Operating Room Scheduling Research Articles

Frustrations in Providing Orthopaedic Trauma Care: An OTA Member Survey Study.

To determine sources of frustration for surgeons and how they might affect job satisfaction within the orthopaedic trauma community. A 25-question survey regarding potential common causes of frustration, operating room (OR) utilization, and career satisfaction was electronically distributed to the Orthopaedic Trauma Association (OTA) membership. Four hundred seventy-one surgeons participated in the survey (response rate 27.5%). The highest ranked source of frustration was OR turnover time, followed by staff turnover, and OR scheduling. Most reported they were very satisfied with their career (n=240, 51.0%). The top three strategies to increase career satisfaction from most to least effective were increased compensation, case delay reduction, and decreased work hours. The majority of surgeons reported that if their number one frustration was solved, they would feel a higher level of career satisfaction (very satisfied: n=344, 73.1%). Most felt their cases did not start on time (n=273, 58.0%), with the majority of respondents reporting that their cases were delayed on average between 15-60 minutes (n=222, 81.9%). The number one ranked reason for case start delays was OR turnover and case setup, followed by the anesthesia team. Many surgeons within the orthopaedic trauma community reported concern with OR turnover time and staff turnover. Addressing these sources of frustration is crucial to maintaining surgeon well-being and can aid in optimizing delivery of patient care. Future studies should seek out institutions that have successfully addressed these common frustrations and identify potential strategies in order to improve the delivery of orthopaedic trauma care across North America. Level V.

Evaluating the Impact of the Level of Robustness in Operating Room Scheduling Problems.

Managing uncertainty in surgery times presents a critical challenge in operating room (OR) scheduling, as it can have a significant impact on patient care and hospital efficiency. Objectives: By incorporating robustness into the decision-making process, we can provide a more reliable and adaptive solution compared to traditional deterministic approaches. Materials and methods: In this paper, we consider a cardinality-constrained robust optimization model for OR scheduling, addressing uncertain surgery durations. By accounting for patient waiting times, urgency levels and delay penalties in the objective function, our model aims to optimise patient-centred outcomes while ensuring operational resilience. However, to achieve an appropriate balance between resilience and robustness cost, the robustness level must be carefully tuned. In this paper, we conduct a comprehensive analysis of the model's performance, assessing its sensitivity to robustness levels and its ability to handle different uncertainty scenarios. Results: Our results show significant improvements in patient outcomes, including reduced waiting times, fewer missed surgeries and improved prioritisation of urgent cases. Key contributions of this research include an evaluation of the representativeness and performance of the patient-centred objective function, a comprehensive analysis of the impact of robustness parameters on OR scheduling performance, and insights into the impact of different robustness levels. Conclusions: This research offers healthcare providers a pathway to increase operational efficiency, improve patient satisfaction, and mitigate the negative effects of uncertainty in OR scheduling.

Integrated Care in a Large Public System Improves Simple Kidney Stone Management.

In the Los Angeles County Department of Health Services-the second largest public health care system in the United States-clinical integration allows specialists and primary care providers to collaboratively provide specialty care. We used time-driven activity-based costing to compare patient burden and cost of kidney stone diagnosis, workup, and management with and without clinical integration. We interviewed and observed teams of physicians and staff to understand workflow practices and personnel, space, material, and device requirements for stone care in the 2 models. We created process maps that outline the entire arc of care and used time-driven activity-based costing to calculate the all-inclusive costs of kidney stone diagnosis, workup, and scheduling for surgical treatment. The total cost of kidney stone treatment per stone episode in the integrated pathway was $499.04, compared with $699.81 in the traditional, nonintegrated pathway, a difference of 29%. The number of steps needed to arrive at operating room scheduling was 11 with integration and 14 without it. Clinical integration resulted in more efficient and cost-effective care from patient, provider, and health system perspectives. Integrated care may improve access to specialty care and increase the value of care and reduce treatment burden on patients.

Influence of a surgeon's exposure to operating room turnover delays on patient outcomes.

