Scheduling Operating Rooms with Elective and Emergency Surgeries

Production and Operations Management, 2019

Operating rooms (ORs) are the greatest source of revenues for hospitals and also their largest cost centers. When scheduling surgeries, hospitals face a trade-off between the need to conduct planned elective surgeries and the need to be responsive to emergency cases. However, scheduling ORs, especially at Level-1 trauma hospitals, is challenging due to significant uncertainties in the arrivals of patients requiring emergent surgery. The issue of allocating limited capacity to emergent surgery cases while scheduling elective patients has major policy implications. We develop a model for allocating the OR capacity to elective patients in such a way that the emergency patients who arrive randomly can be accommodated without incurring excessive delays. The objective is to develop a framework for aggregate weekly schedules and generate detailed daily schedules that minimize the total cost of the ORs' expected operating time, idle time, and overtime. We present optimization procedures for generating effective schedules and rescheduling procedures that adjust the schedules of elective patients affected by emergency arrivals. Initially, the procedures assume deterministic surgery times for elective patients; the procedures are then extended to include stochastic surgery times. We show that for a given arrival rate of emergency patients, the total expected cost is convex in the weekly load of elective surgeries being scheduled. Numerical experiments are devised to obtain total expected cost curves for various emergency arrival rates. Using these curves, the optimal capacity allocation of ORs to elective patients can be determined as a function of the emergency arrival rate.

Proceedings of the 2010 Winter Simulation Conference, 2010

When organizing the operating theatre and scheduling surgeries, hospitals face a trade-off between the need to be responsive to emergency cases and to conduct scheduled elective surgeries efficiently. We develop a simulation model to compare a flexible and a focused resource-allocation policy. We evaluate these two policies on patient and provider outcome measures, including patient wait time and physician overtime. We find that the focused policy results in lower elective wait time and lower overtime, which leads to the conclusion that electives benefit more from the elimination of emergency disruptions than what they lose from the reduced access to operating rooms. Emergency patient wait time, however, increases significantly as we shift from the flexible to the focused policy. The sensitivity analysis showed that average emergency wait time can decrease as the processing time variability increases. The trade-off between efficiency and responsiveness calls for additional research on other operating-room-allocation policies.

European Journal of Operational Research, 2010

Surgery is one of the most important functions in hospitals and it generates revenue and admissions to hospitals. The operating cost of a surgery department is the one of the largest hospital cost category, approximately one-third of the total cost (Macario et al., 1995). Surgery is thus ...

European Journal of Operational Research, 2008

This paper describes a stochastic model for Operating Room (OR) planning with two types of demand for surgery: elective surgery and emergency surgery. Elective cases can be planned ahead and have a patient-related cost depending on the surgery date. Emergency cases arrive randomly and have to be performed on the day of arrival. The planning problem consists in assigning elective cases to different periods over a planning horizon in order to minimize the sum of elective patient related costs and overtime costs of operating rooms. A new stochastic mathematical programming model is first proposed. We then propose a Monte Carlo optimization method combining Monte Carlo simulation and Mixed Integer Programming. The solution of this method is proved to converge to a real optimum as the computation budget increases. Numerical results show that important gains can be realized by using a stochastic OR planning model.

Scientia Iranica, 2021

The rapid growth of the population has resulted in an increasing demand for healthcare services, which forces managers to use costly resources such as operating rooms effectively. The surgery-scheduling problem is a general title for problems that consists of the patient selection and sequencing of the surgeries at the operational level, setting their start times, and assigning the 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 through a simulation method based on real data. This algorithm respects working hours and availability of staff and other resources in a surgical suite.

Social Science Research Network, 2020

Elective surgery patients receive surgery in the operating room (OR), and then recover in one or more subsequent downstream recovery units for several consecutive hours or days after surgery. Indeed, upstream scheduling that focuses on OR alone or a resource-constrained scheduling approach that fails to account for the inherent uncertainty in surgery durations and postoperative downstream recovery times yield sub-optimal or infeasible schedules and, consequently, higher cost and reduced quality of care. However, modeling such uncertainties at multiple levels is challenging, especially with limited reliable data on the random parameters in the models. Moreover, sequencing of surgical and recovery activities, and the multiple conflicting objectives of all parties involved (including management, clinicians, patients), lead to a class of complex combinatorial and multicriteria stochastic optimization problems. In this review, we focus on stochastic optimization (SO) approaches for elective surgery scheduling and downstream capacity planning. We describe the art of formulating and solving such a class of stochastic resource-constrained scheduling problems, provide an analysis of existing SO approaches and their challenges, and highlight areas of opportunity for developing tractable, implementable, and data-driven approaches that might be applicable within and outside healthcare operations, particularly where multiple entities/jobs share the same downstream limited resources.

