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

Fone, David Lawrence; Hollinghurst, Sandra; Temple, M. L.; Round, Alison; Lester, Nathan; Weightman, Alison Lesley; Roberts, Katherine J.; Coyle, Edward F.; Bevan, Gwyn; Palmer, Stephen

doi:10.1093/pubmed/fdg075

Cited by 452 publications

(308 citation statements)

References 55 publications

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“…There is much discussion on the use and potential use of simulation in health with applications ranging across planning, scheduling, reorganisation and management of healthcare and hospital services, communicable diseases, bio-terrorism, screening, costs of illness, economic evaluation (comparing alternative healthcare interventions), policy and strategy evaluation, and performance modelling (Fone et al, 2003;Mustafee et al, 2010;Günal and Pidd, 2010). Comprehensive reviews of the use of simulation in health are provided by Jun et al (1999), Tunnicliffe Wilson (1981), Fone et al (2003), Jacobson et al (2006), Sobolev et al (2011) andvan Lent et al (2012).…”

Section: The Challenges Of Simulation In Healthmentioning

confidence: 99%

“…Comprehensive reviews of the use of simulation in health are provided by Jun et al (1999), Tunnicliffe Wilson (1981), Fone et al (2003), Jacobson et al (2006), Sobolev et al (2011) andvan Lent et al (2012).…”

Section: The Challenges Of Simulation In Healthmentioning

confidence: 99%

“…Complexity also arises from uncertainty over the needs of patients (Fone et al, 2003) and from the need to address multiple, competing objectives (Harper and Shahani, 2002).…”

Section: Development Of the Questionnairementioning

confidence: 99%

See 2 more Smart Citations

Is simulation in health different?

Tako

Robinson

2015

Journal of the Operational Research Society

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It is often stated that health simulation is quite different and even that it is more difficult than in other sectors. But, is simulation in health really different to simulation in other sectorselsewhere? In this paper we explore this question through a survey of simulation modellers and academics. We elicit their opinions across a range of factors concerning the difficulties of health modelling against modelling in other domains. The analysis considers the responses of the whole group of respondents and the sub-group of respondents who have experience both in and outside of health modelling. The results show that, overall, there is a perception that health modelling is different and that it is more difficult across a range of factors. The implications for simulation research and practice in health are discussed.

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Section: The Challenges Of Simulation In Healthmentioning

confidence: 99%

Section: The Challenges Of Simulation In Healthmentioning

confidence: 99%

See 1 more Smart Citation

Is simulation in health different?

Tako

Robinson

2015

Journal of the Operational Research Society

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“…This work is consonant with earlier findings. Fone et al [37] presented an extended literature review of OR applications in healthcare and concluded that the quality of models had improved from 1980 until 1999 but noted that most models analyse the micro level of the system: specific aspects of some part of healthcare systems, such as Accident and Emergency (A&E) or the departments of a hospital. They point to the lack of more generic models with wider applicability.…”

Section: Literature Reviewmentioning

confidence: 99%

A generic method to develop simulation models for ambulance systems

Pinto

Silva

2015

Simulation Modelling Practice and Theory

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In this paper, we address the question of generic simulation models and their role in improving emergency care around the world. After reviewing the development of ambulance models and the contexts in which they have been applied, we report the construction of a reusable model for ambulance systems. Further, we describe the associated parameters, data sources, and performance measures, and report on the collection of information, as well as the use of optimisation to configure the service to best effect. Having developed the model, we have validated it using real data from the emergency medical system in a Brazilian city, Belo Horizonte. To illustrate the benefits of standardisation and reusability we apply the model to a UK context by exploring how different rules of engagement would change the performance of the system. Finally, we consider the impact that one might observe if such rules were adopted by the Brazilian system.

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“…Some of these [5,[9][10][11][12][13] are queueing models, others [14][15][16][17][18][19] utilise simulation or population/ ratio based approaches which use average lengths of stay to quantify the number of patients requiring care resources, for example [20][21][22][23][24]. However, some of these methods, which are mostly stochastic or simulation based, are computationally very complex and require substantial data and logistics [25].…”

Section: Introductionmentioning

confidence: 99%

A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system

Garg

McClean

Meenan

et al. 2009

Health Care Manag Sci

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Healthcare resource planners need to develop policies that ensure optimal allocation of scarce healthcare resources. This goal can be achieved by forecasting daily resource requirements for a given admission policy. If resources are limited, admission should be scheduled according to the resource availability. Such resource availability or demand can change with time. We here model patient flow through the care system as a discrete time Markov chain. In order to have a more realistic representation, a nonhomogeneous model is developed which incorporates timedependent covariates, namely a patient's present age and the present calendar year. The model presented in this paper can be used for admission scheduling, resource requirement forecasting and resource allocation, so as to satisfy the demand or resource constraints or to meet the expansion or contraction plans in a hospital and community based integrated care system. Such a model can be used with both fixed and variable numbers of admissions per day and should prove to be a useful tool for care managers and policy makers who require to make strategic management decisions. We also describe an application of the model to an elderly care system, using a historical dataset from the geriatric department of a London hospital.

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

Cited by 452 publications

References 55 publications

Is simulation in health different?

Is simulation in health different?

A generic method to develop simulation models for ambulance systems

A non-homogeneous discrete time Markov model for admission scheduling and resource planning in a cost or capacity constrained healthcare system

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