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This article presents a new architecture for the surgery scheduling process in hospitals, integrating Artificial Intelligence (AI) and Business Analytics (BA) techniques to optimise information flows and decision-making. The proposed platform has been designed as a modular architecture to monitor key performance indicators such as the number of surgeries scheduled, the percentage of shift occupancy, surgeries scheduled outside of deadlines and the total penalty. The AI algorithms automate the flow of information between surgeries on hold and scheduling dates, but also allow manual adjustments by healthcare professionals, guaranteeing total autonomy in user decision-making throughout the surgical scheduling process. The main aim is to reduce scheduling delays and patients on the waiting list, optimise the use of existing resources and minimise the financial penalties that are increasing every year. Through exhaustive analysis and a rigorous development process, the platform ensures a balance between the automation of processes and the indispensable analysis and human clinical intervention. [ABSTRACT FROM AUTHOR]

Complexity of surgery scheduling reduces surgical staff efficiency and patient satisfaction. Nurses and surgeons face extreme workload every day, whose schedules can be improved by correct scheduling of surgeries. This paper addresses integrated scheduling of operating rooms, nurses, and surgeons aiming to minimize makespan and maximize patient satisfaction and surgical teams' affinity. A constraint programming model and a hybrid method of NSGA-II and multi-objective dragonfly algorithm (MODA) are developed. Results indicate that high-quality solutions for problem instances with up to 150 surgeries are obtained by the CP model in less than 500 seconds. Superiority of the proposed meta-heuristic method compared to NSGA-II, MODA, SPEA-2, and a hybrid of NSGA-II and SPEA-2 is also shown. [ABSTRACT FROM AUTHOR]

The rapid population growth has resulted in ever-growing demand for healthcare services, which forces managers to use costly resources such as operating rooms effectively. The surgery-scheduling problem is a general title that is attributed to such problems as patient selection and sequencing of surgeries at the operational level, setting their start times, and assigning resources. Hospital managers usually encounter elective surgeries that can be delayed slightly and emergency surgeries whose arrivals are unexpected, and most of them need quick access to operating rooms. Reserving operating room capacity for handling incoming emergency surgeries is expensive. Moreover, emergency surgeries cannot afford long waiting times. This paper deals with the problem of surgery scheduling in the presence of emergency surgeries with a focus on balancing the efficient use of operating room capacity and responsiveness to emergency surgeries. We proposed a new algorithm for surgery scheduling with a specific operating room capacity planning and analyzed it using simulation method based on real data. This algorithm respects working hours and availability of staff and other resources in a surgical suite. [ABSTRACT FROM AUTHOR]

• Addresses surgery scheduling problems with considering setup time of surgical resources. • An improved artificial bee colony algorithm with combining Q-learning is proposed, named QABC. • Six local search operators are proposed according to the problem-specific knowledge. With the increasing demand for surgeries, surgery scheduling become an important problem in hospital management. Efficient surgery scheduling can enhance the optimal use of surgical resources, leading to high efficiency of surgery assignments. This work addresses surgery scheduling problems with surgical resources setup time. A mathematical model is established to describe the considered problems with the objective of minimizing the maximum completion time of the surgeries (makespan). Second, a modified artificial bee colony (ABC) algorithm is proposed, named QABC. Six local search operators are developed based on the characteristics of the problem, aiming to strengthen the local search capability of the algorithm. To further improve the performance of the algorithm, this study combines a Q-learning strategy with ABC algorithm. During each iteration of the algorithm, the Q-learning strategy is employed to guide the selection of search operators. Finally, the effectiveness of the local search operators and Q-learning based local search selection is verified by solving 20 cases with varying scales. And the results obtained by the Gurobi solver are compared with the proposed QABC. Furthermore, the proposed QABC is compared with the state-of-the-art algorithms. The experimental results and comparisons show that QABC is more effective than its peers for solving the surgery scheduling problems with setup time. [ABSTRACT FROM AUTHOR]

