Optimization of diagnostic kits usage based on symptomatic presentation: an application of military decision making in public health.

In situations like pandemics or epidemics where patient surges occur, medical institutions face shortages of resources, such as beds, medications, and testing resources. Operations research has explored efficient resource allocation in this problem domain, but the scarcity of laboratory testing resources has received little attention. In such a situation, clinicians have conserved testing resources by diagnosing overtly symptomatic patients without testing. Although this heuristic approach has statistical ground in regard to pre-test probability and test accuracy, this has not been proven optimal in the literature. This paper applies military operations research’s firing-control theory to formalize this problem to maximize the overall value of the testing resources. We then propose a strategy that optimizes resource performance using the index of suspicion (IoS), a measure of infection likelihood given symptom manifestation. Numerical simulations indicated a sub-optimal strategy for deciding the testing of individual patients based on IoS, maximizing the use of limited resources. This approach provides a robust theoretical foundation to the heuristic strategy, while ensuring a practical method for resource allocation in healthcare settings, especially under conditions of limited testing capabilities, such as during pandemics and seasonal flu epidemics.