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Objective: This study aimed to investigate the causes and prevalence of elective surgery cancellations in the operating room, and the clinical outcomes of affected patients. Methods: This prospective, cross-sectional study assessed the prevalence and causes of elective surgery cancellations once patients are in the operating room. A tertiary academic referral center hosted the study between January 2022 and January 2023. The study sample consisted of 7,482 adult patients scheduled for elective surgeries and taken to the operating room. The 7,415 completed procedures were in Group 2, whereas the 67 cancelled surgeries were in Group 1. Patients were divided into two groups on the basis of whether their surgeries were completed or cancelled. Factors such as age, American Society of Anesthesiologists (ASA) status, and surgical department were analyzed. The two groups were compared on the basis of age, ASA status, surgical department, and surgery time (month and day). Results: Elective surgery cancellations occurred in the operating room at a rate of 0.9%. Group 1 was substantially older than Group 2 (p

Objective: For patient safety, maintaining hemodynamic stability during surgical procedures is critical. Dynamic indices [such as systolic pressure variation (SPV) and pulse pressure variation (PPV)], have recently seen an increase in use. Given the risks associated with such invasive techniques, there is growing interest in non-invasive monitoring methods-and plethysmographic waveform analysis. However, many such non-invasive methods involve intricate calculations or brand-specific monitors. This study introduces the simple systolic ratio (SSR), derived from pulse oximetry tracings, as a non-invasive method to assess fluid responsiveness. Methods: This prospective observational study included 25 adult patients whose SPV, PPV, and SSR values were collected at 30-min intervals during open abdominal surgery. The SSR was defined as the ratio of the tallest waveform to the shortest waveform within pulse tracings. The correlations among SSR, SPV, and PPV were analyzed. Additionally, anaesthesia specialists visually assessed pulse oximetry tracings to determine fluid responsiveness using the SSR method. Results: Strong correlations were observed between SSR and both SPV (r = 0.715, P < 0.001) and PPV (r = 0.702, P < 0.001). Receiver operator curve analysis identified optimal SSR thresholds for predicting fluid responsiveness at 1.47 for SPV and 1.50 for PPV. A survey of anaesthesia specialists using the SSR method to visually assess fluid responsiveness produced an accuracy rate of 83%. Conclusion: Based on the strong correlations it exhibits with traditional markers, SSR has great potential as a clinical tool, especially in resource-limited settings. However, further research is needed to establish its role, especially as it pertains to its universal applicability across monitoring devices.

This paper seeks to model and solve a surgery scheduling problem in hospitals subject to a constraint that surgeries can only be started at or after the scheduled start time, and to evaluate the effects of proposed changes to improve operating room (OR) efficiency and utilization using a Monte Carlo simulation approach. The surgery scheduling problem is to assign surgeries to ORs and assign surgery start times so as to minimize the sum of idle times of ORs and late start times for surgeries. Employing historical data provided by a large university hospital, this research provides a mathematical formulation for the problem, proposes a series of heuristics, and develops a Monte Carlo simulation model for planned surgeries to minimize the penalty. Our research demonstrates that the changes proposed can shorten the delayed start duration for surgeries and the idle times for ORs. The results can be helpful to the decision makers in hospital settings who are considering minimizing delays in surgeries start times and ORs idle times while dealing with the stochastic nature of surgery times.

Abstract Background Surgical theater (ST) operations planning is a key subject in the healthcare management literature, particularly the scheduling of procedures in operating rooms (ORs). The OR scheduling problem is usually approached using mathematical modeling and made available to ST managers through dedicated software. Regardless of the large body of knowledge on the subject, OR scheduling models rarely consider the integration of OR downstream and upstream facilities and resources or validate their propositions in real life, rather using simulated scenarios. We propose a heuristic to sequence surgeries that considers both upstream and downstream resources required to perform them, such as surgical kits, post anesthesia care unit (PACU) beds, and surgical teams (surgeons, nurses and anesthetists). Methods Using hybrid flow shop (HFS) techniques and the break-in-moment (BIM) concept, the goal is to find a sequence that maximizes the number of procedures assigned to the ORs while minimizing the variance of intervals between surgeries’ completions, smoothing the demand for downstream resources such as PACU beds and OR sanitizing teams. There are five steps to the proposed heuristic: listing of priorities, local scheduling, global scheduling, feasibility check and identification of best scheduling. Results Our propositions were validated in a high complexity tertiary University hospital in two ways: first, applying the heuristic to historical data from five typical ST days and comparing the performance of our proposed sequences to the ones actually implemented; second, pilot testing the heuristic during ten days in the ORs, allowing a full rotation of surgical specialties. Results displayed an average increase of 37.2% in OR occupancy, allowing an average increase of 4.5 in the number of surgeries performed daily, and reducing the variance of intervals between surgeries’ completions by 55.5%. A more uniform distribution of patients’ arrivals at the PACU was also observed. Conclusions Our proposed heuristic is particularly useful to plan the operation of STs in which resources are constrained, a situation that is common in hospital from developing countries. Our propositions were validated through a pilot implementation in a large hospital, contributing to the scarce literature on actual OR scheduling implementation.

