David Anderson1, Laura Pimentel2, Bruce Golden3, Edward Wasil4, Jon Mark Hirshon5. 1. Zicklin School of Business, Baruch College, City University of New York, New York, NY. 2. Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD. Electronic address: lpimentel@umem.org. 3. Robert H. Smith School of Business, University of Maryland, College Park, MD. 4. Kogod School of Business, American University, Washington, DC. 5. Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD.
Abstract
STUDY OBJECTIVE: The percentage of patients leaving before treatment is completed (LBTC) is an important indicator of emergency department performance. The objective of this study is to identify characteristics of hospital operations that correlate with LBTC rates. METHODS: The Emergency Department Benchmarking Alliance 2012 and 2013 cross-sectional national data sets were analyzed using multiple regression and k-means clustering. Significant operational variables affecting LBTC including annual patient volume, percentage of high-acuity patients, percentage of patients admitted to the hospital, number of beds, academic status, waiting times to see a physician, length of stay (LOS), registered nurse (RN) staffing, and physician staffing were identified. LBTC was regressed onto these variables. Because of the strong correlation between waiting times measured as door to first provider (DTFP), we regressed DTFP onto the remaining predictors. Cluster analysis was applied to the data sets to further analyze the impact of individual predictors on LBTC and DTFP. RESULTS: LOS and the time from DTFP were both strongly associated with LBTC rate (P<.001). Patient volume is not significantly associated with LBTC rate (P=.16). Cluster analysis demonstrates that physician and RN staffing ratios correlate with shorter DTFP and lower LBTC. CONCLUSION: Volume is not the main driver of LBTC. DTFP and LOS are much more strongly associated. We show that operational factors including LOS and physician and RN staffing decisions, factors under the control of hospital and physician executives, correlate with waiting time and, thus, in determining the LBTC rate.
STUDY OBJECTIVE: The percentage of patients leaving before treatment is completed (LBTC) is an important indicator of emergency department performance. The objective of this study is to identify characteristics of hospital operations that correlate with LBTC rates. METHODS: The Emergency Department Benchmarking Alliance 2012 and 2013 cross-sectional national data sets were analyzed using multiple regression and k-means clustering. Significant operational variables affecting LBTC including annual patient volume, percentage of high-acuity patients, percentage of patients admitted to the hospital, number of beds, academic status, waiting times to see a physician, length of stay (LOS), registered nurse (RN) staffing, and physician staffing were identified. LBTC was regressed onto these variables. Because of the strong correlation between waiting times measured as door to first provider (DTFP), we regressed DTFP onto the remaining predictors. Cluster analysis was applied to the data sets to further analyze the impact of individual predictors on LBTC and DTFP. RESULTS: LOS and the time from DTFP were both strongly associated with LBTC rate (P<.001). Patient volume is not significantly associated with LBTC rate (P=.16). Cluster analysis demonstrates that physician and RN staffing ratios correlate with shorter DTFP and lower LBTC. CONCLUSION: Volume is not the main driver of LBTC. DTFP and LOS are much more strongly associated. We show that operational factors including LOS and physician and RN staffing decisions, factors under the control of hospital and physician executives, correlate with waiting time and, thus, in determining the LBTC rate.
Authors: Maame Yaa A B Yiadom; Christopher W Baugh; Tyler W Barrett; Xulei Liu; Alan B Storrow; Timothy J Vogus; Vikram Tiwari; Corey M Slovis; Stephan Russ; Dandan Liu Journal: Acad Emerg Med Date: 2018-01 Impact factor: 3.451
Authors: Morgan R Bobb; Azeemuddin Ahmed; Paul Van Heukelom; Rachel Tranter; Karisa K Harland; Brady M Firth; Randy Fry; Katherine Schneider; Kathryn K Dierks; Sarah L Miller; Nicholas M Mohr Journal: Acad Emerg Med Date: 2018-01-22 Impact factor: 3.451