Operating Room Scheduling Research Articles

Can an Emergency Surgery Scheduling Software Improve Residents’ Time Management and Quality of Life?

Background: Operating room efficiency is invaluable. Particularly in public health systems, where resources are limited and patient loads are high, efficient systems underpin the continued delivery of high quality care. In addition to impacting patients, the implementation of efficient healthcare tools has the potential to improve staff quality of life. In the face of growing surgical resident attrition and healthcare worker burnout, developments in standard practice, such as the implementation of the 80-hour work week, are necessary to improve quality of life. Materials and methods: A new online scheduling software (ORNET.CA) was created, installed, and piloted in a Level I Trauma Center after instructing users (physicians and nurses) on its use. A 20-item survey was then distributed to all users to assess the effect implementation of the software had on their quality of life. Results: ORnet was shown to improve communication between hospital staff and physicians, reduce workflow interruptions, and improve the quality of the working environment. The survey showed that 60% of residents and 50% of attending staff believed that ORNET.CA improved their quality of life. Conclusions: We present data from a novel emergency operating room scheduling system that allowed surgical residents and attending physicians to better plan their on-call shifts. Staff (resident and physician) reported survey results suggest that implementation of this system resulted in an improved quality of life and a decrease in stress and anxiety levels.

Increasing On-Time Starts of Neurology Procedures in Interventional Radiology

The first scheduled surgical or diagnostic procedure of the day sets the tone for the entire daily operating room (OR) schedule. Delayed first procedures of the day result in subsequent delays, affecting productivity, staffing, and most importantly, patient care.Factors that impact the start of the first scheduled case, include late patient arrival, lack of staffing, and incomplete pertinent laboratory results. Subsequently, this may lead to cancelations and will directly affect the patient’s satisfaction with the process. Cases running in procedural rooms past their scheduled end times result in extra pay for staff, while canceled cases lead to lost revenue; these expenses are consequences of inefficiency. It is hard to determine the financial ramifications of late starts to an organization because overhead facility costs and staff salaries are multifactorial and specific to the setting. This quality improvement (QI) project evaluated the effectiveness of a change in shift start time for nurses as a solution to improving on-time starts by measuring first case on-time starts and the patient and radiology team satisfaction scores.

Benchmarking of Anesthesia and Surgical Control Times by Current Procedural Terminology (CPT®) Codes.

Over half of hospital revenue results from perioperative patient care, thus emphasizing the importance of efficient resource utilization within a hospital's suite of operating rooms (ORs).Predicting surgical case duration, including Anesthesia-controlled time (ACT) and Surgical-controlled time (SCT) has been significantly detailed throughout the literature as a means to help manage and predict OR scheduling. However, this information has previously been divided by surgical specialty, and only limited benchmarking data regarding ACT and SCT exists. We hypothesized that advancing the granularity of the ACT and SCT from surgical specialty to specific Current Procedural Terminology (CPT®) codes will produce data that is more accurate, less variable, and therefore more useful for OR schedule modeling and management.This single center study was conducted using times from surgeries performed at the University of Colorado Hospital (UCH) between September 2018 - September 2019. Individual cases were categorized by surgical specialty based on the specialty of the primary attending surgeon and CPT codes were compiled from billing data. Times were calculated as defined by the American Association of Clinical Directors. I2 values were calculated to assess heterogeneity of mean ACT and SCT times while Levene's test was utilized to assess heterogeneity of ACT and SCT variances. Statistical analyses for both ACT and SCT were calculated using JMP Statistical Discovery Software from SAS (Cary, NC) and R v3.6.3 (Vienna, Austria).All surgical cases (n = 87,537) performed at UCH from September 2018 to September 2019 were evaluated and 30,091 cases were included in the final analysis. All surgical subspecialties, with the exception of Podiatry, showed significant variability in ACT and SCT values between CPT codes within each surgical specialty. Furthermore, the variances of ACT and SCT values were also highly variable between CPT codes within each surgical specialty. Finally, benchmarking values of mean ACT and SCT with corresponding standard deviations are provided.Because each mean ACT and SCT value varies significantly between different CPT codes within a surgical specialty, using this granularity of data will likely enable improved accuracy in surgical schedule modeling compared to using mean ACT and SCT values for each surgical specialty as a whole. Furthermore, because there was significant variability of ACT and SCT variances between CPT codes, incorporating variance into surgical schedule modeling may also improve accuracy. Future investigations should include real-time simulations, logistical modeling, and labor utilization analyses as well asvalidation of benchmarking times in private practice settings.

