Abstract

Background: Populations are under-served by local health policies and management of
resources. This partly reflects a lack of realistically complex models to enable appraisal of a
wide range of potential options. Rising computing power coupled with advances in machine
learning and healthcare information now enables such models to be constructed and
executed. However, such models are not generally accessible to public health practitioners
who often lack the requisite technical knowledge or skills.
Objectives: To design and develop a system for creating, executing and analysing the results
of simulated public health and healthcare policy interventions, in ways that are accessible
and usable by modellers and policy-makers.
Methods: The system requirements were captured and analysed in parallel with the
statistical method development for the simulation engine. From the resulting software
requirement specification the system architecture was designed, implemented and tested. A
model for Coronary Heart Disease (CHD) was created and validated against empirical data.
Results: The system was successfully used to create and validate the CHD model. The initial
validation results show concordance between the simulation results and the empirical data.
Conclusions: We have demonstrated the ability to connect health policy-modellers and
policy-makers in a unified system, thereby making population health models easier to share,
maintain, reuse and deploy.