A surgeon's daily performance may be affected by operating room organizational factors, potentially impacting patient outcomes. The aim of this study was to investigate the link between a surgeon's exposure to delays in starting scheduled operations and patient outcomes. A prospective observational study was conducted from 1 November 2020 to 31 December 2021, across 14 surgical departments in four university hospitals, covering various surgical disciplines. All elective surgeries by 45 attending surgeons were analysed, assessing delays in starting operations and inter-procedural wait times exceeding 1 or 2 h. The primary outcome was major adverse events within 30 days post-surgery. Mixed-effect logistic regression accounted for operation clustering within surgeons, estimating adjusted relative risks and outcome rate differences using marginal standardization. Among 8844 elective operations, 4.0% started more than 1 h late, associated with an increased rate of adverse events (21.6% versus 14.4%, P = 0.039). Waiting time surpassing 1 h between procedures occurred in 71.4% of operations and was also associated with a higher frequency of adverse events (13.9% versus 5.3%, P < 0.001). After adjustment, delayed operations were associated with an elevated risk of major adverse events (adjusted relative risk 1.37 (95% c.i. 1.06 to 1.85)). The standardized rate of major adverse events was 12.1%, compared with 8.9% (absolute difference of 3.3% (95% c.i. 0.6% to 5.6%)), when a surgeon experienced a delay in operating room scheduling or waiting time between two procedures exceeding 1 h, as opposed to not experiencing such delays. A surgeon's exposure to delay before starting elective procedures was associated with an increased occurrence of major adverse events. Optimizing operating room turnover to prevent delayed operations and waiting time is critical for patient safety.

Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples

This paper addresses a real-world Integrated Operating Room Planning and Scheduling (IORPS) problem encountered by a local hospital in Naples, characterized by stringent emergency management constraints, requiring treatment initiation within a 20-minute time-window. We tackle this problem by an original Integer Linear Programming formulation, capable of dealing with different operating room management strategies (open, block and block-modified). Our work differs from conventional cost-focused models by adopting a patient-oriented objective function, aligning with public hospitals obligations. The proposed method has been validated using real-world data provided by the hospital. The performed experimentation demonstrates the efficiency of the approach, capable of determining the optimal solution within an acceptable computation time that aligns with hospital requirements. Moreover, it also highlights the relevance of using our optimization approach to reduce delays in emergency responsiveness. This confirms its practical usage as a substitute for the current manual procedure.

Operating Room Scheduling via Answer Set Programming: improved encoding and test on real data

Abstract The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different units. In the past years, Answer Set Programming (ASP) has been successfully employed for addressing and solving the ORS problem. Despite its importance, due to the inherent difficulty of retrieving real data, all the analyses on ORS ASP encodings have been performed on synthetic data so far. In this paper, first we present a new, improved ASP encoding for the ORS problem. Then, we deal with the real case of ASL1 Liguria, an Italian health authority operating through three hospitals, and present adaptations of the ASP encodings to deal with the real-world data. Further, we analyse the resulting encodings on hospital scheduling data by ASL1 Liguria. Results on some scenarios show that the ASP solutions produce satisfying schedules also when applied to such challenging, real data.1

What could improve surgical data system at health facilities with high surgical volume in Ethiopia?

BackgroundThe effective management of surgical and anesthesia care relies on quality data and its readily availability for both patient-centered decision-making and facility-level improvement efforts. Recognizing this critical need, the Strengthening Systems for Improved Surgical Outcomes (SSISO) project addressed surgical care data management and information use practices across 23 health facilities from October 2019 to September 2022. This study aimed to evaluate the effectiveness of SSISO interventions in enhancing practices related to surgical data capture, reporting, analysis, and visualization.MethodsThis study employed a mixed method, pre- post intervention evaluation design to assess changes in data management and utilization practices at intervention facilities. The intervention packages included capacity building trainings, monthly mentorship visits facilitated by a hub-and-spoke approach, provision of data capture tools, and reinforcement of performance review teams. Data collection occurred at baseline (February – April 2020) and endline (April – June 2022). The evaluation focused on the availability and appropriate use of data capture tools, as well as changes in performance review practices. Appropriate use of registers was defined as filling all the necessary data onto the registers, and this was verified by completeness of selected key data elements in the registers.ResultsThe proportion of health facilities with Operation Room (OR) scheduling, referral, and surgical site infection registers significantly increased by 34.8%, 56.5% and 87%, respectively, at project endline compared to baseline. Availability of OR and Anesthesia registers remained high throughout the project, at 91.3% and 95.6%, respectively. Furthermore, the appropriate use of these registers improved, with statistically significant increases observed for OR scheduling registers (34.8% increase). Increases were also noted for OR register (9.5% increase) and anesthesia register (4.5% increase), although not statistically significant. Assessing the prior three months reports, the report submissions to the Ministry of Health/Regional Health Bureau (MOH/RHB) rose from 85 to 100%, reflecting complete reporting at endline period. Additionally, the proportion of surgical teams analyzing and displaying data for informed decision-making significantly increased from 30.4% at baseline to 60.8% at endline period.ConclusionThe implemented interventions positively impacted surgical data management and utilization practice at intervention facilities. These positive changes were likely attributable to capacity building trainings and regular mentorship visits via hub-and-spoke approach. Hence, we recommend further investigation into the effectiveness of similar intervention packages in improving surgical data management, data analysis and visualization practices in low- and middle-income country settings.

