Using Reinforcement Learning to Optimize Responses in Care Processes: A
  Case Study on Aggression Incidents
Bart J. Verhoef Xixi Lu
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.