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Advance Patient Appointment Scheduling

In: Markov Decision Processes in Practice

Author

Listed:
  • Antoine Sauré

    (University of Ottawa)

  • Martin L. Puterman

    (University of British Columbia)

Abstract

This chapter describes the use of the linear programming approach to approximate dynamic programming as a means of solving advance patient appointment scheduling problems, which are problems typically intractable using standard solution techniques. Starting from the linear programming approach to discounted infinite-horizon Markov decision processes, and employing an affine value function approximation in the state variables, the method described in this chapter provides a systematic way of identifying effective booking guidelines for advance patient appointment scheduling problems. Two applications found in the literature allow us to show how these guidelines could be used in practice to significantly increase service levels for medical appointments, measured as the percentage of patients booked within medically acceptable wait times, and thus to decrease the potential impact of delays on patients’ health.

Suggested Citation

  • Antoine Sauré & Martin L. Puterman, 2017. "Advance Patient Appointment Scheduling," International Series in Operations Research & Management Science, in: Richard J. Boucherie & Nico M. van Dijk (ed.), Markov Decision Processes in Practice, chapter 0, pages 245-268, Springer.
  • Handle: RePEc:spr:isochp:978-3-319-47766-4_8
    DOI: 10.1007/978-3-319-47766-4_8
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    Cited by:

    1. Marquinez, José Tomás & Sauré, Antoine & Cataldo, Alejandro & Ferrer, Juan-Carlos, 2021. "Identifying proactive ICU patient admission, transfer and diversion policies in a public-private hospital network," European Journal of Operational Research, Elsevier, vol. 295(1), pages 306-320.

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