Improving Hospital Discharge Time

Delays in discharging patients can impact hospital and emergency department (ED) throughput. The discharge process is complex and involves setting specific challenges that limit generalizability of solutions. The aim of this study was to assess the effectiveness of using Six Sigma methods to improve the patient discharge process. This is a quantitative pre and post-intervention study. Three hundred and eighty-six bed tertiary care hospital. A series of Six Sigma driven interventions over a 10-month period. The primary outcome was discharge time (time from discharge order to patient leaving the room). Secondary outcome measures included percent of patients whose discharge order was written before noon, percent of patients leaving the room by noon, hospital length of stay (LOS), and LOS of admitted ED patients. Discharge time decreased by 22.7% from 2.2 hours during the preintervention period to 1.7 hours post-intervention (P< 0.001). A greater proportion of patients left their room before noon in the postintervention period (P< 0.001), though there was no statistical difference in before noon discharge. Hospital LOS dropped from 3.4 to 3.1 days postintervention (P< 0.001). ED mean LOS of patients admitted to the hospital was significantly lower in the postintervention period (6.9 7.8 vs 5.9 7.7 hours; P< 0.001). Six Sigma methodology can be an effective change management tool to improve discharge time. The focus of institutions aspiring to tackle delays in the discharge process should be on adopting the core principles of Six Sigma rather than specific interventions that may be institution-specific. (Medicine 94(12):e633) Abbreviations: AUB = American University of Beirut, AUBMC = American University of Beirut Medical Center, CI = confidence interval, DMAIC = define, measure, analyze, improve and control, Hani Tamim, PhD, and Eveline A. Hitti, MD, MBA INTRODUCTION P ressures to cut cost have led many health care organizations to adopt strategies for reducing patient length of stay (LOS) and improving hospital throughput over the past 3 decades. In the United States, the introduction of the prospective payment system by Medicare in 1983, whereby hospital reimbursement moved from a per-diem basis to a flat payment related to diagnosis, was instrumental in driving down LOS, as was the emergence of managed care organizations that looked closely at hospital utilization. As a result, average LOS for inpatients in the United States has dropped to around 5.2 days, from its peak of 7.3 days in the 1980s and will likely continue to feel similar pressures with the implementation of the Affordable Care Act. Similar trends in decreased LOS of inpatients have been observed in other countries including the UK, Australia, and Sweden. Cost-effectiveness has not been the sole driver of improved hospital throughput. Concerns about quality and safety, particularly of emergency department (ED) patients impacted by lack of access to inpatient beds, have led Joint Commission to implement a leadership standard that hospitals should ‘‘manage the flow of patients throughout the hospital’’ (LD 04.03.11). Boarding inpatients in the ED has become a global problem contributing to over-crowdedness and has been linked to deleterious patient care outcomes as well as adversely impacted ED efficiency metrics. LOS of emergency patients is impacted by multiple factors including the efficiency of the discharging inpatients from the hospital as well as the timing of inpatient discharges. To improve access to beds, The Joint Commission (TJC) has stipulated that hospitals: have processes in place that support patient flow throughout the hospital; measure available supply of beds and efficiency of patient care areas; report measurements to leadership; and use data to drive improvements in patient flow processes. TJC has also recently started urging hospitals to use specific quality improvement tools to develop more reliable processes, including Lean and Six Sigma. The latter is an industrial management methodology for data-driven process improvement that is increasingly being applied in healthcare. The objective of this study was to assess the effectiveness of using Six Sigma method to improve the patient discharge process at a tertiary teaching university hospital in a developing country. METHODS Study Design and Setting The American University of Beirut Medical Center (AUBMC) is a tertiary care teaching hospital with 386 beds located in Beirut, Lebanon. It is accredited by The Joint Commission International. AUBMC provides care to around 35,000 inpatients annually. This was a quantitative pre, postintervention study of the impact of introducing a series of Six ions on hospital discharge time focusing harge process. Five months of preinter2012–December 2012) were compared www.md-journal.com | 1 with 5 months of postintervention (November 2013–March of 2014). The study was approved by the AUBMC hospital administration in accordance with the quality assurance policy and deemed exempt from human subject research by the institutional review board of AUB. Intervention The Hospital Throughput Project was conducted at AUBMC from August 2012 until October 2013 with ongoing key performance metrics monitoring, following a process improvement framework developed using Six Sigma principles. Six Sigma relies on a structured approach to uncover the root cause of a problem using the Define, Measure, Analyze, Improve and Control (DMAIC) method by: defining the problem; measuring the defect; analyzing the causes; improving the process by removing major causes; and controlling the process to ensure defects do not recur. A multidisciplinary team was formed at the outset and included a physician sponsor, chief medical resident, director of bed management, manager of billing, patient care unit manager, charge nurses, unit clerk representative, director of environmental services, assistant director of health information management, and manager of the transport services. A hospital administrator was the assigned Six Sigma expert responsible for team building and project management. Following