The high costs of operating rooms (ORs) make surgery scheduling decisions critical for hospitals, which refer to resource allocations and determining the start time of each surgery. We take into account an important issue that could affect surgery scheduling decisions, ie., assistant surgeons. The operating room scheduling problem is considered as a two-stage problem. The first stage refers to resource allocations, including allocating surgeries into ORs and assigning assistant surgeons to surgeries, while the start time of each surgery is determined in the second stage. The robustness of the model is considered in a tractable way. We develop a bound-based algorithm to solve the model with the objective of minimising the total cost in the two stages. Numerical studies show the good performances of the proposed algorithm. Sensitivity analysis is conducted to examine the effects of the key parameters. An easy-to-implement policy for decisions in the first stage is inspired by the results of the experiments.

Background: According to the importance of health and treatment, it is necessary to use suitable models for planning and setting surgery time. In this study, a mathematical model is offered for operational scheduling of surgeries at surgery rooms of hospitals. Material and Methods: This is an applied study and its data is related to the surgery rooms of Zahedan Al-Zahra eye hospital. Study population was the surgeries performed in March 2016. The mathematical model of scheduling surgeries at the surgery room was optimized in MATLAB2014. Results: Due to limitations on patient admission capacity in hospital surgery ward, 79 surgeries were called off. In the proposed model, the total waiting time index for performing surgeries was 1547.29, and this index was found to be 1842 without the use of the model. Therefore, the waiting time index was improved by 16%. In accordance with the third purpose (objective) function, the tally of delays for predicted surgery ending time in one month was estimated to be 69.15 hours. The process of each surgery includes four defined activities. The end time of the activities related to each surgery has been examined and it has been optimized according to the existing limitations. Conclusion: The proposed model can improve the waiting time by 16% and makes it possible to choose the surgical procedures that should be canceled and delayed according to medical priorities.

The stochastic high-patient-throughput surgery scheduling problem under a limited number of staffed ward beds is addressed in this paper. This work proposes a novel way to minimize the risk of last-minute cancellations by bounding the likelihood of exceeding the staffed ward beds. Given historical data, it is possible to determine an empirical distribution for the length of stay in the ward. Then, for any given combinations of patients, one can estimate the likelihood of exceeding the number of staffed ward beds using Monte Carlo sampling. As these ward patient combinations grow exponentially, an alternative, more efficient, worst-case robust ward optimization model is compared. An extensive data set was collected from the National University Hospital of Iceland for computational experiments, and the models were compared with actual scheduling data. The models proposed achieve high quality solutions in terms of overtime and risk of overflow in the ward.

This paper addresses the operating room management problem for private hospitals aimed at the high-end customer market. A patient preference-driven policy that incorporates surgeon scheduling into the surgery scheduling process is proposed to satisfy patients' personalised preferences for surgeons and surgery dates. A stochastic programming model is formulated with the objective of minimizing operating rooms' staffing costs. A column generation-based heuristic algorithm is developed to solve the integrated surgeon and surgery scheduling issue. The performance of the algorithm is tested on different scale instances. Numerical results indicate that the patient-preference driven approach can identify an effective solution to operating room scheduling that satisfies patients' preferences with only slightly increased staffing costs. Additionally, the column generation-based heuristic algorithm can obtain solutions within a 1.6% gap of the lower bound obtained by the linear relaxed problem.

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named “genetic algorithm for scheduling optimization” (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use.

The operation room (OR) is one of the most expensive material resources in hospitals. Additionally, the demand for surgical service is increasing due to the aging population, while the number of surgical interventions performed is stagnated because of budget reasons. In this context, the importance of improving the efficiency of the surgical service is accentuated. The main objective of this work is to propose and to evaluate a Decision Support System (DSS) for helping medical staff in the automatic scheduling of elective patients, improving the efficiency of medical teams’ work. First, the scheduling criteria are fixed and then the scheduling problem of elective patients is approached by a mathematical programming model. A heuristic algorithm is proposed and included in the DSS. Moreover, other different features are implemented in a software tool with a friendly user interface, called CIPLAN. Considering realistic data, a simulation comparison of the scheduling obtained using the approach presented in this paper and other similar approaches in the bibliography is shown and analyzed. On the other hand, a case study considering real data provided by the Orthopedic Surgical Department (OSD) of the “Lozano Blesa” hospital in Zaragoza (HCU) is proposed. The simulation results show that the approach presented here obtains similar occupation rates and similar confidence levels of not exceeding the available time than approaches in the bibliography. However, from the point of view of respecting the order of the patients in the waiting list, the approach in this paper obtains scheduling much more ordered. In the case of the Orthopedic Surgical Department of the “Lozano Blesa” hospital in Zaragoza, the occupation rate may be increased by 2.83%, which represents a saving of 110,000 euros per year. Moreover, medical doctors (who use this tool) consider CIPLAN as an intuitive, rapid and efficient software solution that can make easier the corresponding task.