Automated Diagnosis of Clinic Workflows
This work addresses scheduling inefficiencies for healthcare providers, but it is incremental as it applies an existing optimization approach to a specific clinic setting.
The paper tackled the problem of outpatient clinics running behind schedule by developing a constraint optimization method to diagnose workflow disruptions, finding that long cycle times had a greater impact on schedules than late patients in a Vanderbilt clinic study from March to April 2017.
Outpatient clinics often run behind schedule due to patients who arrive late or appointments that run longer than expected. We sought to develop a generalizable method that would allow healthcare providers to diagnose problems in workflow that disrupt the schedule on any given provider clinic day. We use a constraint optimization problem to identify the least number of appointment modifications that make the rest of the schedule run on-time. We apply this method to an outpatient clinic at Vanderbilt. For patient seen in this clinic between March 27, 2017 and April 21, 2017, long cycle times tended to affect the overall schedule more than late patients. Results from this workflow diagnosis method could be used to inform interventions to help clinics run smoothly, thus decreasing patient wait times and increasing provider utilization.