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Managing responsiveness in the emergency department: Comparing dynamic priority queue with fast track
Author(s) -
Ferrand Yann B.,
Magazine Michael J.,
Rao Uday S.,
Glass Todd F.
Publication year - 2018
Publication title -
journal of operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.649
H-Index - 191
eISSN - 1873-1317
pISSN - 0272-6963
DOI - 10.1016/j.jom.2018.03.001
Subject(s) - prioritization , computer science , fast track , queue , priority queue , service (business) , operations research , priority ceiling protocol , variable (mathematics) , operations management , priority inheritance , emergency department , medicine , business , dynamic priority scheduling , process management , quality of service , computer network , mathematical analysis , surgery , mathematics , marketing , rate monotonic scheduling , economics , engineering , psychiatry
Emergency Departments (EDs) commonly face capacity imbalances and long wait times in a service system handling patients with different priorities. These problems are particularly important for low‐priority patients who often remain in the queue for extended periods. We investigate two distinct approaches to address these challenges: fast track (FT) and dynamic priority queue (DPQ). Traditionally, EDs have prioritized patients using an Emergency Severity Index (ESI), in conjunction with FT, to strictly or partially dedicate resources to different ESI patient classes. With our proposed DPQ, patients are prioritized using ESI and additional real‐time operational information about the patient, specifically the amount of accumulated wait time and flow time. Using an empirical simulation, we compare the impact of different resource allocation and prioritization approaches on patient length of stay (LOS), including the existing system at the ED, FT with strict and partial dedication and the possibility of shorter and less variable service times, and versions of the proposed DPQ using simple dynamic prioritization. Our main results are that: (i) the DPQ approach dominates the other approaches tested; (ii) for various ED sizes, FT with strict and partial dedication do not reduce average LOS of low‐priority patients without significantly increasing average LOS of high‐priority patients, unless service time mean and variance are reduced; (iii) DPQ using accumulated wait time or accumulated flow time improves performance. The results are robust to changes in the proportion of patients in each priority level. Overall, expanding decision making about patient prioritization from only considering the patient's clinical condition to also including operational data can improve performance dramatically, even without improved service times.