Multi-surgeon and priority-aware scheduling for operating rooms scheduling: a robust-based approach

Operating Room (OR) scheduling is crucial to allow efficient use of ORs. Currently, the predicted durations of surgical procedures are unreliable and the OR schedulers have to follow the progress of the procedures in order to update the daily planning accordingly. The OR schedulers often acquire the needed information through verbal communication with the OR staff, which causes undesired interruptions of the surgical process. The aim of this study was to develop a system that predicts in real-time the remaining procedure duration and to test this prediction system for reliability and usability in an OR. The prediction system was based on the activation pattern of one single piece of equipment, the electrosurgical device. The prediction system was tested during 21 laparoscopic cholecystectomies, in which the activation of the electrosurgical device was recorded and processed in real-time using pattern recognition methods. The remaining surgical procedure duration was estimated and the optimal timing to prepare the next patient for surgery was communicated to the OR staff. The mean absolute error was smaller for the prediction system (14 min) than for the OR staff (19 min). The OR staff doubted whether the prediction system could take all relevant factors into account but were positive about its potential to shorten waiting times for patients. The prediction system is a promising tool to automatically and objectively predict the remaining procedure duration, and thereby achieve optimal OR scheduling and streamline the patient flow from the nursing department to the OR.

Due to surgery duration variability and arrivals of emergency surgeries, the planned Operating Room (OR) schedule is disrupted throughout the day which may lead to a change in the start time of the elective surgeries. These changes may result in undesirable situations for patients, wards or other involved departments, and therefore, the OR schedule has to be adjusted. In this paper, we develop a decision support system (DSS) which assists the OR manager in this decision by providing the three best adjusted OR schedules. The system considers the preferences of all involved stakeholders and only evaluates the OR schedules that satisfy the imposed resource constraints. The decision rules used for this system are based on a thorough analysis of the OR rescheduling problem. We model this problem as an Integer Linear Program (ILP) which objective is to minimize the deviation from the preferences of the considered stakeholders. By applying this ILP to instances from practice, we determined that the given preferences mainly lead to (i) shifting a surgery and (ii) scheduling a break between two surgeries. By using these changes in the DSS, the performed simulation study shows that less surgeries are canceled and patients and wards are more satisfied, but also that the perceived workload of several departments increases to compensate this. The system can also be used to judge the acceptability of a proposed initial OR schedule.

Operating room (OR) scheduling is a critical challenge in healthcare, directly impacting patient outcomes and hospital efficiency. Traditional methods often struggle with the complex, multi-level constraints and uncertainties inherent in OR scheduling, such as resource limitations, variable surgery durations, and emergency cases. This study aims to develop a novel hybrid framework that optimizes OR scheduling by integrating multi-level optimization with reinforcement learning and column generation techniques. The proposed framework decomposes the OR scheduling problem into strategic, tactical, and operational levels, enabling focused optimization at each layer while ensuring cohesive decision-making across the hierarchy. Reinforcement learning guides the column generation process, learning policies that identify promising scheduling options to enhance solution quality and computational efficiency. Robust uncertainty handling mechanisms are incorporated to manage variability in surgery durations and resource availability without compromising tractability. Experiments were conducted using three years of real-world data from Shanxi Provincial People's Hospital, complemented by large-scale synthetic datasets to evaluate scalability and robustness of the framework. The framework demonstrates meaningful improvements in key operational metrics compared to traditional approaches. Analysis of three years of implementation shows consistent enhancements in operational efficiency, including a reduction in average patient waiting time by 15.8% (from 10.1 to 8.5 days), an increase in OR utilization by 5.4 percentage points (from 73.8% to 79.2%), and improved workload balance among surgeons. The framework maintains robust performance under uncertainty, achieving a 92.5% feasibility rate and reducing schedule disruptions by 26.2%. The proposed hybrid framework offers a practical and scalable solution for optimizing OR scheduling, demonstrating improvements in healthcare delivery and operational performance in real hospital environments. By effectively balancing multiple operational objectives while handling practical constraints and uncertainties, the framework provides a viable approach for healthcare systems seeking incremental yet sustainable improvements in efficiency and patient care.

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.

One of the critical issues in healthcare management is the operating room (OR) scheduling problem. Solutions to this problem consider surgery durations and allocate elective surgeries to OR sessions in order to create surgical lists of high quality. Determining the quality of a surgical list is a key undertaking within OR scheduling and is the focus of this research. Currently, probability- and/or expectation-based measures of surgical lists are used instead of statistical distributions of surgery lists to measure quality. The use of multiple measures, e.g., a combination of expectation and probability to assess a surgical list, complicates OR scheduling, so we introduce a new single measure – the OR scheduling metric – for evaluating surgical lists before their realisations, i.e., for use within OR scheduling. We apply the OR scheduling metric to an actual elective dataset and use simulation to demonstrate its use, including customised scheduling rules. We recommend the adoption of a benchmarked OR scheduling metric by the elective surgical services in hospitals with expected practical benefits in the long run, i.e., simpler OR scheduling and more desirable room utilisation, to be similar to that observed in our simulations.

