Litcius/Paper detail

Modernizing the Data Infrastructure for Clinical Research to Meet Evolving Demands for Evidence

Joseph B. Franklin, Caroline Marra, Kaleab Z. Abebe, Atul J. Butte, Deborah J. Cook, Laura J. Esserman, Lee A. Fleisher, Cynthia Grossman, Nancy Kass, Harlan M. Krumholz, Kathy Rowan, Amy P. Abernethy, Ali Abbasi, Kaleab Z. Abebe, Amy P. Abernethy, Stacey J. Adam, Derek C. Angus, Jamy D. Ard, Rachel Bender Ignacio, Michael Berkwits, Scott Berry, Deepak L. Bhatt, Kirsten Bibbins‐Domingo, Robert O. Bonow, Marc Bonten, Sharon A. Brangman, John S. Brownstein, Melinda Buntin, Atul J. Butte, Robert M. Califf, Marion Campbell, Anne Rentoumis Cappola, Anne C. Chiang, Deborah Cook, Steven R. Cummings, Gregory Curfman, Laura J. Esserman, Lee A. Fleisher, Joseph B. Franklin, Ralph Gonzalez, Cynthia Grossman, Tufia C. Haddad, Roy S. Herbst, Adrian F. Hernandez, Diane Holder, Leora Horn, Grant D. Huang, Alison Huang, Nancy Kass, Rohan Khera, Walter J. Koroshetz, Harlan M. Krumholz, Martin Landray, Roger Lewis, Tracy A. Lieu, Preeti Malani, Christa Lese Martin, Mark McClellan, Mary Mcdermott, Stephanie R. Morain, Susan A. Murphy, Stuart G. Nicholls, Stephen J. Nicholls, Peter J. O’Dwyer, Bhakti K. Patel, Eric D. Peterson, Sheila A. Prindiville, Joseph S. Ross, Kathy Rowan, Gordon D. Rubenfeld, Christopher Seymour, Rod S Taylor, Joanne Waldstreicher, Tracy Y. Wang

2024JAMA25 citationsDOI

Abstract

Importance: The ways in which we access, acquire, and use data in clinical trials have evolved very little over time, resulting in a fragmented and inefficient system that limits the amount and quality of evidence that can be generated. Observations: Clinical trial design has advanced steadily over several decades. Yet the infrastructure for clinical trial data collection remains expensive and labor intensive and limits the amount of evidence that can be collected to inform whether and how interventions work for different patient populations. Meanwhile, there is increasing demand for evidence from randomized clinical trials to inform regulatory decisions, payment decisions, and clinical care. Although substantial public and industry investment in advancing electronic health record interoperability, data standardization, and the technology systems used for data capture have resulted in significant progress on various aspects of data generation, there is now a need to combine the results of these efforts and apply them more directly to the clinical trial data infrastructure. Conclusions and Relevance: We describe a vision for a modernized infrastructure that is centered around 2 related concepts. First, allowing the collection and rigorous evaluation of multiple data sources and types and, second, enabling the possibility to reuse health data for multiple purposes. We address the need for multidisciplinary collaboration and suggest ways to measure progress toward this goal.

Topics & Concepts

MedicineClinical trialQuality (philosophy)Data qualityRisk analysis (engineering)Data scienceOperations managementPathologyComputer scienceEpistemologyMetric (unit)PhilosophyEconomicsElectronic Health Records SystemsEthics in Clinical ResearchResearch Data Management Practices