A machine learning approach to rank pricing problems in branch-and-price

This paper presents a novel approach exploiting machine learning to enhance the efficiency of the branch-and-price algorithm. The focus is, specifically, on problems characterized by multiple pricing problems. Pricing problems often constitute a substantial portion of CPU time due to their repetitive nature. The primary contribution of this work includes the introduction of a machine learning-based ranker that strategically guides the search for new columns in the column generation process. The master problem solution is analyzed by the ranker, which then suggests an order for solving the pricing problems to prioritize those with the potential to improve the master problem the most. This prioritization mechanism is essential in speeding up the column generation since, by identifying new columns early in the process, we can terminate the search procedure sooner. Furthermore, our technique exhibits applicability across all nodes of the branching tree, making it a valuable tool for solving a wide range of optimization problems. We demonstrate the usefulness of this approach in the challenging domain of operating room scheduling, an area that has seen limited exploration in the context of machine learning. Extensive experimental evaluations underline the effectiveness of the developed algorithm, consistently outperforming traditional search strategies in terms of time, number of solved pricing problems, searched nodes in the branching tree, and performed column generation iterations.

Artificial bee colony algorithm for operating room scheduling problem with dedicated/flexible resources and cooperative operations

Artificial bee colony algorithm for operating room scheduling problem with dedicated/flexible resources and cooperative operations

Surgery Duration Prediction Using Multi-Task Feature Selection.

Efficient optimization of operating room (OR) activity poses a significant challenge for hospital managers due to the complex and risky nature of the environment. The traditional "one size fits all" approach to OR scheduling is no longer practical, and personalized medicine is required to meet the diverse needs of patients, care providers, medical procedures, and system constraints within limited resources. This paper aims to introduce a scientific and practical tool for predicting surgery durations and improving OR performance for maximum benefit to patients and the hospital. Previous works used machine-learning models for surgery duration prediction based on preoperative data. The models consider covariates known to the medical staff at the time of scheduling the surgery. Given a large number of covariates, model selection becomes crucial, and the number of covariates used for prediction depends on the available sample size. Our proposed approach utilizes multi-task regression to select a common subset of predicting covariates for all tasks with the same sample size while allowing the model's coefficients to vary between them. A regression task can refer to a single surgeon or operation type or the interaction between them. By considering these diverse factors, our method provides an overall more accurate estimation of the surgery durations, and the selected covariates that enter the model may help to identify the resources required for a specific surgery. We found that when the regression tasks were surgeon-based or based on the pair of operation type and surgeon, our suggested approach outperformed the compared baseline suggested in a previous study. However, our approach failed to reach the baseline for an operation-type-based task. By accurately estimating surgery durations, hospital managers can provide care to a greater number of patients, optimize resource allocation and utilization, and reduce waste. This research contributes to the advancement of personalized medicine and provides a valuable tool for improving operational efficiency in the dynamic world of medicine.

Techniques for Improving Genetic Algorithms in Solving Operating Room Scheduling Problems: An Integrative Review

Operating room scheduling is a complex process that involves various resources and takes the interests of many parties into consideration. The genetic algorithm is the frequently used metaheuristic algorithm to solve a large-size operating room scheduling problem. Many techniques have been developed to improve the genetic algorithms' performance in dealing with the operating room scheduling complexity. In this paper, we survey available literature to identify improvement techniques used at each stage of the genetic algorithm and capture the underlying problems. This review provides a mapping of improvement techniques in genetic algorithms correlating with the considered problems. The results can be employed by other researchers as a guide for future research in integrating a genetic algorithm or other population-based metaheuristic algorithm with a recent heuristic algorithm following the future directions of operating room scheduling research.

