Abstract
Moving toward new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines' effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V's of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V's whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.
Highlights
Real-world data (RWD) collected throughout the medicinal product life cycle will enable more flexible forms of access to innovative medicines, as well as adaptive pathways for their development [1], for example, by providing relative treatment effectiveness evidence during the intensive regulatory processes related to market indication approval, pricing, and reimbursement [2]
Two types of real-world data (RWD) studies can provide important insights: Hypothesis Evaluating Treatment Effectiveness (HETE) studies, which test a specific hypothesis in a specific population using research-driven data, and Exploratory Treatment Effectiveness (ExTE) studies, or data-driven research that seeks to learn more about possible treatment effectiveness [4]
For this to happen there are still three important aspects that need attention based on our own experience: (i) the databases should aim for reusability on an international scale, accessible through a common data model (CDM), facilitating both HETE and ExTE; (ii) the information systems should be able to analyze RWD on an ongoing basis to support value-based healthcare, facilitating outcomebased managed entry agreements based on HETE studies; and (iii) the databases should become longitudinally oriented to investigate long-term treatment effects for the most innovative medicines for both HETE and ExTE studies
Summary
Real-world data (RWD) collected throughout the medicinal product life cycle will enable more flexible forms of access to innovative medicines, as well as adaptive pathways for their development [1], for example, by providing relative treatment effectiveness evidence during the intensive regulatory processes related to market indication approval, pricing, and reimbursement [2]. By generating RWE using such ExTE studies, big data analytics can provide new and powerful insights into the effectiveness and performance of products among their specific real-world population and healthcare systems [5].
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