Static and dynamic appointment scheduling to improve patient access time

Corine Laan, Maartje van de Vrugt* (Corresponding Author), Jan Olsman, Richard J. Boucherie

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
192 Downloads (Pure)

Abstract

Appointment schedules for outpatient clinics have great influence on efficiency and timely access to health care services. The number of new patients per week fluctuates, and capacity at the clinic varies because physicians have other obligations. However, most outpatient clinics use static appointment schedules, which reserve capacity for each patient type. In this paper, we aim to optimise appointment scheduling with respect to access time, taking fluctuating patient arrivals and unavailabilities of physicians into account. To this end, we formulate a stochastic mixed integer programming problem, and approximate its solution invoking two different approaches: (1) a mixed integer programming approach that results in a static appointment schedule, and (2) Markov decision theory, which results in a dynamic scheduling strategy. We apply the methodologies to a case study of the surgical outpatient clinic of the Jeroen Bosch Hospital. We evaluate the effectiveness and limitations of both approaches by discrete event simulation; it appears that allocating only 2% of the capacity flexibly already increases the performance of the clinic significantly.
Original languageEnglish
Pages (from-to)148-159
Number of pages12
JournalHealth systems
Volume7
Issue number2
Early online dateDec 2017
DOIs
Publication statusPublished - 4 May 2018

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Dynamic Scheduling
Schedule
Mixed Integer Programming
Stochastic Integer Programming
Decision Theory
Discrete Event Simulation
Healthcare
Approximate Solution
Scheduling
Optimise
Vary
Methodology
Evaluate

Keywords

  • Queueing model
  • Simulation
  • Mathematical programming
  • health care decision making

Cite this

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title = "Static and dynamic appointment scheduling to improve patient access time",
abstract = "Appointment schedules for outpatient clinics have great influence on efficiency and timely access to health care services. The number of new patients per week fluctuates, and capacity at the clinic varies because physicians have other obligations. However, most outpatient clinics use static appointment schedules, which reserve capacity for each patient type. In this paper, we aim to optimise appointment scheduling with respect to access time, taking fluctuating patient arrivals and unavailabilities of physicians into account. To this end, we formulate a stochastic mixed integer programming problem, and approximate its solution invoking two different approaches: (1) a mixed integer programming approach that results in a static appointment schedule, and (2) Markov decision theory, which results in a dynamic scheduling strategy. We apply the methodologies to a case study of the surgical outpatient clinic of the Jeroen Bosch Hospital. We evaluate the effectiveness and limitations of both approaches by discrete event simulation; it appears that allocating only 2{\%} of the capacity flexibly already increases the performance of the clinic significantly.",
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Static and dynamic appointment scheduling to improve patient access time. / Laan, Corine; van de Vrugt, Maartje (Corresponding Author); Olsman, Jan; Boucherie, Richard J.

In: Health systems, Vol. 7, No. 2, 04.05.2018, p. 148-159.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Laan, Corine

AU - van de Vrugt, Maartje

AU - Olsman, Jan

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AB - Appointment schedules for outpatient clinics have great influence on efficiency and timely access to health care services. The number of new patients per week fluctuates, and capacity at the clinic varies because physicians have other obligations. However, most outpatient clinics use static appointment schedules, which reserve capacity for each patient type. In this paper, we aim to optimise appointment scheduling with respect to access time, taking fluctuating patient arrivals and unavailabilities of physicians into account. To this end, we formulate a stochastic mixed integer programming problem, and approximate its solution invoking two different approaches: (1) a mixed integer programming approach that results in a static appointment schedule, and (2) Markov decision theory, which results in a dynamic scheduling strategy. We apply the methodologies to a case study of the surgical outpatient clinic of the Jeroen Bosch Hospital. We evaluate the effectiveness and limitations of both approaches by discrete event simulation; it appears that allocating only 2% of the capacity flexibly already increases the performance of the clinic significantly.

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