UNASSIGNED: This study aimed to investigate the causes and prevalence of elective surgery cancellations in the operating room, and the clinical outcomes of affected patients.
UNASSIGNED: 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).
UNASSIGNED: Elective surgery cancellations occurred in the operating room at a rate of 0.9%. Group 1 was substantially older than Group 2 (p<0.001). Group 1 had a larger number of ASA III patients (p<0.001). The department with the highest cancellation rate was ophthalmology (2.5%), followed by general surgery (2.1%), urology (1.5%), and ear, nose, and throat (1.4%). It was possible to avoid 59.7% of cancelations.
UNASSIGNED: The study revealed a 0.9% prevalence rate of elective surgery cancelations in the operating room. Older age and higher ASA status greatly influenced these cancellations. Optimized surgery scheduling and patient assessment processes may prevent many of these cancellation.

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.
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.
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%.
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.

OBJECTIVE: Sigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.
METHODS: This retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC).
RESULTS: The Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22).
CONCLUSIONS: A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.

Resource coordination in surgical scheduling remains challenging in health care delivery systems. This is especially the case in highly-specialized settings such as coordinating Intraoperative Neurophysiologic Monitoring (IONM) resources. Inefficient coordination yields higher costs, limited access to care, and creates constraints to surgical quality and outcomes. To maximize utilization of IONM resources, optimization-based algorithms are proposed to effectively schedule IONM surgical cases and technologists and evaluate staffing needs. Data with 10 days of case volumes, their surgery durations, and technologist staffing was used to demonstrate method effectiveness. An iterative optimization-based model that determines both optimal surgery and technologist start time (operational scenario 4) was built in an Excel spreadsheet along with Excel\'s Solver settings. It was compared with current practice (operational scenario 1) and optimization solution on only surgery start time (operational scenario 2) or technologist start time (operational scenario 3). Comparisons are made with respect to technologist overtime and under-utilization time. The results conclude that scenario 4 significantly reduces overtime by 74% and under-utilization time by 86% as well as technologist needs by 10%. For practices that do not have flexibility to alter surgeon preference on surgery start time or IONM technologist staffing levels, both scenarios 2 and 3 also result in substantial reductions in technologist overtime and under-utilization. Moreover, IONM technologist staffing options are discussed to accommodate technologist preferences and set constraints for surgical case scheduling. All optimization-based approaches presented in this paper are able to improve utilization of IONM resources and ultimately improve the coordination and efficiency of highly-specialized resources.

Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration\'s uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today. If all the available information can be taken advantage of, then distributions individualized to every case can be constructed whose precision would support higher quality solutions in the presence of uncertainty. Our individualized stochastic optimization framework shows how the quantile regression forest (QRF) method predicts individualized distributions that are integrable into sample-average approximation, robust optimization, and distributionally robust optimization models for problems like surgery scheduling. In this paper, we present some related theoretical performance guarantees for each formulation. Numerically, we also study our approach\'s benefits relative to three other traditional models using data from Memorial Sloan Kettering Cancer Center in New York, NY, USA.

This paper offers a summary of the latest studies on healthcare scheduling problems including patients\' admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients\' admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

BACKGROUND: Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure.
OBJECTIVE: This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs.
METHODS: A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process.
RESULTS: Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well.
CONCLUSIONS: Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

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.

Providing timely access to surgery is crucial for patients with high acuity diseases like cancer. We present a methodological framework to make efficient use of scarce resources including surgeons, operating rooms, and clinic appointment slots with a goal of coordinating clinic and surgery appointments so that patients with different acuity levels can see a surgeon in the clinic and schedule their surgery within a maximum wait time target that is clinically safe for them. We propose six heuristic scheduling policies with two underlying ideas behind them: (1) proactively book a tentative surgery day along with the clinic appointment at the time an appointment request is received, and (2) intelligently space out clinic and surgery appointments such that if the patient does not need his/her surgery appointment there is sufficient time to offer it to another patient. A 2-stage stochastic discrete-event simulation approach is employed to evaluate the six scheduling policies. In the first stage of the simulation, the heuristic policies are compared in terms of the average operating room (OR) overtime per day. The second stage involves fine-tuning the most-effective policy. A case study of the division of colorectal surgery (CRS) at the Mayo Clinic confirms that all six policies outperform the current scheduling protocol by a large margin. Numerical results demonstrate that the final policy, which we refer to as Coordinated Appointment Scheduling Policy considering Indication and Resources (CASPIR), performs 52% better than the current scheduling policy in terms of the average OR overtime per day under the same access service level. In conclusion, surgical divisions desiring stratified patient urgency classes should consider using scheduling policies that take the surgical availability of surgeons, patients\' demographics and indication of disease into consideration when scheduling a clinic consultation appointment.