Todd R. Huschka

Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty

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.

Operating Room Pooling and Parallel Surgery Processing Under Uncertainty

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.

Impact of a Diabetes Electronic Management System on the Care of Patients Seen in a Subspecialty Diabetes Clinic

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.

Adapting the Chronic Care Model to Treat Chronic Illness at a Free Medical Clinic

This pilot project was designed to determine the feasibility and effectiveness of an adaptation of the chronic care model applied to uninsured patients in a free medical clinic staffed by volunteer physicians. Of the 149 enrolled patients, 117 had hypertension, 91 had diabetes, and 51 had hyperlipidemia. Patients were enrolled in a chronic disease registry from March 1, 2001 through September 30, 2002 at the Salvation Army Free Clinic (SAFC). Two part-time registered nurses served as care managers providing disease-specific management using evidence-based guidelines. Consistent specialty consultation was available via phone, e-mail, or physician visit. Patient self-management was encouraged through collaborative goal setting. There were 40 patients lost to follow-up; 109 completed the study. A clinically significant improvement was obtained in at least one chronic disease for 79 patients. The chronic care model was a useful template for the delivery of effective chronic disease care to a group of uninsured patients at a free medical clinic.

Optimal booking and scheduling in outpatient procedure centers

Patient appointment booking, sequencing, and scheduling decisions are challenging for outpatient procedure centers due to uncertainty in procedure times and patient attendance. We extend a previously developed appointment scheduling model to formulate a model based on a two-stage stochastic mixed integer program for optimizing booking and appointment times in the presence of uncertainty. The objective is to maximize expected profit. Analytical insights are reported for special cases and experimental results show that they provide useful rules of thumb for more general problems. Three solution methods are described which take advantage of the underlying structure of the stochastic program, and a series of experiments are performed to determine the best method. A case study based on an endoscopy suite at a large medical center is used to draw a number of useful managerial insights for procedure center managers.

Bi-criteria evaluation of an outpatient procedure center via simulation

Surgical services require the coordination of many activities, including patient check-in and surgical preparation, surgery, and recovery after surgery. Each of these activities requires the availability of resources including staff, operating rooms, and intake and recovery beds. Furthermore, each of these activities has substantial uncertainty in their duration. The combination of a complex resource constrained environment, and uncertainty in the duration of activities, creates challenging scheduling problems. In this study we report on a discrete event simulation model of an outpatient surgical suite, and investigate the impact of several sequencing and scheduling heuristics on competing performance criteria.

Do Complication Screening Programs Detect Complications Present at Admission

BACKGROUND A study was undertaken to verify the accuracy of computer algorithms on administrative data to identify hospital complications. The assessment was based on a medical records indicator that differentiated hospital-acquired conditions from preexisting comorbidities. METHODS The indicators for identifying potential hospital complications were applied to all secondary diagnoses to distinguish hospital-acquired from preexisting conditions for all 1997-1998 discharges. RESULTS Of the 95 defined complication types, cases were found with secondary diagnoses that met the criteria for 71 different complications. Sixty-nine of these complications had one or more cases with the trigger diagnosis coded as an acquired condition. Thirty-five complications had at least 30 cases with acquired conditions. Hospital complications add greatly to costs; for example, postoperative septicemia increased the hospital bill by more

Using simulation in the implementation of an outpatient procedure center

25,000, added 13 hospital days to the stay, and increased hospital mortality by 16.6%. CONCLUSIONS Current complication algorithms identify many cases where the condition was actually present on hospital admission. This fact, coupled with the known variability in coding between institutions, makes comparisons between hospitals on many of the complications problematic. Collection of the present-on-admission flag significantly reduces the noise in monitoring complication rates.

Capacity management and patient scheduling in an outpatient clinic using discrete event simulation

Creation of an outpatient procedure center (OPC) is a complicated endeavor, requiring a detailed understanding of the resources available and the procedures to be performed. Miscalculation of resource allocation or patient flow through the area can result in the waste of expensive resources, patient dissatisfaction, and health care provider inefficiency. The use of discrete event simulation can assist in the design of an OPC with the ultimate goal of reducing resource waste and improving patient flow through the system. In this article we provide a case study of the application of a discrete event simulation model used to support analysis required for moving an existing group (interventional procedures for pain medicine) into a new area. This resulted in major changes to the group¿s practice and modified the new facility utilization.

A simulation tool to support recovery bed planning for surgical patients

Capacity management and scheduling decisions are important for managing an outpatient clinic in which multiple classes of patients are treated. After an appointment is scheduled, it can be rescheduled, cancelled, or a patient may not show-up on their appointment day. This study simulates the behavior of patients with regard to the time to appointment, examining different demand rates and service times for each patient class (new external patients, internal patients, established patients and subsequent visit patients); we also consider different delay-dependent reschedule, cancellation, and no-show rates. A discrete event simulation model is developed to analyze the effects of allowing different appointment windows, i.e., the maximum time between the appointment request date and the actual appointment date, for different patient classes. Capacity utilization, patient access, and financial rewards are used as the performance indicators.