Chance-constrained admission scheduling of elective surgical patients in a dynamic, uncertain setting. (September 2019)

• Record Type: Journal Article
• Authors: Vancroonenburg, Wim; De Causmaecker, Patrick; Vanden Berghe, Greet
• Is Part Of: Operations research for health care. Volume 22(2019)
• Publication Date: 2019-09
• Subjects: Surgery admission scheduling -- Chance-constrained stochastic optimization -- Sample average approximation -- Meta-heuristic; Medical care -- Mathematical models -- Periodicals; Medical policy -- Mathematical models -- Periodicals; Health services administration -- Mathematical models -- Periodicals; Operations research -- Periodicals; Operations Research -- Periodicals; Health Services Research -- Periodicals; Health Policy -- Periodicals; Delivery of Health Care -- Periodicals; 362.106805
• DOI: 10.1016/j.orhc.2019.100196
• Languages: English
• ISSNs: 2211-6923

Abstract

Abstract: In the present contribution, a chance-constrained scheduling model is presented for determining admission dates of elective surgical patients. The admission scheduling model is defined considering a dynamic, stochastic decision-making environment. The primary aim of the model concerns the minimization of operating theatre costs and patient waiting times, while simultaneously avoiding bed shortages at a fixed certainty level through a chance-constrained formulation. This stochastic model is implemented by means of sample average approximation and is solved by a meta-heuristic algorithm. To illustrate the applicability of the model, the approach is used to implement four admission scheduling policies on this dynamic decision-making setting that are evaluated on different criteria in a computational study using simulation. The results show that the stochastic approach is able to account for the uncertainty in patients' length of stay and surgical procedure duration, enabling it to avoid bed shortages while still optimizing operating theatre costs and patient waiting times. Highlights: A chance-constrained model for admission scheduling of surgical patients is proposed. Sample average approximation is employed for approximating the stochastic model. A Late Acceptance Hill Climbing meta-heuristic is implemented for solving the models. The flexibility of the model is illustrated by implementing four admission policies. Computational results show that the model enables managing the risk on bed shortages.