The Health Databank comprises all data aspects related to the system.  This includes not only the management of data, but also the analytics. Some of the analytical processes will be automated using machine learning techniques.  Such techniques will allow not only the end user to extract data for informed decisions, but also to make provision for outcome predictions.

Distributed computing architectures are now the providing platforms for Big Data analytics, very suitable for the types of data being produced in the mICF context. These architectures allow for real-time data interaction (including probing the data in an iterative manner). Being distributed and typically hosted in shared computing resources accessed by the internet, i.e. ‘cloud computing’, allows very short response times, much less than dealing with data in a centralised warehouse. One of the biggest advantages of modern Big Data architectures is the fact that they can handle complex content. This is important in the mICF context, since we will be processing both structured and unstructured data. Structured data will be presented as ICF codes, but some interfaces will generate additional (unstructured) data which will inform the analytic (machine learning) models. From a cost perspective, the modern analytic architectures are also more affordable, since they are typically built on hosted services using open source software components.

The necessary measures for national privacy and personal data security laws will be put in place to ensure the protection and privacy of person-owned, as well as person-shared data.  Standard measures that incorporate not only compliance, but also interoperability, are to be implemented so as not to widen existing population-level inequities.