the DMAIC approach, the team set out to define the scope of the project and decided to focus on improving the administrative processes that contributed to delays in discharging patients. Mapping the steps from when the order is written to the patient leaving the room demonstrated a fragmented process with providers functioning in silos and relying on patients to alert staff to initiate the next step. The team redesigned the process to a single-piece flow that included electronically entered time stamps for each step that reflected the status of the discharge process to all the stakeholders and sent alerts to managers when delays beyond a set threshold occurred. Target discharge time was set at 105 minutes, approximately 20% reduction from baseline historical data that was around 130 minutes. Once measurement of key process step times was ensured through the introduction of electronic time stamps, the team, through extensive discussions, completed a root cause analysis for delayed discharge and outlined barriers, waste, and proposed changes from the perspective of each stakeholder (Table 1). Interventions were sequentially tested through pilots on a few floors before full implementation across all hospital regular inpatient floors. Intensive care and intermediate care units were excluded as most patients on these units do not proceed through the routine discharge process but rather undergo transfer to a regular unit before discharge from the hospital. Measurements Two sets of data were structured for the purpose of addressing the objective of the study. The first dataset was for the hospital inpatients ‘‘hospital analyses,’’ which included 8494 patients in the preintervention phase and 8560 patients in the postintervention phase. The primary outcome of these analyses was discharge time, whereas the secondary outcomes were percent patients discharged before noon, percent orders written before noon, and hospital LOS. Hospital LOS was El-Eid et al calculated in days by subtracting the discharge date from admission date. Before noon and after noon discharges were assigned according to the time during the day the discharge was 2 | www.md-journal.com completed. Specifically, ‘‘before noon’’ was between 12 AM and 12 PM, whereas ‘‘after noon’’ was between 12 PM and 12 AM. The second dataset was for ED patients admitted to the hospital ‘‘ED analyses,’’ which included 2901 patients in the preintervention period and 3169 in the postintervention period. The primary outcome of these analyses was LOS. LOS of ED patients was calculated from ED registration time to ED discharge time. Statistical Analysis For both hospital and ED analyses, descriptive statistics were carried out, wherein categorical variables were summarized by number and percent, whereas continuous variables were summarized by mean and standard deviation. Assessing the association between the interventions (pre vs post) was carried out using the Student t test for continuous variables, whereas the Pearson Chi Square test was used for categorical ones. Multivariate analysis was carried out to identify the association between the interventions and the outcomes, while controlling for the potentially confounding effect of the different factors. More specifically, multiple linear regression analysis using a backward selection procedure, with significance level for removal from the model set at 0.1, was conducted to examine the relationship between outcomes (discharge time and LOS) and various potential predictors mainly the effect of the intervention. All determinants that are statistically and clinically significant were included into the regression analysis. Multicollinearity was assessed by carrying out correlation analysis to identify explanatory variables, which were highly correlated with each other. The Statistical Software for Social Sciences (SPSS version 21) was used to carry out these analyses. The level of statistical significance was set at the 0.05 level. Individual control chart was used to analyze trends in average daily discharge time, special cause variations (nonroutine events), and common cause variations (routine events), and assess the process for stability (statistical control). To further assess variation in the process, the following were also measured: six sigma scores (number of short-term standard deviations between the center of a process and the closest specification limit); yield (percentage of discharge times meeting team goal of 105 minutes); defects per million (number of times that discharge time exceeded the target, per million discharge opportunities). RESULTS For the hospital analyses, 38,495 patients were admitted to the hospital during the study period. After excluding admissions from units where the intervention was not implemented (3060), admissions during the intervention period from January 1, 2013 to October 31, 2013 (17860) and negative discharge times due to errors in the data entry (521), a total of 17054 admitted patients were analyzed. Table 2 presents the distribution of the different variables collected in the study for the whole sample, as well as by the intervention period (pre and postintervention). There was a significant difference in age between the two periods (45.0 years preintervention and 46.4 postintervention, P< 0.001). The monthly rate of occupancy also was found to be higher in the postintervention period (68.9%, SD1⁄4 16.0%) as compared to the preintervention period (66.6%, SD1⁄4 13.1%) (P< 0.001). There was a statistically significant reduction in Medicine Volume 94, Number 12, March 2015 hospital LOS in the postintervention (3.1 days, SD1⁄4 4.2) versus the preintervention period (3.4 days, SD1⁄4 5.2) (P< 0.001). Similarly, there was a significant drop in discharge time Copyright # 2015 Wolters Kluwer Health, Inc. All rights reserved. T A B L E 1 . B a rr ie r, W a st e , a n d C h a n g e fr o m th e P e rs p e ct iv e o f D if fe re n t S ta ke h o ld e rs O b st ac le s, W as te s, an d C h an ge s P at ie n t B il li n g O ffi ce r P at ie n t A cc es s S ta ff F lo or C le rk R N A tt en d in g P h ys ic ia n O b st ac le s C ar e p ro v id er s