Optimizing Cash Flows and Minimizing Simultaneous Turnovers in Operating Room Scheduling

Currently, the scheduling of surgical suites follows either an open booking or block booking framework. Under block booking, medical departments (or surgeons) that provide certain types of services (e.g. ophthalmology, orthopedics, cardiology) are assigned fixed blocks of time that are used to divide access to the operating rooms (ORs) among different specialties. Two integer-programming based methods of generating block schedules are investigated in this research. The first approach focuses on optimizing cash flows, an area not studied previously within the OR scheduling domain. Results indicate that while there is some utility of this approach in improving the liquidity of a healthcare facility, its contribution towards increasing overall revenues is marginal. The second approach aims to minimize simultaneous turnovers of operating rooms. Although reduction in turnover times is a frequently studied area in literature, the solution presented here is novel in its attempt to minimize the occurrences of turnovers in two or more rooms at the same time, which places a strain on shared resources and leads to delays in planned start times of procedures. Results for this approach are promising in reduction of turnover times and consequently, workload on resources required to perform turnovers. Both approaches begin with the study of existing schedules to derive key insights into the chosen target parameters and then propose alternative schedules to optimize the aforementioned objectives. The proposed methods are designed to be minimally disruptive so as to remain feasible in real life scenarios

Joint optimization of allocation and release policy decisions for surgical block time under uncertainty

The research presented in this dissertation contributes to the growing literature on applications of operations research methodology to healthcare problems through the development and analysis of mathematical models and simulation techniques to find practical solutions to fundamental problems facing nearly all hospitals. In practice, surgical block schedule allocation is usually determined regardless of the stochastic nature of case demand and duration. Once allocated, associated block time release policies, if utilized, are often simple rules that may be far from optimal. Although previous research has examined these decisions individually, our model considers them jointly. A multi-objective model that characterizes financial, temporal, and clinical measures is utilized within a simulation optimization framework. The model is also used to test “conventional wisdom” solutions and to identify improved practical approaches. Our result from scheduling multi-priority patients at the Stafford hospital highlights the importance of considering the joint optimization of block schedule and block release policy on quality of care and revenue, taking into account current resources and performance. The proposed model suggests a new approach for hospitals and OR managers to investigate the dynamic interaction of these decisions and to evaluate the impact of changes in the surgical schedule on operating room usage and patient waiting time, where patients have different sensitivities to waiting time. This study also investigated the performance of multiple scheduling policies under multi-priority patients. Experiments were conducted to assess their impacts on the waiting time of patients and hospital profit. Our results confirmed that our proposed threshold-based reserve policy has superior performance over common scheduling policies by preserving a specific amount of OR time for late-arriving, high priority demand

Scheduling Elective Surgeries in Multiple Operating Rooms

This thesis focuses on the problem of designing appointment schedules in a surgery center with multiple operating rooms. The conditions under which overlapping surgeries in the surgeons’ schedule (i.e. parallel surgery processing) at the lowest cost are investigated with respect to three components of the total cost: waiting time, idle time, and overtime. A simulation optimization method is developed to find the near-optimal appointment schedules for elective surgical procedures in the presence of uncertain surgery durations. The analysis is performed in three steps. First, three near-optimal operating room schedules are found for different cost configurations based on the secondary data of surgery durations obtained from the Canadian Institute for Health Information. Second, these near-optimal appointment schedules are used to test a parallel scheduling policy where each surgeon has overlapping surgeries scheduled in two operating rooms for the entire session (480 minutes) and only attends the critical portions of surgeries in the two operating rooms. Lastly, another parallel scheduling policy is tested where each surgeon has overlapping surgeries scheduled for half of the session duration (240 minutes) and only has surgeries scheduled in one operating room for the remaining time. These two policies are tested using simulation with scenarios for parallelizable portions of surgeries varying from 0.1 to 0.9 at 0.1 increments and three cost configurations. In the simulated scenarios, the total cost is calculated as the weighted sum of patient waiting time, surgeon idle time, surgeon overtime, operating room idle time, and operating room overtime. Out of the nine scenarios for each policy and each cost configuration, the parallelizable portion of surgeries that result in the lowest total cost is identified. The results from both policies indicate that implementing parallel scheduling policies for surgery types with higher parallelizable portions results in surgeons remaining idle for longer periods during the session. This idle time cost is justified by a decrease in other cost components for surgeries with parallelizable portions 50% or less; however, the total cost is higher for surgeries with parallelizable portions over 50%. In addition, it has been observed that overlapping surgeries with lower parallelizable portions is more expensive than overlapping those over with 50%. Therefore, it is concluded that the surgery types that allow parallel surgery scheduling policies to be implemented at the lowest cost have 50% of their duration parallelizable

