Reducing post-surgery recovery bed occupancy with a probabilistic forecast model (original) (raw)
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Reducing post-surgery recovery bed occupancy through an analytical prediction model
arXiv: Applications, 2018
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. In this paper, historical data is used to examine the occupancy of post-surgery recovery spaces as a function of the initial surgical case sequence. We show that the number of patients in the recovery space is well modelled by a Poisson binomial random variable. A mixed integer nonlinear programming model for the surgical case sequencing problem is presented that reduces the maximum expected occupancy in post-surgery recovery spaces. Given the complexity of the problem, Simulated Annealing is used to produce good solutions in short amounts of computational time. Computational experiments are performed to compare the methodology here to a full year of historical data. The solution techniques presented are able to reduce maximum expected recovery occupancy by 18% on ...
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Operating Room Staffing and Scheduling
Manufacturing & Service Operations Management, 2020
Problem definition: We consider two problems faced by an operating room (OR) manager: (1) how many baseline (core) staff to hire for OR suites, and (2) how to schedule surgery requests that arrive one by one. The OR manager has access to historical case count and case length data, and needs to balance the fixed cost of baseline staff and variable cost of overtime, while satisfying surgeons’ preferences. Academic/practical relevance: ORs are costly to operate and generate about 70% of hospitals’ revenues from surgical operations and subsequent hospitalizations. Because hospitals are increasingly under pressure to reduce costs, it is important to make staffing and scheduling decisions in an optimal manner. Also, hospitals need to leverage data when developing algorithmic solutions, and model tradeoffs between staffing costs and surgeons’ preferences. We present a methodology for doing so, and test it on real data from a hospital. Methodology: We propose a new criterion called the robu...
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A new model for operating room scheduling with elective patient strategy
INFOR: Information Systems and Operational Research, 2021
Hospitals are dealing with the daunting task of scheduling patients in operating rooms under a limited budget, time, and staff. This article finds the optimal schedule of surgeries by minimizing operating rooms' idle times while maximizing the number of scheduled surgeries during the most effective and desirable time windows. Surgeries during ideal time windows are encouraged by assigning bonus weights in the objective function. Stated and implied benefits of this strategy include mitigating financial loss, complications, and death rate due to a reduction in surgery delays. We introduce a binary programming model for scheduling operating rooms and a mixed integer binary program for planning and scheduling both operating and recovery rooms for elected patients under deterministic conditions. We apply an open scheduling strategy for assigning operating rooms to surgeons and a Lagrangian relaxation method for finding promising solutions. We move hard constraints to the objective to reduce the complexity of the proposed NP-hard model. We incorporate a sub-gradient method that selects the best penalty vector. Finally, we benchmark the results to evaluate the efficiency of the proposed solutions.
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