Multi-objective stochastic scheduling of inpatient and outpatient surgeries

AbstractWith the advancement of surgery and anesthesiology in recent years, surgical clinical pathways have changed significantly, with an increase in outpatient surgeries. However, the surgical scheduling problem is particularly challenging when inpatients and outpatients share the same operating room blocks, due to their different characteristics in terms of variability and preferences. In this paper, we present a two-phase stochastic optimization approach that takes into account such characteristics, considering multiple objectives and dealing with uncertainty in surgery duration, arrival of emergency patients, and no-shows. Chance Constrained Integer Programming and Stochastic Mixed Integer Programming are used to deal with the advance scheduling and the allocation scheduling, respectively. Since Monte Carlo sampling is inefficient for solving the allocation scheduling problem for large size instances, a genetic algorithm is proposed for sequencing and timing procedures. Finally, a quantitative analysis is performed to analyze the trade-off between schedule robustness and average performance under the selection of different patient mixes, providing general insights for operating room scheduling when dealing with inpatients, outpatient, and emergencies.

Robust Operating Room Scheduling: A Two Stage Advanced Ant System

Robust Operating Room Scheduling: A Two Stage Advanced Ant System

Just “Culture”? A qualitative study on stressors impacting surgical clerkship student learning and well-being

PurposeTransition into the surgery clerkship is stressful and challenging for third-year medical students. We conducted this study to explore medical students’ learning stressors during the surgery clerkship which may diminish their learning and well-being. Findings will help to identify targets for future educational well-being interventions to enhance students’ preparedness to enter surgery clerkship.MethodsWe conducted semi-structured interviews with medical students who had completed their surgery clerkship in 2022 at a university-based hospital system using convenience sampling. The interviews were recorded, transcribed, and de-identified. Investigators iteratively coded and analyzed transcripts using a framework method for emerging themes until reaching data saturation.ResultsWe performed 17 interviews with medical students rotating on main campus and community sites from 2 consecutive clerkship cohorts. Each interview was approximately 30 min. Three dominant themes emerged regarding stressors that negatively influenced medical student learning and well-being: 1) expectation mismatch (e.g., student felt incompetent at driving a laparoscope and believed this was a skill she was expected to have), 2) exclusion from the team (e.g., perceiving that their questions are burdensome to the team, and 3) logistical challenges (e.g., where and when to arrive on the first day of clerkship or how to navigate the complexities of the operating room schedule).ConclusionOur study suggests three predominant stressor themes that may jeopardize medical student surgery clerkship learning and well-being. Interventions during clerkship orientation are recommended to establish expectations, clarify logistical confusion, and build a welcoming and supportive surgical culture to facilitate student integration within the medical team.

Anesthesia Practitioners' Goals for Sevoflurane Minimum Alveolar Concentration at the End of Surgery and the Incidence of Prolonged Extubations: A Prospective and Observational Study.

Prolonged times to tracheal extubation (≥15 minutes from dressing on the patient) are consequential based on their clinical and economic effect. We evaluated the variability among anesthesia practitioners in their goals for the age-adjusted end-tidal minimum alveolar concentration ofsevoflurane (MAC) at surgery end and achievement of their goals. We prospectively studied a cohort of 56 adult patients undergoing general anesthesia with sevoflurane as the sole anesthetic agent, scheduled operating room time of at least 3 hours, and non-prone positioning. At the start of surgical closure, an observer asked the anesthesia practitioner their goal for MAC when the surgical drapes are lowered (i.e., the functional end of surgery for the studied procedures). When the drapes were lowered, the MAC achieved was recorded, and the values were compared. The standard deviation of the practitioners' MAC goal was large, 0.199 (N = 56 cases, 95% confidence interval 0.17-0.24), not significantly different from the standard deviation of the MAC achieved of0.253, P= 0.071. The MAC goal and MAC achieved were correlated pairwise, Pearson r=0.65, P< 0.0001. There was no incremental effect of operating room conversation(s) related tocase progress on the association (partial correlation ‑0.01, P= 0.96). Differences among practitioners in the MAC achieved at surgery end were consequential. Specifically, for the N = 12cases with prolonged extubation, the mean MAC was 0.60 (standard deviation 0.10) versus 0.48 (0.21) among the N = 44 cases without prolonged extubation (P = 0.0070). The standard deviation ofthe MAC goal among practitioners was sufficiently large to contribute significantly tothe variability in the MAC achieved at the end of surgery. We confirmed prospectively that the age-adjusted end-tidal MAC at the end of surgery matters clinically and economically because differences of0.60 versus 0.48 were associated with more prolonged extubations. Our novel finding isthat the MAC achieved ≥0.60 werecaused in part by the anesthesia practitioners' stated MAC goals when surgical closures started.

