Block scheduling in practice: An optimal decomposition strategy for nonidentical operating rooms

Operating Room (OR) scheduling is a critical factor affecting overall hospital performance. We examine OR scheduling from two perspectives. In the first perspective we propose a scheme for OR block scheduling that uses a heuristic developed for a three-machine flow shop where the three phases of the peri-operative process (pre-op, OR, and post-op) correspond to the three-machine flow shop. This approach facilitates a hospital-as-a-system perspective. The second perspective used to examine OR scheduling is adaptive control of the OR slate. Recognizing that there are many factors affecting OR throughput performance, especially preemptions from emergent and urgent cases, adaptive control of the OR slate is necessary. To realistically improve performance, adaptive control of the OR slate should incorporate constraints on how surgeries can be rescheduled. We examine the benefits from adaptive control of the OR slate that uses a Priority First Fit Decreasing (PFFD) heuristic while incorporating constraints on OR slate rescheduling. The PFFD heuristic is a priority-driven variation of the classic FFD heuristic used in bin packing problems. We develop a scheme for OR block scheduling and our PFFD heuristic. We then demonstrate our PFFD heuristic in a simulation-based case study, and subsequently run a simulation using 1000 instances to test the performance of our PFFD heuristic in OR slate scheduling and OR slate adaptive control showing improvements in performance relative to the frequently used first-come-first-served rule.

Operating Room Planning under Surgery Type and Priority Constraints

Discrete event simulation is a well‐established tool for examining the effect of different operating room (OR) block schedules on various performance metrics within the OR suite and adjacent units. However, one unit that has rarely been studied is the sterilization processing department (SPD), which cleans and assembles reusable OR instruments. As part of a larger research study, we developed a series of OR block assignment models that sought to reduce the workload of the SPD and developed a tray optimization model to reduce the number of instruments on increasingly bloated instrument trays. While initial numerical experiments were promising, a comprehensive simulation model of the OR and SPD was needed to more thoroughly examine how potential changes to the block schedule and/or more efficient tray configurations could improve SPD processing times. In this article, we incorporate the SPD into an existing simulation model of an OR suite, which is the first of its kind, and examine the effect that different block schedules and tray configurations have on SPD processing times. Simulation results confirm earlier numerical computations. Furthermore, simulation results suggest that more efficient instrument tray configurations are a much better and more viable method for improving SPD processing time than reconfiguring block schedules.

ABSTRACTThe perioperative services (periop) manager establishes policies to manage the operating room (OR) resources that affect both patient and surgeon satisfaction with the hospital's service. Both the surgeon and the patient are customers of the periop department, and their satisfaction is a measure of the service quality. The periop manager uses block scheduling to allocate OR time. This technique gives one surgical practice (or service specialty) priority access to a block of time for scheduling procedures in an OR. Hospitals seek to obtain more of the surgeon's practice by allocating blocks to the surgeon, but the hospital must balance this OR time allocation with the risk that the OR time may not be used. In doing so, the periop manager creates a schedule that balances the hospital's risk of staff working overtime (beyond the OR's scheduled hours of blocked time) with the risk of staff being idle during a block of time reserved for a surgical practice that did not use it. The authors propose an OR management policy that creates service-specific blocks of OR time as well as open-posting blocks (those blocks shared by all surgical practices) and propose that this policy can increase access to the OR for more surgeons and, consequently, increase both surgeon and patient satisfaction. Through the use of two quality metrics–averseness to overtime and flexibility in sharing block time across services–this article examines how the setting of these two metrics affects OR performance. Historical data were used as an input into a modeling framework that created a feasible block schedule for a set of ORs. The block allocation schedule was then evaluated, with a portion of the data set reserved for testing. In an experiment at a large teaching hospital, results indicated that for every 20 percent increase in a manager's averseness to overtime, the hours in reserved blocks increased by 3 percent and utilization decreased by 2 percent. Similarly, a 33 percent increase in open posting flexibility translated into a reduced need of one OR.

Surgical block time satisfaction: A multi-institutional experience across twelve surgical disciplines

Speaking Systems Engineering: Bilingualism in Health Care Delivery Organizations

Highlights From the Third Annual Mayo Clinic Conference on Systems Engineering and Operations Research in Health Care

