In this paper we analyse the operating room planning at a department of orthopaedic surgery in Sweden. We focus on the problem of meeting the uncertainty in demand of patient arrival and surgery duration and at the same time maximizing the utilization of operating room (OR) time. With a discrete-event model we simulate how different management polices affect different performance metrics such as patient waiting time, cancellations and the utilization of OR time. The experiments show that the performance of the operating room department can be improved significantly by applying a different policy in reserving OR-capacity for emergency cases together with a policy to increase staff in stand-by. Moreover, the developed simulation model provides estimates for a what-if situation related to the prognosis of an increasing number of hip-joint replacements.
Abstract. Computer-simulated experiments have become a cost effective way for engineers to replace real experiments in the area of product development. However, one single computer-simulated experiment can still take a significant amount of time. Hence, in order to minimize the amount of simulations needed to investigate a certain design space, different approaches within the design of experiments area are used. One of the used approaches is to minimize the time consumption and simulations for design space exploration through response surface modeling. The traditional methods used for this purpose are linear regression, quadratic curve fitting and support vector machines. This paper analyses and compares the performance of four machine learning methods for the regression problem of response surface modeling. The four methods are linear regression, support vector machines, M5P and random forests. Experiments are conducted to compare the performance of tree models (M5P and random forests) with the performance of non-tree models (support vector machines and linear regression) on data that is typical for concept evaluation within the aerospace industry. The main finding is that comprehensible models (the tree models) perform at least as well as or better than traditional black-box models (the non-tree models). The first observation of this study is that engineers understand the functional behavior, and the relationship between inputs and outputs, for the concept selection tasks by using comprehensible models. The second observation is that engineers can also increase their knowledge about design concepts, and they can reduce the time for planning and conducting future experiments.
ObjectivesTo explore the value of simulation modelling in evaluating the effects of strategies to plan and schedule operating room (OR) resources aimed at reducing time to surgery for non-elective orthopaedic inpatients at a Swedish hospital.MethodsWe applied discrete-event simulation modelling. The model was populated with real world data from a university hospital with a strong focus on reducing waiting time to surgery for patients with hip fracture. The system modelled concerned two patient groups that share the same OR resources: hip-fracture and other non-elective orthopaedic patients in need of surgical treatment. We simulated three scenarios based on the literature and interaction with staff and managers: (1) baseline; (2) reduced turnover time between surgeries by 20 min and (3) one extra OR during the day, Monday to Friday. The outcome variables were waiting time to surgery and the percentage of patients who waited longer than 24 hours for surgery.ResultsThe mean waiting time in hours was significantly reduced from 16.2 hours in scenario 1 (baseline) to 13.3 hours in scenario 2 and 13.6 hours in scenario 3 for hip-fracture surgery and from 26.0 hours in baseline to 18.9 hours in scenario 2 and 18.5 hours in scenario 3 for other non-elective patients. The percentage of patients who were treated within 24 hours significantly increased from 86.4% (baseline) to 96.1% (scenario 2) and 95.1% (scenario 3) for hip-fracture patients and from 60.2% (baseline) to 79.8% (scenario 2) and 79.8% (scenario 3) for patients with other non-elective patients.ConclusionsHealthcare managers who strive to improve the timelines of non-elective orthopaedic surgeries may benefit from using simulation modelling to analyse different strategies to support their decisions. In this specific case, the simulation results showed that the reduction of surgery turnover times could yield the same results as an extra OR.
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