate postdischarge needs, as demonstrated by the 20% of patients not discharged home. The majority (66%) of those who were discharged to a facility required an ambulance. Early and short discharges were less likely to use an ambulance to leave the hospital (8.0% versus 21.4%; P = 0.01), and more likely to be discharged directly to home (99.3% versus 71.4%; P < 0.001).
Based on process mapping, we defined a 4‐step sequential discharge process for all patients (Figure 1). The first step was: decisive test needed by physician to discharge patient. Subgroup analysis demonstrated no significant difference in the timing of this step by individual physician (P = 0.44). The remaining 3 steps were as follows: physician aware of test results, discharge paperwork complete by physician, and patient leaves the unit. Each of the 4 steps showed wide variability in hour of occurrence.
Total discharge duration showed even more variability than the time of day when steps were likely to occur (Figure 2). Median duration of the discharge process was 7.6 hours (25th% to 75th%: 4 to 22 hours). Median duration from decisive test needed until resulted (duration 1) was 2 hours (25th% to 75th%: 0 to 8 hours); between test resulted until discharge paperwork complete (duration 2) was 1.4 hours (25th% to 75th%: 0.3 to 4.2 hours); and between discharge paperwork complete and patient leaving the unit (duration 3) was 2.0 hours (25th% to 75th%: 1.1 to 3.1 hours). All durations were skewed to the right, with durations 1 and 2 each taking at least 24 hours to occur in 10% of patients.
The final multivariate model for time of discharge contained 6 covariates: age, sex, race, test type, prescriptions filled prior to discharge, and need for an ambulance on discharge (Table 2). Special discharge needs continued to be associated with later discharges. Those patients who required an ambulance for transport had mean discharge times that were later by 1.5 hours (95% CI, 0.42.5). If staff obtained discharge medications for patients, these patients left 1.4 hours later than those patients who filled their prescriptions on their own (95% CI, 0.32.4). Patients requiring a consult or procedure also had significantly later discharges (1.2 hours for consults, 95% CI, 0.12.4; 1.1 hours for procedures, 95% CI, 0.12.1) than those needing a bedside exam. Age, sex, and race remained insignificant at the P 0.05 level in the final multivariate model. Length of stay was significantly associated with discharge time in crude analysis, but this variable dropped out of the final multivariate model.
| Adjusted Coefficient, Discharge Time as Outcome in Hours (95% CI)* | P Value | Adjusted Coefficient, Discharge Duration as Outcome in Hours (95% CI) | P Value | |
|---|---|---|---|---|
| ||||
| Demographics | ||||
| Age in quartiles (years) | ||||
| 7294 | 0.5 (0.5, 1.6) | 0.33 | 0.6 (8.3, 7.2) | 0.88 |
| 5671 | 0.5 (0.6, 1.6) | 0.41 | 1.3 (9.2, 6.6) | 0.75 |
| 4455 | 0.2 (0.9, 1.3) | 0.74 | 1.2 (9.0, 6.5) | 0.76 |
| Male gender | 0.0 (0.8, 0.8) | 0.97 | 1.2 (6.8, 4.5) | 0.69 |
| African American race | 0.1 (0.7, 0.9) | 0.80 | 0.3 (5.6, 6.1) | 0.93 |
| Last test characteristics | ||||
| Test type | ||||
| Consult | 1.2 (0.1, 2.4) | 0.04 | 14.8 (6.5, 23.1) | 0.001 |
| Procedure | 1.1 (0.1, 2.1) | 0.03 | 13.4 (6.0, 20.7) | <0.001 |
| Laboratory test | 0.8 (1.8, 0.3) | 0.14 | 0.9 (8.4, 6.6) | 0.82 |
| Exam (reference) | ||||
| Discharge needs | ||||
| Prescriptions filled prior to discharge | 1.4 (0.3, 2.4) | 0.02 | ||
| Not discharged to home | 28.9 (21.9, 35.9) | <0.001 | ||
| Ambulance required for transport | 1.5 (0.4, 2.5) | 0.007 | ||
We used duration of discharge as a secondary outcome measure. The final multivariate model for discharge duration included: age, sex, race, test type, and discharge to a facility (Table 2). Those who went to a facility on average left 28.9 hours (95% CI, 21.935.9) later than those who went home. Test type continued to show a significant association with discharge duration, although the estimates were slightly lower in the adjusted model. Need for a consult was associated with a discharge that was on average 14.8 hours (95% CI, 6.523.1) longer than discharges contingent on a physical exam. Similarly, those patients who had procedures had discharges that were on average 13.4 hours (95% CI, 6.020.7) longer than those whose last test was an exam. Several factors that were significantly associated with discharge duration in unadjusted analyses dropped out of the final multivariate model. These included: need for an ambulance, length of stay, insurance status, and medical complexity as estimated by APRDRGwt.
