Reinforcement learning for optimizing responses in care processes

Abstract

Prescriptive process monitoring aims to derive recommendations for optimizing complex processes. While previous studies have successfully used reinforcement learning techniques to derive actionable policies in business processes, care processes present unique challenges due to their dynamic and multifaceted nature. For example, at any stage of a care process, a multitude of actions is possible. In this study, we follow the Reinforcement Learning (RL) approach and present a general approach that uses event data to build and train Markov decision processes. We proposed three algorithms including one that takes the elapsed time into account when transforming an event log into a semi-Markov decision process. We evaluated the RL approach using an aggression incident data set. Specifically, the goal is to optimize staff member actions when clients are displaying different types of aggressive behavior. The Q-learning and SARSA are used to find optimal policies. Our results showed that the derived policies align closely with current practices while offering alternative options in specific situations. By employing RL in the context of care processes, we contribute to the ongoing efforts to enhance decision-making and efficiency in dynamic and complex environments.