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Operations Research approaches to surgical scheduling are becoming increasingly popular in both theory and practice. Often these models neglect stochasticity in order to reduce the computational complexity of the problem. We wish to provide practitioners and hospital administrative staff with a start-of-day probabilistic forecast for the occupancy of post-surgery recovery spaces. The model minimises the maximum expected occupancy of the recovery unit, thus levelling the workload of hospital staff, and reducing the likelihood of bed shortages and bottlenecks. We show that a Poisson binomial random variable models the number of patients in the recovery when parameterised by the surgical case sequence. A mixed integer nonlinear programming model for the surgical case sequencing problem reduces the maximum expected occupancy in post-surgery recovery spaces. Simulated Annealing produces good solutions in short amounts of computational time. We evaluate the methodology with a full year of historical data. The solution techniques reduce maximum expected recovery occupancy by 18% on average. This alleviates a large amount of stress on staff in the postsurgery recovery spaces, reduces the likelihood of bottlenecks, and improves the quality of care provided to patients.

Demand for healthcare is increasing rapidly. To meet demand, we must improve the efficiency of our public health services. We present a mixed integer programming (MIP) formulation that simultaneously tackles the integrated Master Surgical Schedule (MSS) and Surgical Case Assignment (SCA) problems. We consider volatile surgical durations and non-elective arrivals whilst applying a rolling horizon approach to adjust the schedule after cancellations, equipment failure, or new arrivals on the waiting list. A case study of an Australian public hospital with a large surgical department is the basis for the model. The formulation includes significant detail and provides practitioners with a globally implementable model. We produce good feasible solutions in short amounts of computational time with a constructive heuristic and two hyper metaheuristics. Using a rolling horizon schedule increases patient throughput and can help reduce waiting lists., Comment: arXiv admin note: substantial text overlap with arXiv:1808.10133

In this paper, we address the multiple operating room (OR) surgical case sequencing problem (SCSP). The objective is to maximise total OR utilisation during standard opening hours. This work uses a case study of a large Australian public hospital with long surgical waiting lists and high levels of non-elective demand. Due to the complexity of the SCSP and the size of the instances considered herein, heuristic techniques are required to solve the problem. We present constructive heuristics based on both a modified block scheduling policy and an open scheduling policy. A number of real-time reactive strategies are presented that can be used to maintain schedule feasibility in the case of disruptions. Results of computational experiments show that this approach maintains schedule feasibility in real-time, whilst increasing operating theatre (OT) utilisation and throughput, and reducing the waiting time of non-elective patients. The framework presented here is applicable to the real-life scheduling of OT departments, and we provide recommendations regarding implementation of the approach.

BACKGROUND: Analytical techniques are being implemented with increasing frequency to improve the management of surgical departments and to ensure that decisions are well-informed. Often these analytical techniques rely on the validity of underlying statistical assumptions, including those around choice of distribution when modelling uncertainty. OBJECTIVE: The objective of the research is to determine a set of suitable statistical distributions and provide recommendations to assist hospital planning staff, based on three full years of historical data. METHODS: Statistical analysis has been performed to determine the most appropriate distributions and models in a variety of surgical contexts. Data from 2013 to 2015 was collected from the surgical department at a large Australian public hospital. RESULTS: A lognormal distribution approximation of the total duration of surgeries in an operating room is appropriate when considering probability of overtime. Surgical requests can be modelled as a Poisson process with rate dependent on urgency and day of the week. It is found that individual cancellations can be modelled as Bernoulli trials, with the probability of patient, staff, and resource based cancellations provided herein. CONCLUSIONS: The analysis presented here can be used to ensure that assumptions surrounding planning and scheduling in the surgical department are valid. Understanding the stochasticity in the surgical department may result in the implementation of more realistic decision models.