Pattern-based strategic surgical capacity allocation
[Display omitted] •Introduce the concept of “pattern” in surgical capacity allocation.•Propose a data-driven approach to coordinate operating room sharing.•Develop a bi-objective integer programming model for allocation optimization.•Conduct a case study with real data from a surgical division at Ma...
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Veröffentlicht in: | Journal of biomedical informatics 2019-06, Vol.94, p.103170-103170, Article 103170 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | [Display omitted]
•Introduce the concept of “pattern” in surgical capacity allocation.•Propose a data-driven approach to coordinate operating room sharing.•Develop a bi-objective integer programming model for allocation optimization.•Conduct a case study with real data from a surgical division at Mayo Clinic.•Show substantial potential in improving surgical capacity utilization.
Strategic allocation of limited operating room (OR) capacity to surgeons is crucial for the coordination of surgical work flow, including planning of consultation and surgery days, and staff assignment to perioperative teams. However, it is a challenging problem in practice, since the capacity allocation needs to be cyclic for schedule predictability and surgical team coordination, and also needs to satisfy surgeons’ preferences. It is further complicated by the practice of surgeons sharing ORs.
In this study, we propose a mathematical optimization model to coordinate capacity allocation among surgeons in order to improve the utilization of surgical capacity. We introduce the concept of capacity allocation patterns to account for schedule cyclicity and surgeons’ preferences. Further, we develop a data-driven approach to coordinate OR sharing among surgeons based on their historical OR usage.
The proposed methodology is applied to a case study with data from a surgical division at Mayo Clinic. Compared with the state-of-the-practice, the proposed approach shows a substantial potential in reducing the maximum number of ORs allocated daily to the division with little overtime. With a solution time of less than 0.5 s, the proposed methodology can be readily used as a decision support tool in surgical practice. |
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ISSN: | 1532-0464 1532-0480 |
DOI: | 10.1016/j.jbi.2019.103170 |