Mass-customized outpatient appointment rule generator

Abstract

This study claims that if a mass-customized appointment rule is developed for each individual outpatient doctor, it could improve the performance and service quality of outpatient clinics. A simulation-based optimization algorithm is proposed for mass-customized appointment rule generation. Various environmental factors, such as user-provided preferred appointment interval time (slot), new patient-to-return patient ratio, no-show patient rate, characteristics of walk-in patients, stochastic consultation times, and patient tardiness, are considered in generating implementable and practical rules. The simulation-based optimization algorithm uses the previously proposed 18 appointment rules as initial solutions, evaluates their effectiveness through a simulation with considerations of practical environmental factors, improves them by using a neighborhood search, and reports the best rule. The algorithm generates a variable-block-size/fixed-interval rule, which consists of the numbers of new and return patients in each appointment interval time. Computational experiments show that the proposed algorithm can generate effective appointment rules with user-provided preferred appointment interval time considering the various environmental factors. © 2019 University of Cincinnati. All rights reserved.