Operating room (OR) planning and scheduling decisions involve the coordination of patients, medical staff, and hospital facilities. The patients arriving to the hospital are assigned to a surgery date and a surgery time slot. At the time of surgery, a suitable OR, the attending surgeon, supporting anesthesiologists, nurses, and, after the surgery, rooms in secondary facilities such as post-anesthesia care unit (PACU), intensive care unit (ICU), and ward need to be available. In order to deal with the complexity and the variety of problems faced in OR scheduling, it is useful to involve methods from operations research. In this chapter, we review the recent literature on the application of operations research to OR planning and scheduling. We start by discussing the impact of planning and scheduling of the ORs on the overall performance of a hospital. Next, we discuss the criteria for included publications and summarize the structure of Cardoen et al. (Eur J Oper Res 201:921–932, 2010) that served as the guideline for organization of this chapter. In the remainder of the chapter, we describe the evolution of the literature over the last 10 years with regard to the patient type, the different performance measures, the decision that has to be made, the incorporation of uncertainty, the operations research methodology, and the applicability of the research. Moreover, each of these evolutions will be demonstrated with a short review of some relevant papers. This chapter ends with conclusions and a discussion of interesting topics for further research.

This paper addresses the problem of operating room (OR) scheduling at the tactical level of hospital planning and control. Hospitals repetitively construct operating room schedules, which is a time-consuming, tedious, and complex task. The stochasticity of the durations of surgical procedures complicates the construction of operating room schedules. In addition, unbalanced scheduling of the operating room department often causes demand fluctuation in other departments such as surgical wards and intensive care units. We propose cyclic operating room schedules, so-called master surgical schedules (MSSs) to deal with this problem. In an MSS, frequently performed elective surgical procedure types are planned in a cyclic manner. To deal with the uncertain duration of procedures we use planned slack. The problem of constructing MSSs is modeled as a mathematical program containing probabilistic constraints. Since the resulting mathematical program is computationally intractable we propose a column generation approach that maximizes the operation room utilization and levels the requirements for subsequent hospital beds such as wards and intensive care units in two subsequent phases. We tested the solution approach with data from the Erasmus Medical Center. Computational experiments show that the proposed solution approach works well for both the OR utilization and the leveling of requirements of subsequent hospital beds.

Most operating room (OR) scheduling is done by scheduling personnel based on experience or heuristics. Such often results in excessive overtime for medical personnel and poor resource-use efficiencies. This study was designed to employ an integrated OR scheduling performance evaluation model (IOSPEM), which integrates quantitative and qualitative multiple objectives, to the improvement of OR scheduling quality. The fuzzy Delphi method was used to obtain the weight values of time factor, cost factor, and risk factor. The fuzzy set theory was applied to transform qualitative risk measures into quantitative values. The desirability function was utilized to integrate time, cost, and risk factors to develop the IOSPEM. The simulated annealing algorithm was used to develop the scheduling system and test proposed model performance. The proposed IOSPEM successfully integrated the quantitative and qualitative indices into a sole quantitative index. Experiment results show that the IOSPEM incorporating the simulated annealing algorithm is able to obtain the most efficient OR schedule and is helpful in reducing costs and fatigue risks. Operating room scheduling will be made more objective and efficient if OR scheduling personnel can simultaneously consider the cost, fatigue risk, and other factors in scheduling. Cost of each OR room should be considered to set appropriate cost coefficients in practical application of the IOSPEM. It is also suggested that other indices (e.g., OR overtime costs and OR nurse fatigue risks) also be considered in the proposed model so as to better reflect the actual scheduling environment. The procedure and methods implemented in this study may be used as the basis for further developing more effective and efficient OR scheduling systems.

Efficient operating room (OR) scheduling can improve OR utilization and reduce costs. We hypothesize that the scheduling office (ORSO) leading the modification scheduling process could increase OR utilization rate. Using retrospective data from a single tertiary hospital in two consecutive calendar years, we compared OR utilization rate, the number of daily cases and cumulative operative time in the pre- and post-implementation of scheduling process alteration. We operated about 100,609 cases in the OR during the study period. Daytime utilization rate increased from 85.6% to 89.4% (P < 0.001); overall OR utilization rate from 115.1% to 117.6% (P = 0.019); daily case numbers from 229.9 ± 7.3 to 239.6 ± 7.6 (P = 0.0.14); and cumulative operation time of total and daytime cases from 611.7 case-hour/day to 624.5 case-hour/day (P = 0.013) and from 510.8 case-hour/day to 533.8 case-hour/day (P < 0.001), respectively. Evening/night time case-hour significantly decreased from 100.9 case-hour/day to 90.7 case-hour/day (P < 0.001). The optimization of the scheduling process and coordination by the office during regular workhours resulted in enhanced OR efficiency. The OR scheduling office can act as a control tower to make OR management more flexible, which can improve efficiency and carry financial benefits in tertiary hospitals.

