We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.

/pdf/taxonomic-classification-of-planning-decisions-in-health-2eisyi4lp1.pdf

Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS

In this survey we review quantitative health care models to illustrate the extent to which they encompass multiple hospital departments. The paper provides general overviews of the relationships that exists between major hospital departments and describes how these relationships are accounted for by researchers. We find the atomistic view of hospitals often taken by researchers is partially due to the ambiguity of patient care trajectories. To this end clinical pathways literature is reviewed to illustrate its potential for clarifying patient flows and for providing a holistic hospital perspective.

/pdf/a-survey-of-health-care-models-that-encompass-multiple-42tk6v2rpl.pdf

A Survey of Health Care Models that Encompass Multiple Departments

In recent years, Operations Research/Management (OR/OM) has had a significant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient flow from the initial call to the ED through disposition, discharge home, or admission to the hospital. We review approximately 350 related papers to (i) demonstrate the influence of OR/OM in EDs, and (ii) assist both researchers and practitioners with the OR/OM techniques already available to optimize ED patient flow. In addition, we elaborate on some practical challenges yet to be addressed. By shedding light on some less studied aspects that can have significant impacts on ED operations, we also discuss important possibilities for future OR/OM researchers.

/pdf/operations-research-management-contributions-to-emergency-1zxan2yfmd.pdf

Operations research/management contributions to emergency department patient flow optimization: Review and research prospects

In recent years, Operations Research/Management (OR/OM) has had a significant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient flow from the initial call to the ED through disposition, discharge home, or admission to the hospital. We review approximately 350 related papers to (a) demonstrate the influence of OR/OM in EDs, and (b) assist both researchers and practitioners with the OR/OM techniques already available to optimize ED patient flow. In addition, we elaborate on some practical challenges yet to be addressed. By shedding light on some less studied aspects that can have significant impacts on ED operations, we also discuss important possibilities for future OR/OM researchers.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2560432_code1185139.pdf?abstractid=2420163&mirid=3

Operations Research/Management Contributions to Emergency Department Patient Flow Optimization: Review and Research Prospects

Developing techniques for optimal allocation of limited resources to a set of activities has received increasing attention in recent years. In this paper, an efficient resource allocation scheme based on particle swarm optimization (PSO) is developed. Different from many existing evolutionary algorithms for solving resource allocation problems (RAPs), this PSO algorithm incorporates a novel representation of each particle in the population and a comprehensive learning strategy for the PSO search process. The novelty of this representation lies in that the position of each particle is represented by a pair of points, one on each side of the constraint hyper-plane in the problem space. The line joining these two points intersects the constraint hyper-plane and their intersection point indicates a feasible solution. With the evaluation value of the feasible solution used as the fitness value of the particle, such a representation provides an effective way to ensure the equality resource constraints in RAPs are met. Without the distraction of infeasible solutions, the particle thus searches the space smoothly. In addition, particles search for optimal solutions by learning from themselves and their neighborhood using the comprehensive learning strategy, helping prevent premature convergence and improve the solution quality for multimodal problems. This new algorithm is shown to be applicable to both single-objective and multiobjective RAPs, with performance validated by a number of benchmarks and by a real-world bed capacity planning problem. Experimental results verify the effectiveness and efficiency of the proposed algorithm.

An Efficient Resource Allocation Scheme Using Particle Swarm Optimization

A simple method to optimize hospital beds capacity.

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A simple method to optimize hospital beds capacity.