Strategic room type allocation for nursing wards through Markov chain modeling

Anders Reenberg Andersen, Wim Vancroonenburg, Greet Vanden Berghe

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Providing patients with the best possible care is the most essential function of any hospital. In an increasing number of countries, hospitals are governed by the number of patients they are able to attract and the corresponding services they provide for patients. One such service, which is often of significant importance for patients, is the option to choose their room type. Hospital decision makers would benefit from a strategic method for optimizing the configuration of room types among nursing wards by distinguishing between patients who prefer private rooms and those who have no preference concerning whether they are assigned to a private or shared room. Such a decision support method is currently non-existent, therefore the goal of this study is to provide a methodology for hospital management. Specifically, a mixed modeling approach is proposed which evaluates the patient flow behavior by applying a Continuous-Time Markov Chain within a heuristic search procedure. This procedure recursively improves a configuration of rooms among the wards by sampling from a gradually improved interpolation of an objective function. Based on patient data obtained from both a Danish and Belgian hospital, the performance and robustness of the proposed approach is validated through various numerical experiments, demonstrating that solutions within a relative gap of 1% from the optimum are attained in most cases.
Original languageEnglish
Article number101705
JournalArtificial Intelligence in Medicine
Volume99
Number of pages14
ISSN0933-3657
DOIs
Publication statusPublished - 2019

Keywords

  • Room allocation
  • Patient flow
  • Markov chains
  • Heuristics

Cite this

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title = "Strategic room type allocation for nursing wards through Markov chain modeling",
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Strategic room type allocation for nursing wards through Markov chain modeling. / Andersen, Anders Reenberg; Vancroonenburg, Wim; Vanden Berghe, Greet.

In: Artificial Intelligence in Medicine, Vol. 99, 101705, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

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AU - Andersen, Anders Reenberg

AU - Vancroonenburg, Wim

AU - Vanden Berghe, Greet

PY - 2019

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AB - Providing patients with the best possible care is the most essential function of any hospital. In an increasing number of countries, hospitals are governed by the number of patients they are able to attract and the corresponding services they provide for patients. One such service, which is often of significant importance for patients, is the option to choose their room type. Hospital decision makers would benefit from a strategic method for optimizing the configuration of room types among nursing wards by distinguishing between patients who prefer private rooms and those who have no preference concerning whether they are assigned to a private or shared room. Such a decision support method is currently non-existent, therefore the goal of this study is to provide a methodology for hospital management. Specifically, a mixed modeling approach is proposed which evaluates the patient flow behavior by applying a Continuous-Time Markov Chain within a heuristic search procedure. This procedure recursively improves a configuration of rooms among the wards by sampling from a gradually improved interpolation of an objective function. Based on patient data obtained from both a Danish and Belgian hospital, the performance and robustness of the proposed approach is validated through various numerical experiments, demonstrating that solutions within a relative gap of 1% from the optimum are attained in most cases.

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