Liam O’Neill

Scheduling Surgical Cases into Overflow Block Time— Computer Simulation of the Effects of Scheduling Strategies on Operating Room Labor Costs

“Overflow” block time is operating room (OR) time for a surgical group’s cases that cannot be completed in the regular block time allocated to each surgeon in the surgical group. Having such overflow block time increases OR utilization. The optimal way to schedule patients into a surgical group’s overflow block time is unknown. In this study, we developed a scheduling strategy that balances the OR manager’s need to reduce staffing costs and the needs of patients and surgeons for flexibility in choosing the dates and times of cases. We used computer simulation to evaluate our scheduling strategy. Surgeons and patients (i) can schedule the case into any overflow block within 2 wk; (ii) can only schedule the case into a “first case of the day” start time more than 2 wk in the future if there is not enough open time for the case within 2 wk; (iii) must schedule the case to be done within 4 wk; and (iv) are encouraged to perform the case on the earliest possible date. Staffing costs were lowest when the OR manager did not incorporate surgeon and patient preferences when scheduling cases into overflow block time. The strategy we developed provides surgeons and patients with some flexibility in scheduling, while only increasing OR staffing costs slightly over the minimum achieved when the OR manager controls scheduling. Implications: The strategy we developed provides surgeons and patients with some flexibility in scheduling, while increasing OR staffing costs only slightly over the minimum achieved when the OR manager controls scheduling. Staffing costs were lowest when the operating room (OR) manager did not incorporate surgeon and patient preferences when scheduling cases into overflow block time.

Data Envelopment Analysis to Determine by How Much Hospitals Can Increase Elective Inpatient Surgical Workload for Each Specialty

We apply data envelopment analysis to discharge data from the 115 hospitals in the rural state of a study hospital to answer three questions. We use a case study to investigate the usefulness and limitations of data envelopment analysis for assessing three common questions regarding hospital market capture for elective inpatient surgery. (i) The hospital studied in this paper performs 40% of the neurosurgery and 25% of the inpatient urology surgery in its state. Workloads are twice that of the hospitals with the next largest workloads. In contrast, the hospital performs 9% of its state’s cardiac surgery and has a workload half that of the largest volume hospital. The cardiac surgeons want more operating room time, faster turnovers, and capital investment for minimally invasive equipment. Controlling for the distance patients would need to travel for care, would increasing capacity likely increase cardiac surgery workload? (ii) The study hospital has fewer hospitalizations for thoracic surgery than for any other specialty. Is thoracic surgery inpatient workload of 121 lung resections large or small compared with those of orthopedics’ 213 hip replacements, urology’s 132 nephrectomies, and cardiac surgery’s 304 coronary artery bypass grafts? (iii) The hospital’s busiest specialty by discharges is orthopedics. How sensitive is the hospital’s orthopedic workload to changes in decision making at nearby competing hospitals?

Competition strategy and efficiency evaluation for decision making units with fixed-sum outputs

This paper develops a DEA (data envelopment analysis) model to accommodate competition over outputs. In the proposed model, the total output of all decision making units (DMUs) is fixed, and DMUs compete with each other to maximize their self-rated DEA efficiency score. In the presence of competition over outputs, the best-practice frontier deviates from the classical DEA frontier. We also compute the efficiency scores using the proposed fixed sum output DEA (FSODEA) models, and discuss the competition strategy selection rule. The model is illustrated using a hypothetical data set under the constant returns to scale assumption and medal data from the 2000 Sydney Olympics under the variable returns to scale assumption.

The Effect of RFID On Inventory Management and Control

Our thesis is that the evolution of information technology (IT) facilitates the flow of information, which in turn may reduce the variance of an inventory system, and hence its cost. We use radio-frequency identification (RFID) as a paradigm. RFID is the latest application of IT to tracking goods and services or anything for that matter, including human beings. It is an evolution from bar code and palette technology, and, in this chapter, we present the argument that RFID is superior in reducing the mean and variance of inventory cycle times. As inventory cost is a function of these (among other variables, such as unit holding and shortage costs), we show that RFID reduces this cost. Also, because RFID leads to rapid transmission of data, it would help avoid excessive inventories and shortages, further reducing total inventory cost. We argue that RFID is superior to existing identification technologies according to mean-variance stochastic dominance. We discuss the ethical implications and the societal trade-offs inherent in RFID, as society must decide how much of its privacy it is willing to curtail in the pursuit of lower prices versus physical security.

Evaluating the Efficiency of Hospitals’ Perioperative Services Using Dea

Elective surgery typically generates 40 percent or more of a hospital’s total revenue, and individual surgeons almost always have a net positive contribution margin. Perioperative services include surgical operations, preoperative care of patients, and post-operative care. This chapter presents a method to identify best practices among hospitals’ perioperative services using Data Envelopment Analysis (DEA). This analysis included 44,033 procedures performed by 3,502 surgeons at 53 non-metropolitan Pennsylvania hospitals. Eight procedures, each performed by one surgical specialty, were selected. For each hospital, DEA 1) identifies untapped markets for surgery; 2) identifies relatively high and low procedure volumes among specialties; and 3) suggests a strategy for increasing surgical volume for inefficient hospitals. Findings may be used by managers of perioperative services to aid in resource allocation decisions, such as hiring and recruitment among different surgical specialties.

If more competition is the answer, why hasn't it worked?

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