An Optimization Model and Decision Support System of Operating Room Scheduling in a Teaching Hospital

An Optimization Model and Decision Support System of Operating Room Scheduling in a Teaching Hospital

Two-stage no-wait hybrid flow shop with inter-stage flexibility for operating room scheduling

Operating rooms are amongst the most critical resources in hospitals. Appropriate schedules of surgical interventions increase the surgeries’ success rates. Indeed, surgery outcomes strongly depend on the timing of each step in the surgery process. Therefore, effective and efficient surgery schedules can ease patients’ suffering and even save their lives while making good use of limited hospital resources. This paper studies the two-stage no-wait hybrid flow shop scheduling problem with inter-stage flexibility. The problem is inspired from hospital operating room scheduling under limited healthcare resources. We propose a time-indexed mixed integer linear programming formulation of the problem. We also introduce valid inequalities along with four lower bounds and four heuristics to handle the large scale of the problem. The proposed model is tested on randomly generated instances based on realistic data for operating room scheduling. Experimental results on the performance of the model and comparisons among the lower bounds and heuristics are reported for the different sizes of instance classes.

Elective surgery growth at Florida hospitals accrues mostly from surgeons averaging 2 or fewer cases per week: A retrospective cohort study

Study objectiveHospital administrators often seek to increase operating room (OR) elective caseload. Previous studies from Iowa demonstrated that surgical growth is mostly from low-caseload surgeons (ie, ≤2 cases per week). We repeated that study using data from Florida, a much more populous state, to confirm the generalizability of the findings. DesignRetrospective cohort study. SettingAll hospitals in the state of Florida. PatientsAll patients undergoing elective surgery during 2018 and 2019. MeasurementsWe determined growth between 2018 and 2019 in regular-workday elective surgical caseload and intraoperative work relative value units (wRVU) at hospitals. Using the two-sided, one group Student t-test, we compared the fractions of those increases attributable to low-caseload surgeons vs. 50% to assess if they accounted for most surgical growth. We used the exact binomial test to compare the fraction of hospitals where most growth (>50%) occurred from low-caseload surgeons to half (50%). Main resultsWe studied the 1,629,879 elective cases from 202 hospitals. Surgeons averaging ≤2.0 cases per week accounted for 73.3% (P < 0.0001 compared to 50%) of caseload growth and 68.7% (P < 0.0001 compared to 50%) of wRVU growth. The corresponding overall pooled growth estimates among hospitals were 70.8% for caseload and 65.0% for wRVU. There were 76.2% of the N = 202 hospitals with more than half their growth in cases from surgeons performing, on average, ≤2.0 cases per week (P < 0.0001 compared to 50% of hospitals). The vast majority of surgical growth at hospitals accrued from the contributions of low-caseload surgeons. ConclusionsSurgical growth in elective surgery at Florida hospitals accrued mostly from the increased activity of low-caseload surgeons averaging ≤2.0 cases per week during the preceding year, confirming the generalizability of the previous Iowa study. If growth in caseload is desired, surgical governance committees should ensure that low-caseload surgeons have access to the OR schedule.

