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.