This paper analyzes the features of emergency surgeries under urban emergencies, and establishes a model for batch surgery scheduling, taking the issue as the flow-shop scheduling problem (FSP) with parallel machines. The modelling also takes account of the fatigue of doctors after long-term continuous operations, which causes the aging of the operating time and the success rate of saving lives. Then, the improved moth-flame optimization (IMFO) was adopted to solve the established emergency surgery scheduling model. Finally, the proposed model and algorithm were verified through an actual case. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Improving operating room efficiency has become one of the most important goals for health care providers given growing expenditures in managing hospital operations. Effective appointment schedules, which minimize the cost of patient waiting time and operating room idle time and overtime, play an important role in terms of improving efficiency. The variability inherent in surgical procedure times and random arrival of urgent cases significantly complicates the scheduling process. In this study, we compare several strategies for scheduling outpatient surgical procedures in the presence of urgent arrivals. First, a simulation optimization approach is used to find heuristic solutions for a surgery scheduling problem with a heterogeneous set of elective and urgent procedures in a multiple operating room setting. Second, a discrete-event simulation model is used to numerically evaluate how different sequencing and allocation policies perform for multiple surgery procedure types. Historical surgery procedure data for a hospital providing day surgeries over a two-year period from the Canadian Institute for Health Information (CIHI) database will be used to provide empirical support for the input parameters of the model. [ABSTRACT FROM AUTHOR]

In hospitals, surgeries are treated either on an outpatient or on an inpatient basis. Outpatients are normally routine patients that enter and leave the hospital on the same day, while inpatients who need more complex surgeries have to stay overnight. More recently, a shift from inpatient surgery to outpatient surgery is occurring due to scientific progress in anaesthesia and surgical techniques. Identifying possible similarities and differences between outpatient surgery scheduling and inpatient surgery scheduling can serve as a valuable decision-making foundation for practitioners and for operations researchers to efficiently schedule patients for surgery in the surgical department. This paper provides the first literature review on comparing outpatient surgery scheduling with inpatient surgery scheduling. The literature published between 2000 and 2020 that explicitly mentions either scheduling setting is included and it is analyzed from three dimensions, i.e., the uncertainty incorporation, the research methodology, and a scheduling performance comparison between both settings. We find that outpatient surgery can observe better results in many of the performance measures (i.e., operating room utilization, overtime, and patient cancellation rate) as opposed to inpatient surgery. This is due to the fact that inpatient surgery duration is longer and more variable and to the presence of more emergency patients, although there is a higher likelihood of no-shows for outpatients. Moreover, we identify future research directions that provide opportunities for expanding existing methodologies and especially for narrowing the gap between theory and practice. • A review is performed on comparing outpatient with inpatient surgery scheduling. • 178 papers published between 2000 and 2020 are identified and analyzed. • The uncertainty, research methodology, and performance measures are discussed. • Ideas for future work are suggested to narrow the gap between theory and practice. [ABSTRACT FROM AUTHOR]

Early or tardy surgeries are frustrating for both patients and personnel, and cause inefficient use of resources at the operating rooms. The stochastic Earliness/Tardiness (E/T) scheduling problem addresses this by minimizing the total expected deviation of the surgery completion times from the planned completion times. We introduce the concept of E/T-concavity as a property of a probability distribution if the E/T costs are concave as a function of the standard deviation of the completion time, whenever the optimal planned completion times are selected. We use this concept to generate an optimal schedule for the multiple machine variant of the E/T problem. The optimal schedule is not unique and therefore allows us to consider several optimization objectives in addition to the E/T objective. We demonstrate the usefulness of our results in practice by proving E/T-concavity for several probability distributions and by showing that, under the assumption of E/T-concavity, a simple Shortest Variance First (SVF) rule is optimal. We conclude by providing a numerical example of surgery scheduling where we demonstrate the benefits of the SVF rule compared to several commonly used scheduling rules. [ABSTRACT FROM AUTHOR]