Hibrit Genetik Algoritma Kullanarak Ameliyat Odası Çizelgeleme

Hospitals are among the most important institutions of today. For hospitals, efficient use of operating rooms is of great importance. Efficient use of operating rooms is a problem that needs to be solved. The operating room scheduling problem is a very complex problem with large number of constraints. This type of problem called as NP-Hard type problem. NP-Hard type problems do not consist of polynomial values. Therefore, the solution of these problems is very complex and difficult. Solutions consisting of polynomial values can be solved effectively with existing mathematical methods. However, more effective algorithms were needed to solve NP-hard type problems. As a result of the studies, many heuristic, meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Taboo Search Algorithm have been developed to solve the complexity of NP-Hard problems. In this article, the operating room scheduling problem solved with a hybrid genetic algorithm. In this solution, it shows how the algorithm affects the solution area in the changes in the number of surgeons, operating rooms and operating room reservations, which are among the operating room parameters. In the developed software, C# programming language has been preferred in order to provide comfortable use of the end user.

Similarities Between Pediatric and General Hospitals Based on Fundamental Attributes of Surgery Including Cases Per Surgeon Per Workday.

IntroductionOperating room (OR) management decision-making at both pediatric and adult hospitals is determined, in large part, by the same fundamental attributes of surgery and other considerations related to case duration prediction. These include the non-preemptive nature of surgeries, wide prediction limits for case duration, and constraints to moving or resequencing cases on the day of surgery. Another attribute fundamentally affecting OR management is the median number of cases a surgeon performs on their OR days. Most adult surgeons have short lists of cases (i.e., one or two cases per day). Similarly, at adult hospitals, growth in caseloads is mostly due to the subset of those surgeons who also operate just once or twice per week. It is unknown if these characteristics of surgery apply to pediatric surgeons and pediatric hospitals as well.MethodsOur retrospective cohort study included all elective surgical cases performed at the six pediatric hospitals in Florida during 2018 and 2019 (n = 71,340 cases). We calculated the percentages of combinations of surgeon, date, and hospital (lists) comprising one or two cases, or just one case, and determined if the values were statistically >50% (i.e., indicative of “most”). We determined if most of the growth in caseload and intraoperative work relative value units (wRVUs) at the pediatric hospitals between 2018 and 2019 accrued from low-caseload surgeons. Results are reported as mean ± standard error of the mean.ResultsAveraging among the six pediatric hospitals, the non-holiday weekday lists of most surgeons at each facility had just one or two elective cases, inpatient and/or ambulatory (68.1%; p = 0.016 vs. 50%, n = 27,557 lists). Growth in surgical caseloads from 2018 to 2019 was mostly attributable to surgeons who in 2018 averaged ≤2.0 cases per week (76.3% ± 5.4%, p = 0.0085 vs. 50%). Similarly, growth in wRVUs was mostly attributable to these low-caseload surgeons (73.8% ± 5.4%, p = 0.017 vs. 50%).ConclusionsLike adult hospitals, most pediatric surgeons’ lists of cases consist of only one or two cases per day, with many lists containing a single case. Similarly, growth at pediatric hospitals accrued from low-caseload surgeons who performed one or two cases per week in the preceding year. These findings indicate that hospitals desiring to increase their surgical caseload should ensure that low-caseload surgeons are provided access to the OR schedule. Additionally, since percent-adjusted utilization and raw utilization cannot be accurately measured for low-caseload surgeons, neither metric should be used to allocate OR time to individual surgeons. Since most adult and pediatric surgeons have low caseloads, this is a fundamental attribute of surgery.

The effect of few historical data on the performance of sample average approximation method for operating room scheduling

AbstractWe model the scheduling problem of a single operating room for outpatient surgery, with uncertain case durations and an objective function comprising waiting time, idle time, and overtime costs. This stochastic scheduling problem has been studied in diverse forms. One of the most common approaches used is the sample average approximation (SAA). Our contribution is to study the use of SAA to solve this problem under few historical data using families of log t distributions with varying degrees of freedom. We analyze the results of the SAA method in terms of optimality convergence, the effect of the number of scenarios, and average computational time. Given the case sequence, computational results demonstrate that SAA with an adequate number of scenarios performs close to the exact method. For example, we find that the optimality gap, in units of proportional weighted time, is relatively small when 500 scenarios are used: 99% of the instances have an optimality gap of less than 2.6 7% (1.74%, 1.23%) when there are 3 (9, many) historical samples. Increasing the number of SAA scenarios improves performance, but is not critical when the case sequence is given. However, choosing the number of SAA scenarios becomes critical when the same method is used to choose among sequencing heuristics when there are few historical data. For example, when there are only three (nine, many) historical samples, 99% of the instances have less than 25.38% (13.15%, 6.87%) penalty in using SAA with 500 scenarios to choose the best sequencing heuristic.

Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms

Today, planning and scheduling problems are the most significant issues in the world and make a great impact on improving organizational productivity and serving systems such as medical and healthcare providers. Since operating room planning is a major problem in healthcare organizations, the optimization of medical staff and equipment plays an essential role. Thus, this study presents a multi-objective mathematical model with a new categorization (preoperative, intraoperative, and postoperative) to minimize operating room scheduling and the risk of using equipment. Time constraints in healthcare systems and medical equipment limited capacity are the most significant considered limitation in the present study. In this regard, since the duration of patient preparation and implementation of treatment processes occur in three states of optimistic, pessimistic, and normal, the introduced parameters are examined relying on a fuzzy uncertainty analysis of the problem. Hence, the model is measured in a real numerical solution sample in a medical center to evaluate and confirm the proposed mathematical model. Then, two meta-heuristic algorithms (NRGA and NSGAII) are implemented on the mathematical model to analyze the proposed model. Finally, the research results indicate that the NSGA-II is more efficient in the operating room scheduling problem.

Daily Operating Room Schedule Taking into Account the Preferences of Hospital Physicians (Case Study of Imam Khomeini Hospital)

The issue of allocating the capacity of operating rooms to different departments of surgery is done by observing the decisions made at the strategic level of hospital decisions. Therefore, operating room scheduling plays an important role in increasing their productivity and reducing hospital costs. On the other hand, due to various uncertainties in operating room activities, this issue can be very challenging. In this case, according to the defined objective function and also the existing limitations, the capacity of operating rooms is allocated to different surgical groups. Therefore, in this model, the daily schedule of surgeries in two stages of surgery and recovery and considering all the real limitations in hospitals has been investigated. This issue determines the sequence, start time of each surgery and allocation of resources required in each stage with the aim of minimizing the total cost of employing surgeons according to the differences in their skills, performance and preferences.

Technology for improved operating room scheduling - a case of Kilimanjaro Christian Medical Center of Tanzania

Optimal scheduling of surgeries can result in efficient utilisation of the limited available operating rooms. Considering that operation theatres are an expensive investment, the optimal usage of these facilities will ensure that the dividend from the investment on the facilities is maximised. The integration of proper technology in the scheduling process can aid the creation of optimal schedules of patients who want to undergo surgeries leading to minimisation of patient waiting times under conditions of limited available operating facilities and specialised equipment. This work seeks to model the scheduling of surgeries at Kilimanjaro Christian Medical Center, a referral hospital located on the northern corridor of the Republic of Tanzania. We model the operation room scheduling problem as an integer linear programming problem. The model is solved using Torsche toolbox with the help of MATLAB routines and functions by considering appropriate configurations like resources, task parameters and optimisation criterion.

Operating room scheduling 2019 survey

Numerous optimisation problems in healthcare have been approached by researchers over the last three to four decades. Hospital logistics - organised and structured to secure patient satisfaction in...

Operating room scheduling 2019 survey

Operating room scheduling 2019 survey

Framework for a retrospective analysis of operating room schedule execution

Framework for a retrospective analysis of operating room schedule execution

A prediction-optimization approach to surgery prioritization in operating room scheduling

ABSTRACT This study proposes a mixed-integer programming model to optimize daily schedules based on surgery priority. Stacking ensemble learning is employed to predict surgery priority. The stacking algorithm is composed of K-nearest neighbor, multi-nominal logistic regression, decision tree, multi-layer perceptron, and ensemble learning. Then, the predicted priorities are fed into an optimization model. Six patient-related variables are used to predict surgery priority: surgery type, patient acuity, patient age, number of delayed days a surgery is postponed, patient age, and surgery time. The study contribution comes from integrating machine learning and optimization to propose a priority-based decision model for optimally sequencing surgeries daily. The experimental results show that the proposed approach is better than the current practice in handling unscheduled surgeries, while the scheduling cost remains nearly unchanged. We show the effectiveness of the proposed approach for handling the surgery cancellation problem in operating room systems with high surgery demands.