Administrators routinely seek to increase contribution margin (revenue minus variable costs) to better cover fixed costs, provide indigent care, and meet other community service responsibilities. Hospitals with high operating room (OR) utilizations can allocate OR time for elective surgery to surgeons based partly on their contribution margins per hour of OR time. This applies particularly when OR caseload is limited by nursing recruitment. From a hospital's annual accounting data for elective cases, we calculated the following for each surgeon's patients: variable costs for the entire hospitalization or outpatient visit, revenues, hours of OR time, hours of regular ward time, and hours of intensive care unit (ICU) time. The contribution margin per hour of OR time varied more than 1000% among surgeons. Linear programming showed that reallocating OR time among surgeons could increase the overall hospital contribution margin for elective surgery by 7.1%. This was not achieved simply by taking OR time from surgeons with the smallest contribution margins per OR hour and giving it to the surgeons with the largest contribution margins per OR hour because different surgeons used differing amounts of hospital ward and ICU time. We conclude that to achieve substantive improvement in a hospital's perioperative financial performance despite restrictions on available OR, hospital ward, or ICU time, contribution margin per OR hour should be considered (perhaps along with OR utilization) when OR time is allocated. For hospitals where elective surgery caseload is limited by nursing recruitment, to increase one surgeon's operating room time either another surgeon's time must be decreased, nurses need to be paid a premium for working longer hours, or higher-priced "traveling" nurses can be contracted. Linear programming was performed using Microsoft Excel to estimate the effect of each of these interventions on hospital contribution margin.

Fire in Labor and Delivery: Simulation Case Scenario

Durable Improvements in Efficiency, Safety, and Satisfaction in the Operating Room

Optimizing Operating Room Scheduling

Background: Operating room (OR) planning involves the creation of a master surgical schedule in which surgeons are assigned to specific operating rooms (ORs) on specific days of a week. The master schedule is typically one or two weeks long repeatable for several months. Objectives: The purpose of this study was to recommend using a mathematical program to generate a rotation in a way that the limited operating room capacity could be distributed based on smoothing expected demand for in-patient beds. Patients and Methods: This study concentrated on the service-level scheduling at Sunnybrook Health Sciences Centre in Toronto, Canada, to build such a model. We assumed that the number of blocks (days) for each surgeon was given, and that the expected case-mix for each surgeon was chosen by random sampling based on historical data. The goal was to assign surgeons to the blocks so tat bed occupancy in the wards would become as stable as possible during the week. The planning problem was first formulated as a stochastic integer programming. Then, an approach with combination of Monte Carlo simulation and Premium Solver provided an approximate solution. Results: The integer program provided scheduled OR number and day of the week for each surgeon, corresponding to the sample. The final result of model, approximated by the proposed method, was the maximum number of beds for each surgical service throughout the week. These were the required bed capacities to handle demands for surgeries. Conclusions: An Integer Programming was presented to schedule OR and day of surgery for each surgeon with restrictions on the available ORs and required number of blocks. The problem was quickly solved using Premium Solver. The reliability of the results was highly dependent on the data. Another fundamental restriction for implementation of the results was to convince surgeons to accept changes in the schedules. The surgeon preferences might be included in the model constraints for more acceptable results.

Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department

Administrators at hospitals with a fixed annual budget may want to focus surgical services on priority areas to ensure its community receives the best health services possible. However, many hospitals lack the detailed managerial accounting data needed to ensure that such a change does not increase operating costs. The authors used a detailed hospital cost database to investigate by how much a change in allocations of operating room (OR) time among surgeons can increase perioperative variable costs. The authors obtained financial data for all patients who underwent outpatient or same-day admit surgery during a year. Linear programming was used to determine by how much changing the mix of surgeons can increase total variable costs while maintaining the same total hours of OR time for elective cases. Changing OR allocations among surgeons without changing total OR hours allocated will likely increase perioperative variable costs by less than 34%. If, in addition, intensive care unit hours for elective surgical cases are not increased, hospital ward occupancy is capped, and implant use is tracked and capped, perioperative costs will likely increase by less than 10%. These four variables predict 97% of the variance in total variable costs. The authors showed that changing OR allocations among surgeons without changing total OR hours allocated can increase hospital perioperative variable costs by up to approximately one third. Thus, at hospitals with fixed or nearly fixed annual budgets, allocating OR time based on an OR-based statistic such as utilization can adversely affect the hospital financially. The OR manager can reduce the potential increase in costs by considering not just OR time, but also the resulting use of hospital beds and implants.

Background: Estimating operative time with low accuracy and precision leads to suboptimal scheduling of available operating rooms (ORs) and waste of healthcare resources. We aimed to develop a predictive machine learning (ML) model for operative time using our institutional data to improve the scheduling of neurosurgical ORs, minimize waste, and maximize resource utilization.Methods: We developed a predictive model using our institution’s multicenter registry, which included diverse neurosurgical cases across three tertiary hospitals. We applied multiple ML techniques, including linear regression, support vector regression, deep neural networks, and extreme gradient boosting regression (XGBR). The best model was combined with the first fit decreasing bin-packing algorithm to create a practical OR scheduling system based on prior data.Results: The XGBR model exhibited the best performance, with lower mean absolute errors (MAEs) (root mean squared error: 52.24, MAE: 37.25) and higher R2 (0.76) values than other models. Implementing this model in a simulated OR scheduling scenario using institutional data, we successfully scheduled 30 random procedures with minimal residual time, such that the model ran out of procedures for the week with 86 and 171 min remaining in the cranial and spinal ORs, respectively, demonstrating potential efficiency gains. The cranial procedures were scheduled over 4 days and spinal procedures over 6 days, optimizing available OR time.Conclusion: Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.