Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents
This work addresses dynamic and complex care processes for staff members, but it is incremental as it applies existing methods to a new domain with limited novel findings.
The paper tackled optimizing staff responses to aggression incidents in care processes using reinforcement learning, finding that derived policies were similar to current frequent actions but offered a few more options in certain situations.
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.