Collaborative scheduling of operating room in hospital network: Multi-objective learning variable neighborhood search

In this study, the operating room scheduling of hospital networks with virtual alliance has been studied, which at the same time, there is a kind of cooperation and competition among the agents. The main feature in networks with the virtual alliance is the possibility of different objective functions among the agents, which has priority for agents compared to the network’s overall objective. Here, by considering the conditions of emergency arrival, the time of inter-hospital transportation, and the elective patients and non-elective patients in the scheduling, an attempt has been made to bring the problem closer to real-world situations. To solve this problem, first, a mixed-integer mathematical programming model is proposed. Because of its NP-hardness, then, a multi-objective learning variable neighborhood search algorithm is designed to minimize total completion of surgeries, the cost of allocating the patient to the hospital and the surgeon, and the cost of overtime operating rooms throughout the network. Finally, the performance of the proposed algorithm is evaluated in comparison with the NSGA-II and memetic-based algorithm, which due to considering the learning mechanism along with the use of various neighborhood structures in the proposed algorithm, its results are promising. It is expected that by using the proposed algorithms in a cooperative structure, the hospitals are able to achieve optimal/near-optimal solutions in a reasonable time, in which, in addition to more economic activity, patients also benefit due to better use of resources.

Artificial intelligence in thoracic surgery: a narrative review.

ObjectiveThe aim of this article is to review the current applications of artificial intelligence in thoracic surgery, from diagnosis and pulmonary disease management, to preoperative risk-assessment, surgical planning, and outcomes prediction.BackgroundArtificial intelligence implementation in healthcare settings is rapidly growing, though its widespread use in clinical practice is still limited. The employment of machine learning algorithms in thoracic surgery is wide-ranging, including all steps of the clinical pathway.MethodsWe performed a narrative review of the literature on Scopus, PubMed and Cochrane databases, including all the relevant studies published in the last ten years, until March 2021.ConclusionMachine learning methods are promising encouraging results throughout the key issues of thoracic surgery, both clinical, organizational, and educational. Artificial intelligence-based technologies showed remarkable efficacy to improve the perioperative evaluation of the patient, to assist the decision-making process, to enhance the surgical performance, and to optimize the operating room scheduling. Still, some concern remains about data supply, protection, and transparency, thus further studies and specific consensus guidelines are needed to validate these technologies for daily common practice.KeywordsArtificial intelligence (AI); thoracic surgery; machine learning; lung resection; perioperative medicine

Improving operating room efficiency using lean management tools

Introducing Lean management (LM) enables the surgical process to reach considerably high levels of performance and efficiency, in addition to increased patient safety and satisfaction among healthcare professionals. This contribution presents a consolidated model of LM which has proved highly successful in improving both overall efficiency in operation rooms and patients’ perceived value of efficient hospital care. This model is based on a real case study carried out over a 3-year period. A surgical process improvement team was set up, which then designed a Process Value Flow Map using LM tools, and detected opportunities for improvement. The map reflects the patient’s journey through the process, the flow of information and materials for optimising processes. Findings from the three improvement projects described in the paper were used to (1) enhance waiting list management, (2) improve operating room scheduling criteria and sharing of surgical resources, and (3) create continuous improvement teams. The results of the study are twofold: (1) reduced waiting times for operations and (2) increased efficiency of operating rooms stemming from a cultural change in surgical resources management, particularly activity planning. A notable increase in surgical efficiency indicators was also observed, as well as the need to incorporate Lean principles into surgical intervention programming.

Letter: Home-Turf Advantage? The Neurosurgery Residency Match During the COVID-19 Pandemic.

Letter: Home-Turf Advantage? The Neurosurgery Residency Match During the COVID-19 Pandemic.