As the main source of income and expenses of hospitals, operating rooms (ORs) are the engines of hospitals' economics and they have a significant impact on public health. Many papers concerned regarding OR planning and scheduling problems, but they have not considerably applied the simulation-based optimization approach to solve the problems. In OR scheduling problems, there are a number of ORs and some surgeons with different specialties and each surgeon has a waiting list of some patients that each surgery should be planned and scheduled on the days when relevant surgeons are available. In this study, we consider two objectives: (1) minimizing the costs of overtime staffing and ORs’ idle time, and (2) minimizing the number of waiting days for patients. The mathematical model of OR scheduling problem is developed and solved by both exact method and simulation-based optimization approach. The comparison of results obtained from exact method and simulation-based optimization approach indicates that the exact method is only able to solve the small-size problems in reasonable time, while simulation-based optimization approach find competitive solutions for both small-size and large-size problems and solve large-size problems in an acceptable time.

The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durations, and the availability of beds for the entire length of stay (LOS) both in the Intensive Care Unit (ICU) and in the wards. A proper solution to the ORS problem is of primary importance for the healthcare service quality and the satisfaction of patients in hospital environments. In this paper we first present a solution to the problem based on Answer Set Programming (ASP). The solution is tested on benchmarks with realistic sizes and parameters, on three scenarios for the target length on 5-day scheduling, common in small–medium-sized hospitals, and results show that ASP is a suitable solving methodology for the ORS problem in such setting. Then, we also performed a scalability analysis on the schedule length up to 15 days, which still shows the suitability of our solution also on longer plan horizons. Moreover, we also present an ASP solution for the rescheduling problem, that is, when the offline schedule cannot be completed for some reason. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time.

To determine how frequently the published operating room (OR) schedule of case start times correlated with the actual OR entry time for elective cases in the Fraser Health Authority (FHA) in British Columbia, Canada. Society guidelines recommend periods of fasting of two hours prior to the induction of general anesthesia, but patients frequently end up fasting much longer. This review aimed to determine when patients arrive in the OR-either earlier than their scheduled time or later. The premise of some is that patients often arrive earlier, and advising short fasting times on the basis of the OR slate time is unreliable. I wished to determine whether this fear is justified. The computerized OR management database was queried for slated time of entry and actual time of entry for elective surgical cases in 11 hospitals in the FHA. The difference in slated vs actual entry time of patients (in 30 min blocks) was reviewed to examine the proportion of patients entering the OR earlier than 90 min from their slated time. Additionally, anesthesiologists from the Royal Columbian/Eagle Ridge Hospitals were surveyed for their recall of case delays that were related to inappropriate consumption of fluids. One hundred and twenty-three thousand eight hundred and sixty-five cases from 11 hospitals over a 32-month period were analyzed. A very small proportion of cases (753 of 123,865 cases, 0.6%) entered the OR earlier than 90 min from their slated time. Relatively few cases were actually cancelled because of inappropriate fluid consumption in the recall of anesthesiologists in two institutions. In the FHA, the OR schedule is a reliable guide to providing instructions on timing of preoperative fluid consumption in appropriately selected elective surgical patients. Quality of care and patient satisfaction will safely be enhanced by limiting the period of fasting for elective surgical patients.

Operating room scheduling is an important task in healthcare sector. This study proposes a Mixed Integer Nonlinear Programming (MINLP) mathematical model for the scheduling of the operating rooms. In the presented model, apart from scheduling the patients’ surgery process, shifting of the medical staff is also carried out. The innovation considered in the proposed model is aimed at prioritizing patients in the operation process, according to the priority and level of the patient’s emergencies, and operating room treatment processes. Ultimately, the proposed model is assessed with random data and in addition to scheduling patients based on the level of service delivery priority, the medical staff have been scheduled, as well. Furthermore, the sensitivity analysis results reveal that the proposed model is very sensitive to preoperative preparation times and this bottleneck needs to be improved in the hospitals. On the other hand, this study presents solutions to improve the operating room scheduling and improving the status of patient services to management and optimize operating room scheduling that result in satisfied patients.

Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy. Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within ±30min or ±20% of the scheduled duration if the scheduled time is ≥ or <150min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded. The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p <.001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p <.001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p =.0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p =.0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p =.22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954hr). The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.

We studied when allocated, but unfilled, operating room (OR) time of surgical services should be released to maximize OR efficiency. OR time was allocated for two surgical suites based on OR efficiency. Then, we analyzed real OR schedules. We added new hypothetical cases lasting 1, 2, or 3 h into OR time of the service that had the largest difference between allocated and scheduled cases (i.e., the most unfilled OR time) 5 days before the day of surgery. The process was repeated using the updated OR schedule available the day before surgery. The pair-wise difference in resulting overutilized OR time was calculated for n = 754 days of data from each of the two surgical suites. We found that postponing the decision of which service gets the new case until early the day before surgery reduces overutilized OR time by <15 min per OR per day as compared to releasing the allocated OR time 5 days before surgery. These results show that when OR time is released has a negligible effect on OR efficiency. This is especially true for ambulatory surgery centers with brief cases or large surgical suites with specialty-specific OR teams. What matters much more is having the correct OR allocations and, if OR time needs to be released, making that decision based on the scheduled workload. Provided operating room (OR) time is allocated and cases are scheduled based on maximizing OR efficiency, then whether OR time is released five days or one day before the day of surgery has a negligible effect on OR efficiency.