A multi-objective planning and scheduling model for elective and emergency cases in the operating room under uncertainty

Hospitals are paramount hubs for delivering healthcare services, with their Operating Rooms (ORs) as a pivotal and financially substantial component. Efficient surgery ward planning is crucial in healthcare institutions, aiming to improve medical service quality while reducing costs. This research delves into the intricacies of integrated OR planning and scheduling, focusing on elective and emergency patients in an uncertain environment. To address these challenges, a mixed integer programming (MIP) framework is developed to minimize inactivity and patient wait times while optimizing high-priority resource allocation. Both upstream and downstream units of the ward, the Pre-operative Holding Unit (PHU), Post Anesthesia Care Unit (PACU), and Intensive Care Unit (ICU) are included. The inherently uncertain aspects of surgery, including surgical duration, Length of Stay (LOS), and the influx of emergency patients, demand an intelligent optimization approach. Consequently, a robust optimization strategy is harnessed to effectively grapple with this pervasive uncertainty. A deterministic model is introduced and improved using an enhanced epsilon constraint method. The culmination of this analytical journey yields a collection of Pareto-optimal solutions. Empirical results, supported by managerial insights, highlight the superiority of the proposed method over the traditional weighting approach.

Factors affecting operating room scheduling accuracy for primary and revision total hip arthroplasty: a retrospective study.

Optimizing operating room (OR) scheduling accuracy is important for OR efficiency, meeting patient expectations, and maximizing value for health systems. However, limited data exist on factors influencing the precision of Total Hip Arthroplasty (THA) OR scheduling. This study aims to identify the factors influencing the accuracy of OR scheduling for THA. A retrospective review of 6,072 THA (5,579 primary THA and 493 revision THA) performed between January 2020 and May 2023 at an urban, academic institution was conducted. We collected baseline patient characteristics, surgeon years of experience, and compared actual wheels in to wheels out (WIWO) OR time against scheduled OR time. Significant scheduling inaccuracies were defined as actual OR times deviating by at least 15% from scheduled OR times. Logistic regression analyses were employed to assess the impact of patient, surgeon, and intraoperative factors on OR scheduling accuracy. Using adjusted odds ratios, primary THA patients who had a lower BMI and surgeons who had less than 10 years of experience were associated with overestimation of OR time. Whereas, higher BMI, younger age, general anesthesia, non-primary osteoarthritis indications, and afternoon procedure start times were linked to underestimation of OR time. For revision THA, lower BMI and fewer components revised correlated with overestimated OR time. Men, higher BMI, more components revised, septic indication for surgery, and morning procedure start times were associated with underestimation of OR time. This study highlights several critical patient, surgeon, and intraoperative factors influencing OR scheduling accuracy for THA. OR scheduling models should consider these factors to enhance OR efficiency.

Predicting operative time for metabolic and bariatric surgery using machine learning models: a retrospective observational study.

Predicting operative time is essential for scheduling surgery and managing the operating room. This study aimed to develop machine learning (ML) models to predict the operative time for metabolic and bariatric surgery (MBS) and to compare each model. The authors used the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database between 2016 and 2020 to develop ML models, including linear regression, random forest, support vector machine, gradient-boosted tree, and XGBoost model. Patient characteristics and surgical features were included as variables in the model. The authors used the mean absolute error, root mean square error, and R 2 score to evaluate model performance. The authors identified the 10 most important variables in the best-performing model using the Shapley Additive exPlanations algorithm. In total, 668723 patients were included in the study. The XGBoost model outperformed the other ML models, with the lowest root mean square error and highest R 2 score. Random forest performed better than linear regression. The relative performance of the ML algorithms remained consistent across the models, regardless of the surgery type. The surgery type and surgical approach were the most important features to predict the operative time; specifically, sleeve gastrectomy (vs. Roux-en-Y gastric bypass) and the laparoscopic approach (vs. robotic-assisted approach) were associated with a shorter operative time. The XGBoost model best predicted the operative time for MBS among the ML models examined. Our findings can be useful in managing the operating room scheduling and in developing software tools to predict the operative times of MBS in clinical settings.

Correction: Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Correction: Innovative operating room scheduling metric for creating surgical lists with desirable room utilisation rates

Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room.

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.