Approximate dynamic programming approaches for appointment scheduling with patient preferences

Xin Stephen LI, Jin WANG, Richard Y.K. FUNG

Research output: Contribution to journalArticlespeer-review

13 Citations (Scopus)

Abstract

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)16-25
JournalArtificial Intelligence in Medicine
Volume85
Early online dateFeb 2018
DOIs
Publication statusPublished - Apr 2018

Citation

Li, X., Wang, J., & Fung, R. Y. K. (2018). Approximate dynamic programming approaches for appointment scheduling with patient preferences. Artificial intelligence in Medicine, 85, 16-25. doi: 10.1016/j.artmed.2018.02.001

Keywords

  • Appointment scheduling
  • Health service
  • Dynamic programming
  • Markov processes

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