1. Allocating Emergency Beds Improves the Emergency Admission Flow
- Author
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A. J. Thomas Schneider, Richard J. Boucherie, Wilbert B. van den Hout, Maartje E. Zonderland, P. Luuk Besselink, Ton J. Rabelink, Paul Bilars, Job Kievit, A. Jaap Fogteloo, Center for Healthcare Operations Improvement and Research, and Stochastic Operations Research
- Subjects
decision support ,emergency department ,Strategy and Management ,acute medical unit ,0211 other engineering and technologies ,02 engineering and technology ,Management Science and Operations Research ,03 medical and health sciences ,0302 clinical medicine ,discrete-event simulation ,length of stay ,Management of Technology and Innovation ,otorhinolaryngologic diseases ,medicine ,030212 general & internal medicine ,Quality of care ,emergency admissions ,021103 operations research ,Emergency admission ,business.industry ,Systems optimalization ,inpatient wards ,Emergency department ,medicine.disease ,humanities ,Medical emergency ,operations efficiency ,hospitals ,business ,systems optimization - Abstract
The increasing number of admissions to hospital emergency departments (EDs) during the past decade has resulted in overcrowded EDs and decreased quality of care. The emergency admission flow that we discuss in this study relates to three types of hospital departments: EDs, acute medical unit (AMUs), and inpatient wards. This study has two objectives: (1) to evaluate the impact of allocating beds in inpatient wards to accommodate emergency admissions and (2) to analyze the impact of pooling the number of beds allocated for emergency admissions in inpatient wards. To analyze the impact of various allocations of emergency beds, we developed a discrete event simulation model. We evaluate the bed allocation scenarios using three performance indicators: (1) the length of stay in the AMU, (2) the fraction of patients refused admission, and (3) the utilization of allocated beds. We develop two heuristics to allocate beds to wards and show that pooling beds improves performance. The partnering hospital has embedded a decision support tool based on the simulation model into its planning and control cycle. The hospital uses it every quarter and updates it with data on a 1-year rolling horizon. This strategy has substantially reduced the number of patients who are refused emergency admission.
- Published
- 2018