Solving The Rubik's Cube Of Competing Forces To Realize The Future Of Clinical Research Part 1: AI And Data Quality In A Decentralized World

By Steve Young, CluePoints

Harnessing the significant potential of artificial intelligence (AI) is crucial for advancing clinical research, as indicated by a 2022 scoping review highlighting AI's positive impact on clinical trial efficacy, safety, and cost containment. While machine learning contributes to accelerating recruitment, improving patient selection, and addressing data quality issues, the reliance on patient-reported data in hybrid and decentralized clinical trials (DCTs) poses its own set of challenges.

By pulling information from wearables, patient-reported outcomes (PROs), and home health, data collection has significantly increased and is set to rise even further before the end of the decade. This will lead to increased data volume, both in terms of sources and data points, further emphasizing a need for AI-driven risk-based quality management (RBQM).

Read the full article to learn why adopting RBQM will be essential in ensuring the accuracy of data from diverse sources and how it will ultimately shape the future of clinical research.