Markov decision process applied to the control of hospital elective admissions.

Abstract

To present a decision model for elective (non-emergency) patient admissions control for distinct specialties on a periodic basis. The purpose of controlling patient admissions is to promote a more efficient utilization of hospital resources, thereby preventing idleness or excessive use of these resources, while considering their relative importance. The patient admission control is modeled as a Markov decision process. A hypothetical prototype is implemented, applying the value iteration algorithm. The model is able to generate an optimal admission control policy that maintains resource consumption close to the desired levels of utilization, while optimizing the established deviation costs. This is a complex model due to its stochastic dynamic and dimensionality. The model has great potential for application, and requires the development of customized solution methods.