Evaluating Appointment Postponement in Scheduling Patients at a Diagnostic Clinic

TL;DR

This paper introduces a new appointment scheduling policy for diagnostic clinics that reduces outpatient waiting times by allowing postponements, modeled via stochastic programming, and validated through simulation.

Contribution

It proposes a novel, low-capital policy for outpatient appointment postponement based on stochastic modeling and simulation validation.

Findings

Significant reduction in outpatient waiting times

Effective capacity allocation for different patient types

Simple policy outperforms traditional scheduling methods

Abstract

Diagnostic clinics are among healthcare facilities that suffer from long waiting times which can cause medical issues and lead to increases in patient no-shows. Reducing waiting times without significant capital investments is a challenging task. We tackle this challenge by proposing a new appointment scheduling policy for such clinics that does not require significant investments. The clinic in our study serves outpatients, inpatients, and emergency patients. Emergency patients must be seen on arrival, and inpatients must be given next day appointments. Outpatients, however, can be given later appointments. The proposed policy takes advantage of this by allowing the postponement of the acceptance of appointment requests from outpatients. The appointment scheduling process is modeled as a two-stage stochastic programming problem where a portion of the clinic capacity is allocated to inpatients and emergency patients in the first stage. In the second stage, outpatients are scheduled based on their priority classes. After a detailed analysis of the solutions obtained from the two-stage stochastic model, we develop a simple, non-anticipative policy for patient scheduling. We evaluate the performance of this proposed, easy-to-implement policy in a simulation study which shows significant improvements in outpatient indirect waiting times.