Introduction

The transformation of Digital Health enables to collect real-world data (RWD)¹ and real-world evidence (RWE) using digital technologies and advanced analytics. In this context and according to the joint HMA/EMA Big Data Task Force  [2] RWD/RWE can be considered a source for Big Data. The task force defines and describes Big Data from a regulatory perspective as follows:

'Big data' is a widely-used term without a commonly-accepted definition. The HMA/EMA Big Data Task Force defined big data as ‘extremely large datasets’ which may be complex, multi-dimensional, unstructured and heterogeneous, which are accumulating rapidly and which may be analysed computationally to reveal patterns, trends, and associations. In general, big data sets require advanced or specialised methods to provide an answer within reliable ‘constraints’.

A single dataset may not strictly meet the definition of big data but, when pooled or linked with other datasets, they become sufficiently large or complex to analyse to assume the characteristics of big data. Sources include real-world data (such as electronic health records, insurance claims data and data from patient registries), genomics, clinical trials, spontaneous adverse drug reaction reports, social media and wearable devices.

Medicines regulators will increasingly use insights derived from big data to assess the benefit-risk of medicines across their lifecycle.