Conclusions
We found that discharge time and duration had wide variability and that certain factors were associated with only one outcome variabledischarge time or duration. Two factorsneed for an ambulance and filling of prescriptions prior to dischargewere associated with later hour of discharge. Discharge to a location other than home was associated with prolonged discharge duration. Test characteristicsin particular need for a procedure and consultwere significantly associated with both longer and later discharges.
In bivariate analysis, several factors were not associated with discharge time or duration. These were: African‐American race, sex, age, Department of Medicine occupancy on day of discharge, source of admission, psychiatric comorbidity, and substance abuse comorbidity. We had expected higher occupancy to delay discharge as demand exceeded capacity for tests, consults, etc. Our findings suggest that even though our study was conducted during the winter months when hospital occupancy is typically at its peak, supply of staff was still adequate enough to meet high demand. We had also expected that psychiatric and substance abuse comorbidities would prolong discharge as prior studies have found some of these diagnoses to be associated with longer lengths of stay.3134 However, our results do not support such an association, and may reflect our decision to group all psychiatric diagnoses together due to limited sample size.
The main strength of our study is the use of 2 outcome variablestime and durationto define delayed discharges. Our findings demonstrate that few factors are associated with both later and longer discharges. In an era when avoiding emergency room walkouts through early morning hospital discharges can be as important as managing overall length of stay, identifying factors associated with both duration and timing of discharges addresses actual challenges faced by hospitals with limited resources. Prior studies have rarely addressed both outcomes. An additional strength of our study is our use of an interdisciplinary survey. The discharge process is a key component of the inpatient stay, but it is also one for which no group is entirely responsible. Through the development and administration of an interdisciplinary survey, our study adds detail to existing descriptions of this fragmented process, and identifies potential areas for improvement.
Several limitations of our study deserve comment. First, we examined patients discharged from a hospitalist unit without house‐staff at an urban tertiary care hospital. Our findings may need additional interpretation prior to their application in dissimilar settings such as: (1) resident‐covered units in which workflow is shaped by teaching rounds, and (2) nonacademic hospitals in which incentives to provide rapid consults and procedures may be different. Second, we relied on self‐reporting for certain variables such as time decisive test needed. This may be subject to recall bias, as we did not have staff to independently verify recalled times. However, since the discharge process is generally a linear one, we were able to verify the general scope of recalled times with times date‐stamped by the computer during the discharge process (eg, checking that time decisive test needed did not occur after the discharge worksheet had been finalized in the electronic order‐entry system). Third, our sample size was not large enough to control for disease‐specific quality measures. Of note, prior studies have not identified a consistently positive or negative relationship between quality of care and efficiency.3538
Past work has used administrative and survey data to analyze the effect of discharge planning interventions on financial or quality outcomes. Outcomes have included readmissions, mortality, patient satisfaction, length of stay, and inappropriate bed days.22, 3845 However, as capacity management has become a more pressing issue for hospitals, greater focus is being placed on the mismatch between supply and demand of patients at each hour of the day. The relevant unit of measure for efficient discharges has become hour of day, in addition to total length of stay. Some hospital improvement projects have already addressed this shift in thinking.20, 21 Our study adds to this work by formally describing the precise timing and duration of steps in the discharge process, and identifying factors associated with both time and duration.
We believe the results of our study have several implications for hospital administrators and patients interested in more timely care. First, the methods used provide a tangible framework for addressing problems that cross disciplines (eg, nursing, physician, social work) and departments (eg, medicine and radiology), and have a multitude of potential causes and confounders. The survey results offer guidance on where to focus resources, provide a shared baseline metric for improvement, and suggest the cross‐functional team that should be involved in change efforts. Such an approach may be useful for addressing common system‐based challenges in inpatient quality and safety.
