Solving Operating Room Scheduling Problems with Surgical Teams via Answer Set Programming

The optimization of daily operating room surgery schedule can be problematic because of many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of surgical teams for complete surgical procedures. Recently, Answer Set Programming (ASP) has been successfully employed for addressing and solving real-life scheduling and planning problems in the healthcare domain. In this paper we present an enhanced solution using ASP for scheduling operating rooms taking explicitly into consideration availability of surgical teams, that include a surgeon and an anesthetist in different specialties for the entire duration of the surgery. We tested our solution on different benchmarks with realistic parameters for schedule’s length up to the target 5-days planning. The results of our experiments show that ASP is a suitable methodology for solving also such enhanced problem

How can patient journey in surgical wards of a referral hospital be improved?

Background: We studied the patient journey in surgical wards in order to find an effective and efficient way of scheduling in surgical wards.Methods: We applied Root cause analysis (RCA) model within three months in a referral hospital. After understanding root causes of the events occurred through a focus discussion group, required interventions were proposed according to literatures, experiences, and preference of the stakeholders. Possible interventions were also analyzed based on its ability to reduce contributing factors in the events and the belief of process-owner that if interventions can be implemented.Results: The results were provided for five main steps: 1) the most important root cause was “not prioritizing patients and pre-scheduling the number of surgical procedures in the days before”. 2) Constraints indicated that workforce weren't allocated proportionally to the number of surgical operations in varying shift lengths, increased numbers of on-calls physicians increased related costs, the admission of patients in VIP wards have been getting a high priority, and surgeon compensation based on fee for service method was challenging. 3) The current situation of allocating three rooms on average for each physician can be changed depending on numbers of surgeries. 4) Proposed interventions are establishing a computer registration system, reforming payment methods, setting up an electronic waiting list, development of scheduling guidelines, and Applying MIP model.Conclusions: Implementing of scheduling reforms requires a comprehensive action plan system and predefined functional indicators. These should be achieved with considering comments of all clinical and technical groups to ensure the feasibility of an operating room schedule.Keywords: patient journey, surgery, scheduling, Root cause analysis (RCA), patient transfe

Managing daily surgery schedules in a teaching hospital: a mixed-integer optimization approach

Background: This study examined the daily surgical scheduling problem in a teaching hospital. This problem relates to the use of multiple operating rooms and different types of surgeons in a typical surgical day with deterministic operation durations (preincision, incision, and postincision times). Teaching hospitals play a key role in the health-care system; however, existing models assume that the duration of surgery is independent of the surgeon's skills. This problem has not been properly addressed in other studies. We analyze the case of a Spanish public hospital, in which continuous pressures and budgeting reductions entail the more efficient use of resources. Methods: To obtain an optimal solution for this problem, we developed a mixed-integer programming model and user-friendly interface that facilitate the scheduling of planned operations for the following surgical day. We also implemented a simulation model to assist the evaluation of different dispatching policies for surgeries and surgeons. The typical aspects we took into account were the type of surgeon, potential overtime, idling time of surgeons, and the use of operating rooms. Results: It is necessary to consider the expertise of a given surgeon when formulating a schedule: such skill can decrease the probability of delays that could affect subsequent surgeries or cause cancellation of the final surgery. We obtained optimal solutions for a set of given instances, which we obtained through surgical information related to acceptable times collected from a Spanish public hospital. Conclusions: We developed a computer-aided framework with a user-friendly interface for use by a surgical manager that presents a 3-D simulation of the problem. Additionally, we obtained an efficient formulation for this complex problem. However, the spread of this kind of operation research in Spanish public health hospitals will take a long time since there is a lack of knowledge of the beneficial techniques and possibilities that operational research can offer for the health-care system

Managing daily surgery schedules in a teaching hospital: A mixed-integer optimization approach

This study examined the daily surgical scheduling problem in a teaching hospital. This problem relates to the use of multiple operating rooms and different types of surgeons in a typical surgical day with deterministic operation durations (preincision, incision, and postincision times). Teaching hospitals play a key role in the health-care system; however, existing models assume that the duration of surgery is independent of the surgeon?s skills. This problem has not been properly addressed in other studies. We analyze the case of a Spanish public hospital, in which continuous pressures and budgeting reductions entail the more efficient use of resources.Fil: Pulido, Raul. Politecnico Di Milano; Italia. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: Aguirre, Adrian Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); ArgentinaFil: Ortega Mier, Miguel. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: García Sanchez, Alvaro. Escuela Técnica Superior de Ingenieros Industriales; EspañaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentin

