Citations

Operating 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.

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 paper, 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 counterpart, 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-to-implement 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 the stochastic recourse model, and has the benefit of limiting the worst-case outcome of the recourse problem.

OBJECTIVE To compare the compliance with diabetes care performance indicators by diabetes specialists using a diabetes electronic management system (DEMS) and by those using the traditional paper medical record. RESEARCH DESIGN AND METHODS A DEMS has been gradually introduced into our subspecialty practice for diabetes care. To assess the value of this DEMS as a disease management tool, we completed a retrospective review of the medical records of 82 randomly selected patients attending a subspecialty diabetes clinic (DC) during the first quarter of 1996. Eligible patients were defined by the suggested criteria from the American Diabetes Association Provider Recognition Program. During the first quarter of 1996, ∼ one half of the providers began using the DEMS for some but not all of their patient encounters. Neither abstractors nor providers were aware of the intent to examine performance in relationship to use of the DEMS. RESULTS Several measures were positively influenced when providers used the DEMS. The number of foot examinations, the number of blood pressure readings, and a weighted criterion score were greater (P < 0.01) for providers using the DEMS. There was evidence, although not statistically significant, for lower mean diastolic blood pressures (P = 0.043) in patients and for number of glycated hemoglobins documented (P = 0.018) by users of the DEMS. CONCLUSIONS Performance and documentation of the process of care for patients with diabetes in a subspecialty clinic are greater with the use of a DEMS than with the traditional paper record.