An exact approach for tactical planning and patient selection for elective surgeries
•We combine allocation of surgery times with patient selection for elective surgery.•We provide an exact mixed integer programming model and demonstrate results.•We use clustering to manage complexity of individual patients.•We demonstrate applicability to what-if scenarios. The allocation of operat...
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| Veröffentlicht in: | European journal of operational research Jg. 268; H. 2; S. 728 - 739 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
16.07.2018
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| Schlagworte: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •We combine allocation of surgery times with patient selection for elective surgery.•We provide an exact mixed integer programming model and demonstrate results.•We use clustering to manage complexity of individual patients.•We demonstrate applicability to what-if scenarios.
The allocation of operating theatres plays a crucial role in hospital management. This paper describes the elective surgery planning problem at a tactical level, motivated by a case study in a local hospital in Melbourne. In this paper, we define a unique problem for determining an allocation of blocks of time on each day of a four-week cycle to surgical units while also selecting patient types to treat. A unique aspect of the problem is that a base Master Surgery Schedule (MSS) is given, and that the difference between the new optimal plan and the current MSS is bounded by a specified allowance.
We take into account a range of constraints related to availability of resources (human and material), multiple (possibly conflicting) objectives, as well as regulatory and legal controls. We assume similar patients in each surgical unit are grouped together and each patient waiting to have surgery is classified to a given surgery group that has a given resource demand, including duration of surgery and length of stay in the hospital. We present a multiple objective mixed integer programming model of this problem including some computational results. This model can provide insightful information to decision makers in the hospital whether they can meet their KPIs with their current resources and also the effect of increasing resources on various KPIs. Computational results show that most solutions can be obtained in a reasonable amount of time in multiple scenarios which is a significant result from computational and practical point view. |
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| ISSN: | 0377-2217 1872-6860 |
| DOI: | 10.1016/j.ejor.2018.01.048 |