Patient Triage and Prioritization Under Austere Conditions

In war zones and economically deprived regions, because of extreme resource restrictions, a single provider may be the sole person in charge of providing emergency care to a group of patients. An important question the provider faces under such circumstances is whether or not to perform triage and how to prioritize the patients. By choosing to triage a particular patient, the provider can determine the health condition and thus the urgency of the patient, but that will come at the expense of delaying the actual service (stabilization or initial treatment) for that patient as well as all the other patients. Motivated by this problem, which also arises in other service contexts, we consider a service system where finitely many patients, all available at time zero, belong to one of the two possible triage classes, where each class is characterized by its waiting cost and expected service time. Patients’ class identities are initially unknown, but the service provider has the option to spend time on triage to determine the class of a patient. Our objective is to identify policies that balance the time spent on triage with the time spent on service by minimizing the total expected cost. We provide a complete characterization of the optimal dynamic policy and show that the optimal dynamic policy that specifies when to perform triage is determined by a switching curve, and we provide a mathematical expression for this curve. One insight that comes out of this characterization is that the server should start with performing triage when there are sufficiently many patients and never perform triage when there are few patients. Finally, we carry out a numerical study in which we demonstrate how one can use our mathematical results to develop policies that can be used in mass-casualty triage and prioritization, and we find that there are substantial benefits to using one of these policies instead of the simpler benchmarks. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2855 . This paper was accepted by Assaf Zeevi, stochastic models and simulation.

• 저널Management Science · 64(10) · 4471–4489
• 토픽Healthcare Operations and Scheduling Optimization · Emergency Medical Services
• DOI10.1287/mnsc.2017.2855
• 저자Zhankun Sun, Nilay Tanık Argon, Serhan Ziya