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

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

Health care has undergone a number of radical changes during the past five years. These include increased competition, fixed-rate reimbursement systems, declining hospital occupancy rates, and growth in health maintenance organizations and preferred provider organizations. Given these changes in the manner in which health care is provided, contracted, and paid for, it is appropriate to review the past research on capacity management and to determine its relevance to the changing industry. This paper provides a review, classification, and analysis of the literature on this topic. In addition, future research needs are discussed and specific problem areas not dealt with in the previous literature are targeted.

Capacity Management in Health Care Services: Review and Future Research Directions*

Due to changing patient loads and demand patterns over time, assigning bed complements for various medical services in a hospital is a recurring problem facing the administrators. For a large public health care delivery system, we present an approach for periodically reallocating beds to services to minimize the expected overflows. Using a queueing model to approximate the patient population dynamics for each service-with admission rates provided by forecasts-the expected overflows under each configuration are computed via a Normal loss integral. Bed allocation is done in two stages. First, we establish a base line requirement for each service so that it can handle a prescribed amount of patient load based on a yearly projection of demand. We then use marginal analysis to distribute the remaining beds to minimize the expected total average overflows while taking month-to-month demand fluctuations into account. The proposed model requires only a modest amount of computation, because of several simplifying assumptions, which were tested for reasonableness. For the two largest services, we used empirical data to evaluate the nonhomogeneous Poisson representation of admissions, and we performed simulation experiments to assess the extent of discrepancy in performance characteristics caused by the ignorance of day-of-week effect on admission rates. In view of the intrinsic complexity of the underlying system, the results obtained from the validation studies suggest that the model is relatively "robust" with respect to the case under consideration. It is hoped that the simplicity of the model and the usefulness of the results will induce practitioners to use this type of formal analysis for bed allocation in an institutional setting on a routine basis.

Bed Allocation in a Public Health Care Delivery System

Performance measurement criteria in health care organizations: Review and future research directions

Minimum cost operation of a hospital requires the correct number of beds to meet the de? mand placed on the facility. An excess of beds results in inflated operating and construction costs, while a bed shortage is unacceptable for a variety of reasons, such as the lack of quality care to the community. Several models have been developed and used to assist planners in finding the correct bed size to meet a given demand. These models are inadequate in an environment where stringent cost containment is second only in importance to quality of care. The pre? vious models, such as the Hill-Burton formu? las and the Poisson approximation, are inad? equate because they are incompatible with contemporary admissions scheduling systems. These systems alter the behavior of the census so that analytical models based upon the Pois? son assumption do not fit the results and, along with normative models such as the Hill Burton program, allow too many beds. The implications of the Poisson assumption will be discussed at length here because the contention in the remainder of the paper is that almost any hospital medical/surgical (M/ S) unit should operate well in excess of that model's widely used occupancy recommen? dations. In most cases this also leads to op? eration in excess of the state Hill-Burton rec? ommendations for medical/surgical units, thus Hill-Burton is rejected as a consequence.1 The Poisson assumption for hospital census was first used in the 1940s,2 and has been ap? plied worldwide to determine the size of hos? pital facilities.3 An appealing feature of the Poisson assumption is the simplicity of the parameters; the census mean is equal to the census variance. The factor which has varied from application to application has been the amount of that variance to allow in sizing a particular facility. The first models used from three to four standard deviations which cor? responded to at least the 99.9 percentage point of the Poisson distribution. Later model build? ers reduced this coefficient. One low value used is 2.06 standard deviations, correspond? ing to the 98 percentage point of the normal approximation to the Poisson distribution:

https://www.jstor.org/stable/info/29771129

Simulation-based occupancy recommendations for adult medical/surgical units using admissions scheduling systems.

Simulation is used to investigate the effects on hospital occupancy of the number of beds in the facility, the percentage of patients who are emergencies, the percentage of elective patients who are scheduled, and the average lengths of stay of emergency and elective patients. A practical method is presented for estimating the optimum size of a short-term hospital on the basis of expected demand, and use of the results in planning is discussed.

J B Martin

Papers

Simulation-based occupancy recommendations for adult medical/surgical units using admissions scheduling systems.

Parameters affecting hospital occupancy and implications for facility sizing.