Publication Date:
2013-01-08
Description:
Background: Elective patient admission and assignment planning is an important task of the strategic and operationalmanagement of a hospital and early on became a central topic of clinical operations research. Themanagement of hospital beds is an important subtask. Various approaches have been proposed, involving thecomputation of efficient assignments with regard to the patients' condition, the necessity of the treatment, andthe patients' preferences. However, these approaches are mostly based on static, unadaptable estimates of thelength of stay and, thus, do not take into account the uncertainty of the patient's recovery. Furthermore, theeffect of aggregated bed capacities have not been investigated in this context.Computer supported bed management, combining an adaptable length of stay estimation with the treatment ofshared resources (aggregated bed capacities) has not yet been sufficiently investigated.The aim of our work is: 1) to define a cost function for patient admission taking into account adaptable lengthof stay estimations and aggregated resources, 2) to define a mathematical program formally modeling theassignment problem and an architecture for decision support, 3) to investigate four algorithmic methodologiesaddressing the assignment problem and one base-line approach, and 4) to evaluate these methodologies w.r.t.cost outcome, performance, and dismissal ratio. Methods: The expected free ward capacity is calculated based on individual length of stay estimates, introducingBernoulli distributed random variables for the ward occupation states and approximating the probabilitydensities. The assignment problem is represented as a binary integer program. Four strategies for solving theproblem are applied and compared: an exact approach, using the mixed integer programming solver SCIP; andthree heuristic strategies, namely the longest expected processing time, the shortest expected processing time,and random choice. A baseline approach serves to compare these optimization strategies with a simple modelof the status quo. All the approaches are evaluated by a realistic discrete event simulation: the outcomes arethe ratio of successful assignments and dismissals, the computation time, and the models cost factors.
Electronic ISSN:
1472-6947
Topics:
Computer Science
,
Medicine
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