Demand analysis and capacity management for hospital emergencies using advanced forecasting models and stochastic simulation. (2021)

• Record Type: Journal Article
• Authors: Barros, Oscar; Weber, Richard; Reveco, Carlos
• Is Part Of: Operations research perspectives. Volume 8(2021)
• Publication Date: 2021
• Subjects: Health care management -- Emergency capacity management -- Forecasting models -- Process design -- Simulation; Operations research -- Periodicals; Management science -- Periodicals; 658.403405
• DOI: 10.1016/j.orp.2021.100208
• Languages: English
• ISSNs: 2214-7160

Abstract

Highlights: According to the results presented in this paper for three hospitals, it is possible to perform demand forecasting with great confidence for hospital emergency services. After testing several forecasting methods, SVR provided best results in terms of variance and accuracy. Based on this forecasting, a logic for managing capacity was developed for one hospital. Such logic uses the comparison between the forecasted demand and the available medical resources and a simulation model to assess the performance of different configurations of facilities and resources. These analyses provide hospital managers a decision tool for determining the number and distribution of medical resources on the emergency service, based on a cost/benefit analysis of resources and service improvement. The results above support the task of assigning doctors to different kinds of boxes, defining their work schedules, and considering additional doctors.. The forecasting method and the capacity management logic proposed in this paper have been validated and accepted by hospital managers and staff, and are currently in use in the Hospital Luis Calvo Mackenna (HLCM) which is a major pediatric hospital in Santiago, Chile. For this use, there was a need for formal processes, which embed the forecasting model and the resources management logic, including a support computing system. The results with the implemented processes have been encouraging and the National Health Authorities are considering extending the whole design concept to other public hospitals in Chile. It is important to notice that the design of the processes, with the embedded analytics and IT support, is not a one-time effort. Its design includes the periodical execution and adaptation of the processes under changing conditions, such as unexpected demand, for example, epidemic episodes and new campaigns, which require adapting capacity. An interesting feature of the work reported in this paper is the integration of several methods presented independently in different publications. Such methods are forecasting, simulation, processes design, and IT support. The methods' integration facilitates the practical use of quantitative models, since, when their use is independent and on a one-time basis, they may produce interesting results, but there is no guarantee of a practical impact. The integration takes care explicitly of designing a solution for routine use, which also has adaptation capabilities to facilitate use under changing conditions. The solution is also general and admits adaptation and extension to other services. Thus, similar work has been started in an ambulance service operation and another in surgical services. Possible future research directions are related to updating the proposed forecasting models as new data is gathered. A methodology for dynamic feature selection and support vector regression has been presented in [1] . Abstract: Demand forecasting and capacity management are complicated tasks for emergency healthcare services due to the uncertainty, complex relationships, and high public exposure involved. Published research does not show integrated solutions to these tasks. Thus, the objective of this paper is to present results from three hospitals that show the feasibility of routinely applying integrated forecasting and capacity management with advanced operations research tools. After testing several forecasting methods, neural networks and support vector regression provided the best results in terms of variance and accuracy. Based on this forecasting, a logic for managing hospital capacity was designed and implemented. This logic includes the comparison between the forecasted demand and the available medical resources and a stochastic simulation model to assess the performance of different configurations of facilities and resources. The logic also provides hospital managers with a decision tool for determining the number and distribution of medical resources on emergency services based on a cost/benefit analysis of resources and service improvement. Such results support the task of assigning doctors to different kinds of boxes, defining their work schedules, and considering additional doctors. The contribution of this paper consists of an integrated solution designed to implement the abovementioned logic. This solution combines forecasting, simulation for capacity management, process design, and IT support, facilitating the practical routine use of complex models. The integration explicitly considers a solution that also has adaptation capabilities to facilitate use under changing conditions. The solution is also general and admits adaptation and extension to other services. Thus, we have already performed similar work for ambulatory and surgical services.