Markov decision process applied to the control of hospital elective admissions

Abstract

Objective: 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.

Methods: The patient admission control is modeled as a Markov decision process. A hypothetical prototype is implemented, applying the value iteration algorithm.

Results: 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.

Conclusion: 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.