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An iterative approach for case mix planning under uncertainty

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  • Freeman, Nickolas
  • Zhao, Ming
  • Melouk, Sharif

Abstract

Case mix planning refers to allocating available time in the operating rooms composing an operating theater (OT) among different surgical specialties. Case mix planning is an important tool for achieving the goals of a hospital with respect to quality of care and financial position. Case mix planning is becoming increasingly prevalent as hospital reimbursement continues to shift from fee-for-service to reimbursement based on diagnostic-related groups. Existing approaches for case mix planning in the academic and medical literature follow a traditional approach that identifies a single “optimal” solution. To ensure tractability, such approaches often exclude several complicating factors such as uncertain patient arrivals, uncertain operation time requirements, and the arrival of patients requiring urgent care. The exclusions limit the applicability of the solution in practice. Thus, we develop a multi-phase approach that utilizes mathematical programming and simulation to generate a pool of candidate solutions. Using simulation allows us to evaluate each candidate solution with respect to a broad range of strategic and operational performance measures including expected patient reimbursement, overutilization of the OT, and the utilization of downstream recovery wards. Providing a pool of solutions, instead of a single solution, gives decision-makers several options from which they may select based on hospital goals. We conduct experiments based on a large, publicly available dataset that documents patient admissions in 203 U.S. hospitals. In comparison to a more traditional single-solution approach, we show that our solution pool approach identifies case mix plans with higher expected patient reimbursement, lower overutilization of OT time, and lower variability in the number of beds required in downstream recovery wards.

Suggested Citation

  • Freeman, Nickolas & Zhao, Ming & Melouk, Sharif, 2018. "An iterative approach for case mix planning under uncertainty," Omega, Elsevier, vol. 76(C), pages 160-173.
  • Handle: RePEc:eee:jomega:v:76:y:2018:i:c:p:160-173
    DOI: 10.1016/j.omega.2017.04.006
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    Cited by:

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    3. Aisha Tayyab & Saif Ullah & Mohammed Fazle Baki, 2023. "An Outer Approximation Method for Scheduling Elective Surgeries with Sequence Dependent Setup Times to Multiple Operating Rooms," Mathematics, MDPI, vol. 11(11), pages 1-15, May.
    4. Bovim, Thomas Reiten & Christiansen, Marielle & Gullhav, Anders N. & Range, Troels Martin & Hellemo, Lars, 2020. "Stochastic master surgery scheduling," European Journal of Operational Research, Elsevier, vol. 285(2), pages 695-711.
    5. A, Augustin & P, Jouvet & N, Lahrichi & A, Lodi & LM, Rousseau, 2022. "A data-driven approach to include availability of ICU beds in the planning of the operating room," Omega, Elsevier, vol. 109(C).
    6. McRae, Sebastian & Brunner, Jens O., 2020. "Assessing the impact of uncertainty and the level of aggregation in case mix planning," Omega, Elsevier, vol. 97(C).
    7. Lien Wang & Erik Demeulemeester & Nancy Vansteenkiste & Frank E. Rademakers, 2022. "On the use of partitioning for scheduling of surgeries in the inpatient surgical department," Health Care Management Science, Springer, vol. 25(4), pages 526-550, December.

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