Operations research for health care, 2019

h i g h l i g h t s • A chance-constrained model for admission scheduling of surgical patients is proposed. • Sample average approximation is employed for approximating the stochastic model. • A Late Acceptance Hill Climbing meta-heuristic is implemented for solving the models. • The flexibility of the model is illustrated by implementing four admission policies. • Computational results show that the model enables managing the risk on bed shortages.

Intelligent Patient …, 2009

In health care system, the operating theatre is recognized as having an important role, notably in terms of generated income and cost. Its management, and in particular its scheduling, is thus a critical activity, and has been the sub ject of many studies. However, the stochasticity of the operating theatre environment is rarely considered while it has considerable effect on the actual working of a surgical unit. In practice, the planners keep a safety margin, let's say 15% of the capacity, in order to absorb the effect of unpredictable events. However, this safety margin is most often chosen sub jectively, from experience. In this paper, our goal is to rationalize this process. We want to give insights to managers in order to deal with the stochasticity of their environment, at a tactical-strategic decision level. For this, we propose an analytical approach that takes account of the stochastic operating times as well as the disruptions caused by emergency arrivals. From our model, various performance measures can be computed: the emergency disruption rate, the waiting time for an emergency, the distribution of the working time, the probability of overtime, the average overtime, etc. In particular, our tool is able to tell how many operations can be scheduled per day in order to keep the overtime limited.

Health Care Management Science, 2014

Operating room (OR) allocation and planning is one of the most important strategic decisions that OR managers face. The number of ORs that a hospital opens depends on the number of blocks that are allocated to the surgical groups, services, or individual surgeons, combined with the amount of open posting time (i.e., first come, first serve posting) that the hospital wants to provide. By allocating too few ORs, a hospital may turn away surgery demand whereas opening too many ORs could prove to be a costly decision. The traditional method of determining block frequency and size considers the average historical surgery demand for each group. However, given that there are penalties to the system for having too much or too little OR time allocated to a group, demand variability should play a role in determining the real OR requirement. In this paper we present an algorithm that allocates block time based on this demand variability, specifically accounting for both over-utilized time (time used beyond the block) and under-utilized time (time unused within the block). This algorithm provides a solution to the situation in which total caseload demand can be accommodated by the total OR resource set, in other words not in a capacity-constrained situation. We have found this scenario to be common among several regional healthcare providers with large OR suites and excess capacity. This algorithm could be used to adjust existing blocks or to assign new blocks to surgeons that did not previously have a block. We also have studied the effect of turnover time on the number of ORs that needs to be allocated. Numerical experiments based on real data from a large health-care provider indicate the opportunity to achieve over 2,900 hours of OR time savings through improved block allocations.

Proceedings of the Winter Simulation Conference 2014, 2014

There are two main types of surgeries within an operating room (OR) suite, namely elective (or scheduled) and non-elective (or non-scheduled) surgeries. Non-elective surgeries count for a considerable proportion of surgery demand and often have a priority for begin served in a timely manner. Accommodating this type of surgery can be a challenging task on the day of surgery. This is mainly a result of the uncertain demand for non-elective surgeries, which discourages hospitals from reserving sufficient capacity for these surgeries. Using simulation, we evaluate an optimal policy for accommodating elective and non-elective surgeries that minimizes waiting time of patients, overtime, and number of patients turned away. We carry out the analysis on a stylized, two-room study where one room is dedicated to non-elective cases and the other room contains elective cases but can accept a non-elective case if necessary. The optimal policy is originally found by using a Markov decision process (MDP). However, since Markov modeling has an exponential arrival rate and steady state assumptions, which may not always be true in a surgical environment, the evaluation through simulation allows these assumptions to be relaxed.