A tutorial on simulation in health care: applications and issues

Standridge, C.R.

doi:10.1109/wsc.1999.823051

Cited by 13 publications

(12 citation statements)

References 17 publications

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“…3 In our setting, the immediate return of results from simulated changes in department staffing and resources were important in determining what improvements should be made. Similar models of patient care flow have been used in other hospital scenarios, such as operating rooms, with the aim of improved efficiency and patient care.…”

Section: Discussionmentioning

confidence: 99%

Computer Modeling of Patient Flow in a Pediatric Emergency Department Using Discrete Event Simulation

Hung¹,

Whitehouse²,

O’Neill³

et al. 2007

Pediatric Emergency Care

107

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Section: Discussionmentioning

confidence: 99%

Computer Modeling of Patient Flow in a Pediatric Emergency Department Using Discrete Event Simulation

Hung¹,

Whitehouse²,

O’Neill³

et al. 2007

Pediatric Emergency Care

107

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show abstract

“…Standridge [20] provides a tutorial on starting a simulation project in healthcare and discusses the modeling and experimental issues unique to health care applications, along with the barriers to the acceptance of simulation in this sector. Lowery [21] further discusses the challenges associated with conducting simulation studies in health care.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic bed balancing of an obstetrics hospital

Cochran

Bharti²

2006

Health Care Manage Sci

133

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Health care is one of the largest industries in the developed world and the top domestic industry in the United States (US). Over the past thirty years there has been a dramatic increase in healthcare costs in the US, of which about one-third can be attributed to hospital spending. One of the key factors in hospital cost containment and revenue enhancement is effective and efficient bed planning and capacity analysis. This study aims to balance bed unit utilizations across an obstetrics hospital and minimize the blocking of beds from upstream units within given constraints on bed reallocation. The methodology includes the assessment and effect of time-dependent patterns of monthly, daily, and hourly demand. Queuing networks are first used to assess the flows between units, establish target utilizations of bed units, and involve stakeholders in a flow characterization that they understand. Discrete-event simulation is then used to maximize the flow through the balanced system including non-homogeneous effects, non-exponential lengths of stay, and blocking behavior. Results of the models are validated against actual data collected from the hospital. Several 'what if' scenarios are studied showing that 38% more patient flow can be achieved with only 15% more patient beds. The results of the study have been implemented.

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“…Ivatts and Millard (2002) size geriatric medicine inpatient units, acknowledge the difficulty associated with data gathering and analysis, and offer a list of the key players in the hospital. Standridge (1999) provides a general overview of simulation applied to problems of public policy, capital expenditures, and patient processes in healthcare. We are motivated by the same concerns as these efforts, but believe that widespread application of solution techniques in hospitals requires a simpler and faster scientific foundation than simulation.…”

Section: Literature Reviewmentioning

confidence: 99%

A queuing-based decision support methodology to estimate hospital inpatient bed demand

Cochran

Roche

2008

Journal of the Operational Research Society

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Hospital inpatient bed capacity might be better described as evolved than planned. At least two challenges lead to this behaviour: different views of patient demand implied by different data sets in a hospital and limited use of scientific methods for capacity estimation. In this paper, we statistically examine four distinct hospital inpatient data sets for internal consistency and potential usefulness for estimating true patient bed demand. We conclude that posterior financial data, billing data, rather than the census data commonly relied upon, yields true hospital bed demand. Subsequently, a capacity planning tool, based upon queuing theory and financial data only, is developed. The delivery mechanism is an Excel spreadsheet. One adjusts input parameters including patient volume and mix and instantaneously monitors the effect on bed needs across multiple levels of care. A case study from a major hospital in Phoenix, Arizona, USA is used throughout to demonstrate the methodologies.

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A tutorial on simulation in health care: applications and issues

Cited by 13 publications

References 17 publications

Computer Modeling of Patient Flow in a Pediatric Emergency Department Using Discrete Event Simulation

Computer Modeling of Patient Flow in a Pediatric Emergency Department Using Discrete Event Simulation

Stochastic bed balancing of an obstetrics hospital

A queuing-based decision support methodology to estimate hospital inpatient bed demand

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