  Post-discharge care management coordinates patients' referrals to improve
their health after being discharged from hospitals, especially elderly and
chronically ill patients. In a care management setting, health referrals are
processed by a specialized unit in the managed care organization (MCO), which
interacts with many other entities including inpatient hospitals, insurance
companies, and post-discharge care providers. In this paper, a
machine-learning-guided discrete event simulation framework to improve health
referrals processing is proposed. Random-forest-based prediction models are
developed to predict the LOS and referral type. Two simulation models are
constructed to represent the as-is configuration of the referral processing
system and the intelligent system after incorporating the prediction
functionality, respectively. By incorporating a prediction module for the
referral processing system to plan and prioritize referrals, the overall
performance was enhanced in terms of reducing the average referral creation
delay time. This research will emphasize the role of post-discharge care
management in improving health quality and reducing associated costs. Also, the
paper demonstrates how to use integrated systems engineering methods for
process improvement of complex healthcare systems.
