In recent decades, health care costs have dramatically increased, while health care organisations have been under severe pressure to provide improved quality health care for their patients. Several health care administrators have used discrete-event simulation as an effective tool for allocating scarce resources to improve patient flow, while minimising health care delivery costs and increasing patient satisfaction. The rapid growth in simulation software technology has created numerous new application opportunities, including more sophisticated implementations, as well as combining optimisation and simulation for complex integrated facilities. This paper surveys the application of discrete-event simulation modeling to health care clinics and systems of clinics (for example, hospitals, outpatient clinics, emergency departments, and pharmacies). Future directions of research and applications are also discussed.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2600669.pdf

Application of discrete-event simulation in health care clinics: A survey

BACKGROUND: The objective of the review was to evaluate the extent, quality and value of computer simulation modelling in population health and health care delivery.
METHODS: A narrative systematic review was carried out of world literature from 1980 to 1999, searching Medline, INSPEC, Embase, HealthSTAR, Science Citation Index, CINAHL, MathSci, INFORMS Online and SIGLE databases, and researchers in the field were contacted. Papers were included if they contained a computer simulation model of individuals in a stochastic system and the topic or setting related to population health or health service delivery.
RESULTS: A total of 182 papers met the inclusion criteria. Simulation modelling has been undertaken in a wide range of health care topic areas, including hospital scheduling and organization, communicable disease, screening, costs of illness and economic evaluation. However, the quality of published papers was variable and few reported on the outcomes of implementation of models, so that the value of modelling could not be assessed.
CONCLUSION: Simulation modelling is a powerful method for modelling both small and large populations to inform policy makers in the provision of health care. It has been applied to a wide variety of health care problems. Although the number of modelling papers has grown substantially over recent years, further research is required to assess the value of modelling.

Systematic review of the use and value of computer simulation modelling in population health and health care delivery

Over the past forty years, health care organizations have faced ever-increasing pressures to deliver quality care while facing rising costs, lower reimbursements, and new regulatory demands. Discrete-event simulation has become a popular and effective decision-making tool for the optimal allocation of scarce health care resources to improve patient flow, while minimizing health care delivery costs and increasing patient satisfaction. The proliferation of increasingly sophisticated discrete-event simulation software packages has resulted in a large number of new application opportunities, including more complex implementations. In addition, combined optimization and simulation tools allow decision-makers to quickly determine optimal system configurations, even for complex integrated facilities. This chapter provides an overview of discrete-event simulation modeling applications to health care clinics and integrated health care systems (e.g. hospitals, outpatient clinics, emergency departments, and pharmacies) over the past forty years.

Discrete-event simulation of health care systems

Modelling patient flow in health care systems is vital in understanding the system activity and may therefore prove to be useful in improving their functionality. An extensively used measure is the average length of stay which, although easy to calculate and quantify, is not considered appropriate when the distribution is very long-tailed. In fact, simple deterministic models are generally considered inadequate because of the necessity for models to reflect the complex, variable, dynamic and multidimensional nature of the systems. This paper focuses on modelling length of stay and flow of patients. An overview of such modelling techniques is provided, with particular attention to their impact and suitability in managing a hospital service.

/pdf/length-of-stay-based-patient-flow-models-recent-developments-38a2qcg5n0.pdf

Length of Stay-Based Patient Flow Models: Recent Developments and Future Directions

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

Due to its highly stochastic nature and complex interaction between services involved, health care has been a demanding area of application for computer simulation. This paper includes details and results of a simulation study realized in the Surgical Emergency Department at Istanbul University School of Medicine. The purpose is to suggest new bed capacities to improve the current system, and also to provide the management with guidelines for their expansion plans. For this aim, arrival rates, treatment procedures, inpatient admittance, and service durations have been carefully analyzed and modeled. The model, coded in SLAM-II simulation language, has been run under several bed capacity scenarios, and resulting queueing and waiting patterns have been discussed in detail.

I Kuban Altιnel

Papers

Simulation modeling for emergency bed requirement planning