Second, with specific regard to discharge planning, our study supports the notion that modifiable factors are associated with discharge time and duration. However, we also describe a fragmented discharge process, with no single bottleneck or department responsible for the majority of late and long discharges. Although procedures and consults were both associated with longer and later discharges, only 26% of patients required a procedure prior to discharge, and 18% required a consult. Moreover, among procedures, different people and events are needed to carry out the 2 most popular procedures: stress echocardiograms, and non‐MRI radiology procedures. Hospital leadership at the highest levels will be required to improve efficiency based on local usage patterns, and to increase coordination among the multiple interdepartmental processes that make up the more general categories of procedure and consult.
Acknowledgements
The authors thank the patients and staff of the Hospitalist Unit for their participation in this study. In addition, they sincerely thank Paul X. Allen, Richard O. Davis, Ronald R. Peterson, and Shuai Shao. They also thank the anonymous reviewers for their thoughtful comments.
- ,,,,.Emergency departments and crowding in United States teaching hospitals.Ann Emerg Med.1991;20:980–986.
- ,,,,,.A conceptual model of emergency department crowding.Ann Emerg Med.2003;42:173–180.
- ,,.Frequent overcrowding in U.S. emergency departments.Acad Emerg Med.2001;8:151–155.
- ,,,,.The effect of hospital occupancy on emergency department length of stay and patient disposition.Acad Emerg Med.2003;10:127–133.
- .Calculus, chaos, and other models of emergency department crowding.Ann Emerg Med.2003;42:181–184.
- ,.Increased health care costs associated with ED overcrowding.Am J Emerg Med.1994;12:265–266.
- ,,, et al.Decreased health care quality associated with emergency department overcrowding.Eur J Emerg Med.1999;6:105–107.
- ,,.Can good bed management solve the overcrowding in accident and emergency departments?Emerg Med J.2003;20:149–155.
- ,.Emergency department overcrowding in the United States: an emerging threat to patient safety and public health.Emerg Med J.2003;20:402–405.
- General Accounting Office. Hospital emergency departments: crowded conditions vary among hospitals and communities. 2003:GAO‐03–460. Available at: http://www.gao.gov/new.items/d03460.pdf. Accessed October.
- ,.Do you want to die?Time.1990;135:58–60+
- .The ER crisis.Ladies Home J.2001;118:70–79.
- .Placing emergency department crowding on the decision agenda.Nurs Econ.2005;23:14–24.
- .Crisis seen in nation's ER care. Washington Post. June 15,2006;A01.
- Committee on the Future of Emergency Care in the United States Health System.Hospital‐based Emergency Care: at the Breaking Point.Washington DC:National Academies Press;2006.
- ,,, et al.Waits to see an emergency department physician: U.S. trends and predictors, 1997–2004.Health Aff.2008;27:w84–w95.
- Advisory Board.Maximizing Hospital Capacity: Expediting Throughput in an Era of Shortage. Health Care Advisory Board research study.Washington DC:Advisory Board; September 12,2002.
- ,,,.Does U.S. hospital capacity need to be expanded?Health Aff.2003;22:40–54.
- Institute for Healthcare Improvement.Optimizing Patient Flow: Moving Patients Smoothly Through Acute Care Settings.Cambridge, MA:Institute for Healthcare Improvement;2003.
- ,. Leveraging six sigma to improve hospital bed availability. Available at: http://healthcare.isixsigma.com/library/content/c030708a.asp. Accessed October2008.
- University Health System Consortium. Managing patient flow implementation: 2005 benchmarking project field brief. Available at: http://www.musc.edu/medcenter/UHC‐CCP/PFMgmtFldBk.pdf.
- ,,,.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;1:CD000313.
- ,,, et al.Predictors of inappropriate hospital days in a department of internal medicine.Int J Epidemiol.1998;27:513–519.
- ,,.Utilisation of hospital beds by the elderly—a cohort study of admissions to a teaching hospital.Ir Med J.1995;88:124–126.
- ,,,,,.Demographic and clinical characteristics as predictors of length of hospitalization and readmission.J Clin Psychol.1977;33:1093–1099.
- ,,,.The epidemiology of delays in a teaching hospital: the development and use of a tool that detects unnecessary hospital days.Med Care.1989;27:112–129.