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

Developing a multi-methodological approach to hospital operating theatre scheduling

Operating theatres and surgeons are among the most expensive resources in any hospital, so it is vital that they are used efficiently. Due to the complexity of the challenges involved in theatre scheduling we split the problem into levels and address the tactical and day-to-day scheduling problems.Cognitive mapping is used to identify the important factors to consider in theatre scheduling and their interactions. This allows development and testing of our understanding with hospital staff, ensuring that the aspects of theatre scheduling they consider important are included in the quantitative modelling.At the tactical level, our model assists hospitals in creating new theatre timetables, which take account of reducing the maximum number of beds required, surgeons’ preferences, surgeons’ availability, variations in types of theatre and their suitability for different types of surgery, limited equipment availability and varying the length of the cycle over which the timetable is repeated. The weightings given to each of these factors can be varied allowing exploration of possible timetables.At the day-to-day scheduling level we focus on the advanced booking of individual patients for surgery. Using simulation a range of algorithms for booking patients are explored, with the algorithms derived from a mixture of scheduling literature and ideas from hospital staff. The most significant result is that more efficient schedules can be achieved by delaying scheduling as close to the time of surgery as possible, however, this must be balanced with the need to give patients adequate warning to make arrangements to attend hospital for their surgery.The different stages of this project present different challenges and constraints, therefore requiring different methodologies. As a whole this thesis demonstrates that a range of methodologies can be applied to different stages of a problem to develop better solutions

The Impact of Block Scheduling and Release Time on Operating Room Efficiency

Planning for sufficient surgical capacity at a hospital requires that many tactical and operational decisions be made before the day of surgery. Typically, blocks of time in operating rooms (ORs) are assigned and specific surgical cases are placed in rooms. The hospital monitors utilization to determine the schedule\u27s effectiveness in balancing the risk of overtime with idle time. In this thesis, we will examine how adjusting schedule risk ratios and penalty values, and providing shared, open posting time affected the hospital\u27s ability to identify an efficient but high quality and low cost block schedule. The proposed schedules were tested by assigning surgical cases to ORs and simulating the schedule\u27s performance using recent data from a local hospital. We also show how scheduling accuracy can impact the performance level of the schedules proposed. Once the schedule has been set, the use of block release time is investigated in order to provide insight on how to better fill these ORs and increase utilization levels. Release policies are simulated based on various surgery arrival distributions, capacity levels, and case durations. We will show how different policies involving assigned and open posting rooms impact utilization levels, number of cases not fit into the schedule, and number of cases posted after the block release time

Scheduling Elective Surgeries in Operation Room with Optimization of Post-Surgery Recovery Unit Capacity

Scheduling of surgeries in the Operation rooms with limited available resources is a very complex process. Patients of different specialties are operated by surgery teams in operation rooms and sent to recovery units. In this thesis, we develop a model to help Operation room scheduling management to schedule elective patients based on the availability of surgeons and operation rooms with three phase hierarchical approach of scheduling. A linear integer goal programming method is used to solve problem. The model tries to minimize number of patients waiting for service, underutilization of operating room hours and maximum number of patients in the recovery units. Windsor Regional Hospital help is taken to understand the surgery booking procedure. Lexicographic goal programming method and weighted goal programming is employed and various combinations of priorities are solved to schedule Operating rooms. The focus of the study is to develop mathematical model for scheduling

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Operating Room Planning under Surgery Type and Priority Constraints

AbstractOperating room (OR) planning is critical in healthcare systems to reduce cost and improve the efficiency of OR scheduling. The OR planning problem is complicated, involving many conflicting factors, such as overtime and idle time, both of which affect OR utilization and consequently affect cost to a hospital. Allocating different types of surgeries into OR blocks affects the setup cost, whereas priorities of surgeries affect OR block scheduling. Surgery durations affect both OR utilization and OR block scheduling. Traditionally, one important method for OR block scheduling is the bin packing model, and the longest processing time (LPT) rule is the most commonly used method to generate the initial sequence for bin packing. In this study. We propose a multistep approach and a priority-type-duration (PTD) rule to generate the initial sequence for bin packing. The results of our case studies show that our PTD rule outperforms the LPT rule based on the cost to OR scheduling