An evaluation of the applicability of system dynamics to patient flow modelling

Vanderby, Sonia; Carter, Michael

doi:10.1057/jors.2009.150

Cited by 35 publications

(32 citation statements)

References 16 publications

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“…This might be compared to a study of health care where, by and large, both the medical provider and patient aspects are either well documented or can be monitored. This observation, and the preceding discussion exemplify why the extensive literature on modelling, for example, the health system, including discrete-event simulation (DES) leading to systems dynamics-based optimisation (Vanderby and Carter 2010;Smits 2010;Davies et al 2000;Fone et al 2003;Robinson 2005) is not applicable to this case.…”

Section: Information Availabilitymentioning

confidence: 95%

Empirical modelling methods to identify issues of concern in a two-sided military system

Curtis

Shrivastava

Moon

2014

EURO Journal on Decision Processes

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We propose the notion of a 'substrate' model which can be used as a common and agreed picture to develop issues of concern in the problem-structuring phase of a study. Such a process provides a transparent and defensible means to develop prioritised options for consideration by decision makers. In this paper, we explore two empirical methods, one based on sequential examination of events in a process and the other uses a functional (influence diagram like) approach to explore how system features change with time. An example is taken from a current problem in the military sphere-the two-sided interaction involving the use of, and defence against, improvised explosive devices. In this paper, we illustrate the strengths and weaknesses of the two modelling methods, the complementarity of the use of a more concrete 'substrate model' with the 'softer' aspects of problem structuring and the implications of the clients' needs, in particular the position of the study within the typical Defence capability development life cycle. We believe that such empirical methods will have practical application to similar studies at the 'front-end'.

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Section: Information Availabilitymentioning

confidence: 95%

Empirical modelling methods to identify issues of concern in a two-sided military system

Curtis

Shrivastava

Moon

2014

EURO Journal on Decision Processes

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“…Simulation is by far the most common methodological tool used in support of these types of studies [17][18][19]. Coté [20] used discrete event simulation (SIMAN IV) at an outpatient clinic to describe and quantify issues related to patient flow and resource utilization for an individual physician.…”

Section: Simulationmentioning

confidence: 99%

“…They found that the maximum number of patients that could be served was linearly related to the number of procedure rooms, with a fixed room-toendoscopist ratio, and that changes in the patient arrival schedule could reduce patient waiting time without requiring a longer clinic day. For other applications of discrete event simulation in healthcare, see, e.g., [23,24]; for similar applications using system dynamics, see, e.g., [19,25].…”

Section: Simulationmentioning

confidence: 99%

Improving patient flow at a family health clinic

Bard

Shu

Morrice

et al. 2014

Health Care Manag Sci

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This paper presents an analysis of a residency primary care clinic whose majority of patients are underserved. The clinic is operated by the health system for Bexar County and staffed primarily with physicians in a three-year Family Medicine residency program at The University of Texas School of Medicine in San Antonio. The objective of the study was to obtain a better understanding of patient flow through the clinic and to investigate changes to current scheduling rules and operating procedures. Discrete event simulation was used to establish a baseline and to evaluate a variety of scenarios associated with appointment scheduling and managing early and late arrivals. The first steps in developing the model were to map the administrative and diagnostic processes and to collect time-stamped data and fit probability distributions to each. In conjunction with the initialization and validation steps, various regressions were performed to determine if any relationships existed between individual providers and patient types, length of stay, and the difference between discharge time and appointment time. The latter two statistics along with resource utilization and closing time were the primary metrics used to evaluate system performance.The results showed that up to an 8.5 % reduction in patient length of stay is achievable without noticeably affecting the other metrics by carefully adjusting appointment times. Reducing the no-show rate from its current value of 21.8 % or overbooking, however, is likely to overwhelm the system's resources and lead to excessive congestion and overtime. Another major finding was that the providers are the limiting factor in improving patient flow. With an average utilization rate above 90 % there is little prospect in shortening the total patient time in the clinic without reducing the providers' average assessment time. Finally, several suggestions are offered to ensure fairness when dealing with out-of-order arrivals.

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“…The quantitative SD approach to simulation is viable where the model can be validated against a real--world foundation of data and measurable relationships or processes. Several recent examples of quantitative and qualitative applications in specific healthcare domains are available in the literature (Vanderby and Carter 2009;Wong et al 2012). This paper describes a whole--systems SD approach to modelling and evaluation of healthcare sustainability, at the macro level of a public healthcare system.…”

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confidence: 99%

System dynamics modelling to support policy analysis for sustainable health care

Lyons

Duggan

2015

Journal of Simulation

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System Dynamics (SD) is an established simulation methodology used to explore the behavior of social systems over time. The field has addressed challenging sustainability problems in fisheries, urban planning, and environmental resource management. It has also been successfully applied to healthcare, in chronic disease modelling and workforce planning. This paper presents system dynamics models of healthcare sustainability, and illustrates two complementary applications of SD: (i) continuous simulation of healthcare infrastructure adequacy; and (ii) conceptual modelling of the wider public policy context for healthcare sustainability. The infrastructure model provides a simulator for evaluating impacts of population growth and ageing, as well as assessing the likely effects of policy interventions on system sustainability. This model is validated using empirical data from Ireland's public health service, and its practical application for sustainability analysis is illustrated. Our conceptual endogenous SD model explores a wider system boundary and public policy interdependencies that impact sustainability outcomes.

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An evaluation of the applicability of system dynamics to patient flow modelling

Cited by 35 publications

References 16 publications

Empirical modelling methods to identify issues of concern in a two-sided military system

Empirical modelling methods to identify issues of concern in a two-sided military system

Improving patient flow at a family health clinic

System dynamics modelling to support policy analysis for sustainable health care

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