- American Medical Association.AMA Physician ICD‐9‐CM 2004, Vols 1 and 2.Eden Prairie, MN:Ingenix;2003.
- 3M Health Information Systems and the Maryland Health Care Payment System: frequently asked questions.2004. Available at: http://multimedia.mmm.com/mws/mediawebserver.dyn?6666660Zjcf6lVs6EVs66S7wwCOrrrrQ‐.
- .Statistics with STATA.Belmont, CA:Duxbury Press;2004.
- ,.Statistical data analysis in the computer age.Science.1991;253:390–395.
- ,,,,.Psychological comorbidity and length of stay in the general hospital.Am J Psychiatry.1991;148(3):324–329.
- ,,,,,.Psychiatric comorbidity and greater hospitalization risk, longer length of stay, and higher hospitalization costs in older adults with heart failure.J Am Geriatr Soc.2007;55:1585–1591.
- ,,,.Does psychiatric comorbidity increase the length of stay in general hospitals?Gen Hosp Psychiatry.2001;23:8–14.
- ,,.The impact of psychiatric comorbidity on length of stay of medical inpatients.Gen Hosp Psychiatry.2003;25:14–19.
- ,,,,,.The implications of regional variations in Medicare spending. Part 1: The content, quality, and accessibility of care.Ann Intern Med.2003;138:273–287.
- ,,,,,.The implications of regional variations in Medicare spending. Part 2: Health outcomes and satisfaction with care.Ann Intern Med.2003;138:288–298.
- ,,, et al.Is early too early? Effect of shorter stays after bypass surgery.Ann Thorac Surg.2007;83:100–107.
- ,.Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization.JAMA.2003;290:1868–1874.
- ,,, et al.Comprehensive discharge planning and home follow‐up of hospitalized elders, a randomized clinical trial.JAMA.1999;281:613–620.
- ,,,,,.Comprehensive discharge planning for the hospitalized elderly, a randomized clinical trial.Ann Intern Med.1994;120:999–1006.
- ,.Evaluating hospital discharge planning: a randomized clinical trial.Med Care.1993;31:358–370.
- ,,,,.Does a dedicated discharge coordinator improve the quality of hospital discharge?Qual Health Care.1996;5:89–96.
- ,,, et al.The effect of a hospitalist service with nurse discharge planner on patient care in an academic teaching hospital.Am J Med.2001;111:627–632.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291:1358–1367.
- ,,.Does increased access to primary care reduce hospital readmissions?N Engl J Med.1996;334:1441–1447.
- ,,,,.Emergency departments and crowding in United States teaching hospitals.Ann Emerg Med.1991;20:980–986.
- ,,,,,.A conceptual model of emergency department crowding.Ann Emerg Med.2003;42:173–180.
- ,,.Frequent overcrowding in U.S. emergency departments.Acad Emerg Med.2001;8:151–155.
- ,,,,.The effect of hospital occupancy on emergency department length of stay and patient disposition.Acad Emerg Med.2003;10:127–133.
- .Calculus, chaos, and other models of emergency department crowding.Ann Emerg Med.2003;42:181–184.
- ,.Increased health care costs associated with ED overcrowding.Am J Emerg Med.1994;12:265–266.
- ,,, et al.Decreased health care quality associated with emergency department overcrowding.Eur J Emerg Med.1999;6:105–107.
- ,,.Can good bed management solve the overcrowding in accident and emergency departments?Emerg Med J.2003;20:149–155.
- ,.Emergency department overcrowding in the United States: an emerging threat to patient safety and public health.Emerg Med J.2003;20:402–405.
- General Accounting Office. Hospital emergency departments: crowded conditions vary among hospitals and communities. 2003:GAO‐03–460. Available at: http://www.gao.gov/new.items/d03460.pdf. Accessed October.
- ,.Do you want to die?Time.1990;135:58–60+
- .The ER crisis.Ladies Home J.2001;118:70–79.
- .Placing emergency department crowding on the decision agenda.Nurs Econ.2005;23:14–24.
- .Crisis seen in nation's ER care. Washington Post. June 15,2006;A01.
- Committee on the Future of Emergency Care in the United States Health System.Hospital‐based Emergency Care: at the Breaking Point.Washington DC:National Academies Press;2006.
