Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents

TL;DR

This paper applies reinforcement learning algorithms to optimize decision-making in care processes, specifically for managing aggression incidents, demonstrating that learned policies can enhance existing staff actions.

Contribution

It introduces a reinforcement learning approach using Q-learning and SARSA to derive policies for handling aggression in care settings, addressing the complexity of dynamic care processes.

Findings

Policies are similar to current practices

Provides additional action options in specific situations

Demonstrates effectiveness of RL in care process optimization

Abstract

Previous studies have used prescriptive process monitoring to find actionable policies in business processes and conducted case studies in similar domains, such as the loan application process and the traffic fine process. However, care processes tend to be more dynamic and complex. For example, at any stage of a care process, a multitude of actions is possible. In this paper, we follow the reinforcement approach and train a Markov decision process using event data from a care process. The goal was to find optimal policies for staff members when clients are displaying any type of aggressive behavior. We used the reinforcement learning algorithms Q-learning and SARSA to find optimal policies. Results showed that the policies derived from these algorithms are similar to the most frequent actions currently used but provide the staff members with a few more options in certain situations.