In the aftermath of a mass-casualty incident, effective policies for timely evaluation and prioritization of patients can mean the difference between life and death. While operations research methods have been used to study the patient prioritization problem, prior research has either proposed decision rules that only apply to very simple cases, or proposed formulating and solving a mathematical program in real time, which may be a barrier to implementation in an urgent situation. We connect these two regimes by proposing a general decision support rule that can handle survival probability functions and an arbitrary number of patient classifications. The proposed survival lookahead policy generalizes not only a myopic policy and a cμ type rule, but also the optimal solution to a version of the problem with two priority classes. This policy has other desirable properties, including index policy structure. Using simple heuristic parameterizations, the survival lookahead policy yields an expected number of survivors that is almost as large as published methods that require mathematical programming, while having the advantage of an intuitive structure and requiring minimal computational support. 
A simple yet effective decision support policy for mass-casualty triage