Application of Artificial Intelligence declarative methods for Solving Operating Room Scheduling problems in Hospital Environments

Digital health is a relatively new but already important field in which digitalization meets the need to automatically and efficiently solve problems in healthcare to improve the quality of life for patients. The need to efficiently solve some of these problems has become even more pressing due to the Covid-19 pandemic that significantly increased stress and demand on hospitals. Hospitals have long waiting lists, surgery cancellations, and even worse, resource overload—issues that negatively impact the level of patient satisfaction and the quality of care provided. Within every hospital, operating rooms (ORs) are an important unit. The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms, taking into account different specialties, lengths and priority scores of each planned surgery, operating room session durations, and the availability of beds for the entire length of stay both in the Intensive Care Unit and in the wards. A proper solution to the ORS problem is of primary importance for the quality of healthcare service and the satisfaction of patients in hospital environments. In this thesis, we provide several contributions to the ORS problem. We first present a solution to the problem based on Knowledge Representation and Reasoning via modeling and solving approaches using Answer Set Programming (ASP). This first basic solution builds on a previous solution but takes into account explicitly beds and ICU units because in the pandemic we understood how important and limiting they were. Moreover, we also present an ASP solution for the rescheduling problem, i.e., when the off-line schedule cannot be completed for some reasons, and a further extension where surgical teams are also considered. Another technical contribution is a second solution for the basic ORS problem with beds and an ICU unit, whose modeling departs from the guidelines previously used and shows efficiency improvements. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

Determining efficient scheduling approach of doctors for operating rooms: An analysis on Al-Shahid Ghazi Al-Hariri hospital in Baghdad

Government hospitals in Iraq have long been suffering from overcrowded patients, and shortages of doctors and nurses. Unstable environment with occurrences of random warrelated incidents has put further burden on hospitals’ limited resources particularly the surgical department. Large number of pre-scheduled elective surgeries has occasionally been interrupted by the incoming war-related incidents patients. This in turn has put tremendous pressure on the hospital management to maximize utilization of its operating rooms’ resources including surgeons and nurses, whilst simultaneously minimizing idle time. Al-Shahid Ghazi Al-Hariri hospital in Baghdad is presently experiencing these issues. Therefore, this study has been undertaken with the aims to identify efficient scheduling approach for elective surgeries for operating rooms in Al-Shahid Ghazi Al-Hariri hospital while considering interruptions from non-elective surgery (incoming patients from warrelated incidents). Specifically, this study intends to develop a Mixed Integer Linear Programming (MILP) model to maximize the utilizations of operating rooms, availability of surgeons as well as to minimize potential idle time. A meta-heuristic approach in the form of a Tabu Search is then employed to generate an acceptable solution and utilizing time more efficiently. Real data was collected from the hospital in the form of interviews, observations and secondary reports. The initial MILP computational results show that the proposed model has successfully produced optimal solutions by improving the utilization of operating rooms. Notwithstanding, the difficulty to produce results in reasonable time for larger problem instances has led to the application of a more efficient meta-heuristic approach. The Tabu Search results indicated better performance of the model with good quality solutions in fewer computation times. The finding is important as it determines the feasibility of the proposed model and its potential benefit to all relevant stakeholders

ADAPTIVE SCHEDULING FOR OPERATING ROOM MANAGEMENT

The perioperative process in hospitals can be modelled as a 3-stage no-wait flow shop. The utilization of OR units and the average waiting time of patients are related to makespan and total completion time, respectively. However, minimizations of makespan and total completion time are NP-hard and NP-complete. Consequently, achieving good effectiveness and efficiency is a challenge in no-wait flow shop scheduling. The average idle time (AIT) and current and future idle time (CFI) heuristics are proposed to minimize makespan and total completion time, respectively. To improve effectiveness, current idle times and future idle times are taken into consideration and the insertion and neighborhood exchanging techniques are used. To improve efficiency, an objective increment method is introduced and the number of iterations is determined to reduce the computation times. Compared with three best-known heuristics for each objective, AIT and CFI heuristics can achieve greater effectiveness in the same computational complexity based on a variety of benchmarks. Furthermore, AIT and CFI heuristics perform better on trade-off balancing compared with other two best-known heuristics. Moreover, using the CFI heuristic for operating room (OR) scheduling, the average patient flow times are decreased by 11.2% over historical ones at University of Kentucky Health Care

Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment

Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital