Automated Diagnosis of Clinic Workflows

TL;DR

This paper presents a constraint optimization approach to diagnose and improve outpatient clinic workflows, aiming to reduce delays and enhance scheduling efficiency.

Contribution

It introduces a generalizable method for diagnosing workflow disruptions in clinics using constraint optimization, applicable across different healthcare settings.

Findings

Long cycle times significantly impact schedule adherence.

The method identifies minimal appointment changes to restore on-time schedules.

Potential to reduce patient wait times and improve provider utilization.

Abstract

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.