The allocation of surgeries to operating rooms (ORs) is a challenging combinatorial optimization problem. There is also significant uncertainty in the duration of surgical procedures, which further complicates assignment decisions. In this article, we present stochastic optimization models for the assignment of surgeries to ORs on a given day of surgery. The objective includes a fixed cost of opening ORs and a variable cost of overtime relative to a fixed length-of-day. We describe two types of models. The first is a two-stage stochastic linear program with binary decisions in the first-stage and simple recourse in the second stage. The second is its robust counter-part, in which the objective is to minimize the maximum cost associated with an uncertainty set for surgery durations. We describe the mathematical models, bounds on the optimal solution, and solution methodologies, including an easy-to-implement heuristic. Numerical experiments based on real data from a large health care provider are used to contrast the results for the two models, and illustrate the potential for impact in practice. Based on our numerical experimentation we find that a fast and easy-toimplement heuristic works fairly well on average across many instances. We also find that the robust method performs approximately as well as the heuristic, is much faster than solving than the stochastic recourse model, and has the benefit of limiting the worst-case outcome of the recourse problem.
O perating room (OR) scheduling is an important operational problem for most hospitals. In this study, we present a novel two-stage stochastic mixed-integer programming model to minimize total expected operating cost given that scheduling decisions are made before the resolution of uncertainty in surgery durations. We use this model to quantify the benefit of pooling ORs as a shared resource and to illustrate the impact of parallel surgery processing on surgery schedules. Decisions in our model include the number of ORs to open each day, the allocation of surgeries to ORs, the sequence of surgeries within each OR, and the start time for each surgeon. Realistic-sized instances of our model are difficult or impossible to solve with standard stochastic programming techniques. Therefore, we exploit several structural properties of the model to achieve computational advantages. Furthermore, we describe a novel set of widely applicable valid inequalities that make it possible to solve practical instances. Based on our results for different resource usage schemes, we conclude that the impact of parallel surgery processing and the benefit of OR pooling are significant. The latter may lead to total cost reductions between 21% and 59% on average.
Uncertainty in the duration of surgical procedures can cause long patient wait times, poor utilization of resources, and high overtime costs. We compare several heuristics for scheduling an Outpatient Procedure Center. First, a discrete event simulation model is used to evaluate how 12 different sequencing and patient appointment time‐setting heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared with current practice. Second, a bi‐criteria genetic algorithm (GA) is used to determine if better solutions can be obtained for this single day scheduling problem. Third, we investigate the efficacy of the bi‐criteria GA when surgeries are allowed to be moved to other days. We present numerical experiments based on real data from a large health care provider. Our analysis provides insight into the best scheduling heuristics, and the trade‐off between patient and health care provider‐based criteria. Finally, we summarize several important managerial insights based on our findings.
BACKGROUND: Disease registries, audit and feedback, and clinical reminders have been reported to improve care processes. OBJECTIVE:To assess the effects of a registrygenerated audit, feedback, and patient reminder intervention on diabetes care. PARTICIPANTS: Seventy-eight categorical Internal Medicine residents caring for 483 diabetic patients participated. Residents randomized to the intervention (n=39) received instruction on diabetes registry use; quarterly performance audit, feedback, and written reports identifying patients needing care; and had letters sent quarterly to patients needing hemoglobin A1c or cholesterol testing. Residents randomized to the control group (n=39) received usual clinic education.MEASUREMENTS: Hemoglobin A1c and lipid monitoring, and the achievement of intermediate clinical outcomes (hemoglobin A1c <7.0%, LDL cholesterol <100 mg/dL, and blood pressure <130/85 mmHg) were assessed. RESULTS:Patients cared for by residents in the intervention group had higher adherence to guideline recommendations for hemoglobin A1c testing (61.5% vs 48.1%, p = .01) and LDL testing (75.8% vs 64.1%, p=.02). Intermediate clinical outcomes were not different between groups.CONCLUSIONS: Use of a registry-generated audit, feedback, and patient reminder intervention in a resident continuity clinic modestly improved diabetes care processes, but did not influence intermediate clinical outcomes.
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