Real-World Evidence Gathering in Oncology: The Need for a Biomedical Big Data Insight-Providing Federated Network

Area of Science:

• Biomedical data science
• Pharmaceutical research
• Health informatics

Background:

• Real-world data (RWD) is crucial for innovative medicine development and access.
• Big data analytics on RWD can yield significant insights into drug effectiveness.
• The healthcare ecosystem faces challenges in leveraging big data analytics for real-world evidence (RWE) generation.

Purpose of the Study:

• To explore the critical data management factors for successful real-world data analytics.
• To differentiate requirements for exploratory (ExTE) versus hypothesis-evaluating (HETE) treatment effectiveness studies.
• To propose solutions for a robust biomedical big data ecosystem.

Main Methods:

• Analysis of RWD challenges within the context of the four V's (Volume, Velocity, Variety, Veracity).
• Distinction between exploratory (ExTE) and hypothesis-evaluating (HETE) study designs for treatment effectiveness.
• Identification of key components for a biomedical big data ecosystem.

Main Results:

• Data Variety and Veracity are essential for both ExTE and HETE studies.
• ExTE studies require high data Volume, while HETE studies necessitate high Velocity.
• Essential components for the biomedical big data ecosystem include international data reusability, real-time processing, and longitudinal data.

Conclusions:

• A federated RWD infrastructure on a common data model is proposed to manage the four V's while ensuring patient privacy.
• This infrastructure enables decentralized data analysis for improved RWE generation.
• Addressing data management and infrastructure needs is vital for advancing adaptive medicine development pathways.