Treatment Planning for Victims with Heterogeneous Time Sensitivities in Mass Casualty Incidents

Yunting Shi (), Nan Liu () and Guohua Wan ()
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Yunting Shi: Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China
Nan Liu: Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467
Guohua Wan: Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China

Operations Research, 2024, vol. 72, issue 4, 1400-1420

Abstract: The current emergency response guidelines suggest giving priority of treatment to those victims whose initial health conditions are more critical. Although this makes intuitive sense, it does not consider potential deterioration of less critical victims. Deterioration may lead to longer treatment time and irrecoverable health damage, but could be avoided if these victims were to receive care in time. Informed by a unique timestamps data set of surgeries carried out in a field hospital set up in response to a large-scale earthquake, we develop scheduling models to aid treatment planning for mass casualty incidents (MCIs). A distinguishing feature of our modeling framework is to simultaneously consider victim health deterioration and wait-dependent service times in making decisions. We identify conditions under which victims with a less critical initial condition have higher or lower priority than their counterparts in an optimal schedule—the priority order depends on victim deterioration trajectories and the resource (i.e., treatment time) availability. A counterfactual analysis based on our data shows that adopting our model would significantly reduce the surgical makespan and the total numbers of overdue and deteriorated victims compared with using the then-implemented treatment plan; dynamic adjustment of treatment plans (if a second batch of victims arrive) and care coordination among surgical teams could further improve operational efficiency and health outcomes. By demonstrating the value of adopting data-driven approaches in MCI response, our research holds strong potentials to improve emergency response and to inform its policy making.

Keywords: Policy Modeling and Public Sector OR; mass casualty incident; treatment planning; patient deterioration; data-driven modeling; scheduling (search for similar items in EconPapers)
Date: 2024
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