Article ID: | iaor20162357 |
Volume: | 19 |
Issue: | 2 |
Start Page Number: | 192 |
End Page Number: | 205 |
Publication Date: | Jun 2016 |
Journal: | Health Care Management Science |
Authors: | Ashour Omar, Okudan Kremer Gl |
Keywords: | demand, scheduling, combinatorial optimization, simulation, markov processes, queues: applications |
The demand on emergency departments (ED) is variable and ever increasing, often leaving them overcrowded. Many hospitals are utilizing triage algorithms to rapidly sort and classify patients based on the severity of their injury or illness, however, most current triage methods are prone to over‐ or under‐triage. In this paper, the group technology (GT) concept is applied to the triage process to develop a dynamic grouping and prioritization (DGP) algorithm. This algorithm identifies most appropriate patient groups and prioritizes them according to patient‐ and system‐related information. Discrete event simulation (DES) has been implemented to investigate the impact of the DGP algorithm on the performance measures of the ED system. The impact was studied in comparison with the currently used triage algorithm, i.e., emergency severity index (ESI). The DGP algorithm outperforms the ESI algorithm by shortening patients’ average length of stay (LOS), average time to bed (TTB), time in emergency room, and lowering the percentage of tardy patients and their associated risk in the system.