e-Labs and the stock of health method for simulating health policies.
Regional outcomes of national health policies are difficult to forecast. This is partly due to a lack of realistically complex models that can be used to appraise policy options and partly a lack of accessible and adaptable tools that can be used to simulate the consequences of policy decisions. These barriers might be overcome by exploiting the commoditization of massively parallel computing architectures, advances in machine learning, and the increased availability of large-scale linked healthcare data. This paper presents a novel modelling methodology, The Stock of Health, for harnessing emerging data and computational resources to simulate health policy, with application initially to coronary heart disease. We detail the use of multi-core graphical processing architectures to facilitate a micro-simulation approach. The simulation tools have been deployed through the IMPACT Framework. We explore how this framework can be extended to support the sharing and reuse of policy models and simulations based on the digital publishing concept of e-Lab.
Centre for Health Informatics, Institute of Population Health, University of Manchester and Manchester Academic Health Science Centre, UK.
Philip
P
Couch
Martin
M
O'Flaherty
Matthew
M
Sperrin
Benjamin
B
Green
Panagiotis
P
Balatsoukas
Stephen
S
Lloyd
James
J
McGrath
Claudia
C
Soiland-Reyes
John
J
Ainsworth
Simon
S
Capewell
Iain
I
Buchan
D003198:Computer Simulation; D003324:Coronary Artery Disease / Q000453:epidemiology* / Q000517:prevention & control*; D006291:Health Policy; D006293:Health Promotion; D006801:Humans; D008962:Models, Theoretical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D015397:Program Evaluation / Q000379:methods*; D012307:Risk Factors; D006113:United Kingdom / Q000453:epidemiology