A Hierarchical Deep Reinforcement Learning Approach for Outpatient Primary Care Scheduling

Primary care clinics suffer from high patient no-shows and late cancellation rates. Admitting walk-in patients to primary care setting helps improving clinic's utilization rates and accessibility, therefore, following an efficient walk-in patient admission policy is highly prominent. This research applies a learning-based outpatient management system investigating patient admission and assignment policies to improve the operational efficiency in a general outpatient clinic with high no-show and cancellation rates and daily walk-in requests. Contrary to the general outpatient literature, our results show that only 30% of the walk-in requests should be admitted to minimize the wait time of already admitted patients and providers' over time. Our results also suggest assigning more than 50% of the available slots of a clinic session to punctual patients who have an appointment, to minimize long-run costs. The model and the results, however, are generated based on specific data and parameters, and cannot be directly generalized to other clinics.