- ,,, et al.Waits to see an emergency department physician: U.S. trends and predictors, 1997–2004.Health Aff.2008;27:w84–w95.
- Advisory Board.Maximizing Hospital Capacity: Expediting Throughput in an Era of Shortage. Health Care Advisory Board research study.Washington DC:Advisory Board; September 12,2002.
- ,,,.Does U.S. hospital capacity need to be expanded?Health Aff.2003;22:40–54.
- Institute for Healthcare Improvement.Optimizing Patient Flow: Moving Patients Smoothly Through Acute Care Settings.Cambridge, MA:Institute for Healthcare Improvement;2003.
- ,. Leveraging six sigma to improve hospital bed availability. Available at: http://healthcare.isixsigma.com/library/content/c030708a.asp. Accessed October2008.
- University Health System Consortium. Managing patient flow implementation: 2005 benchmarking project field brief. Available at: http://www.musc.edu/medcenter/UHC‐CCP/PFMgmtFldBk.pdf.
- ,,,.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;1:CD000313.
- ,,, et al.Predictors of inappropriate hospital days in a department of internal medicine.Int J Epidemiol.1998;27:513–519.
- ,,.Utilisation of hospital beds by the elderly—a cohort study of admissions to a teaching hospital.Ir Med J.1995;88:124–126.
- ,,,,,.Demographic and clinical characteristics as predictors of length of hospitalization and readmission.J Clin Psychol.1977;33:1093–1099.
- ,,,.The epidemiology of delays in a teaching hospital: the development and use of a tool that detects unnecessary hospital days.Med Care.1989;27:112–129.
- American Medical Association.AMA Physician ICD‐9‐CM 2004, Vols 1 and 2.Eden Prairie, MN:Ingenix;2003.
- 3M Health Information Systems and the Maryland Health Care Payment System: frequently asked questions.2004. Available at: http://multimedia.mmm.com/mws/mediawebserver.dyn?6666660Zjcf6lVs6EVs66S7wwCOrrrrQ‐.
- .Statistics with STATA.Belmont, CA:Duxbury Press;2004.
- ,.Statistical data analysis in the computer age.Science.1991;253:390–395.
- ,,,,.Psychological comorbidity and length of stay in the general hospital.Am J Psychiatry.1991;148(3):324–329.
- ,,,,,.Psychiatric comorbidity and greater hospitalization risk, longer length of stay, and higher hospitalization costs in older adults with heart failure.J Am Geriatr Soc.2007;55:1585–1591.
- ,,,.Does psychiatric comorbidity increase the length of stay in general hospitals?Gen Hosp Psychiatry.2001;23:8–14.
- ,,.The impact of psychiatric comorbidity on length of stay of medical inpatients.Gen Hosp Psychiatry.2003;25:14–19.
- ,,,,,.The implications of regional variations in Medicare spending. Part 1: The content, quality, and accessibility of care.Ann Intern Med.2003;138:273–287.
- ,,,,,.The implications of regional variations in Medicare spending. Part 2: Health outcomes and satisfaction with care.Ann Intern Med.2003;138:288–298.
- ,,, et al.Is early too early? Effect of shorter stays after bypass surgery.Ann Thorac Surg.2007;83:100–107.
- ,.Excess length of stay, charges, and mortality attributable to medical injuries during hospitalization.JAMA.2003;290:1868–1874.
- ,,, et al.Comprehensive discharge planning and home follow‐up of hospitalized elders, a randomized clinical trial.JAMA.1999;281:613–620.
- ,,,,,.Comprehensive discharge planning for the hospitalized elderly, a randomized clinical trial.Ann Intern Med.1994;120:999–1006.
- ,.Evaluating hospital discharge planning: a randomized clinical trial.Med Care.1993;31:358–370.
- ,,,,.Does a dedicated discharge coordinator improve the quality of hospital discharge?Qual Health Care.1996;5:89–96.
- ,,, et al.The effect of a hospitalist service with nurse discharge planner on patient care in an academic teaching hospital.Am J Med.2001;111:627–632.
- ,,,,,.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291:1358–1367.
- ,,.Does increased access to primary care reduce hospital readmissions?N Engl J Med.1996;334:1441–1447.
Copyright © 2009 Society of Hospital Medicine
