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Special Communication
Integrating Clinical Trials and Practice
August5, 2024
Joseph B.Franklin,JD, PhD1; CarolineMarra,PhD1; Kaleab Z.Abebe,PhD2; et al Atul J.Butte,MD, PhD3,4; Deborah J.Cook,MD5; LauraEsserman,MD, MBA6; Lee A.Fleisher,MD7; Cynthia I.Grossman,PhD8; Nancy E.Kass,ScD9; Harlan M.Krumholtz,MD, SM10; KathyRowan,PhD11,12; Amy P.Abernethy,MD, PhD13; for the JAMA Summit on Clinical Trials Participants
Author Affiliations Article Information
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1Verily Life Sciences, South San Francisco, California
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2Center for Biostatistics & Qualitative Methodology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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3Bakar Computational Health Sciences Institute, University of California, San Francisco
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4Center for Data-Driven Insights and Innovation, University of California Health, Oakland
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5Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
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6Departments of Surgery and Radiology and Institute for Health Policy Studies, University of California, San Francisco
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7Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia
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8Biogen, Boston, Massachusetts
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9Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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10Yale University School of Medicine, New Haven, Connecticut
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11National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research Programme, London, United Kingdom
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12Intensive Care National Audit & Research Centre (ICNARC), London, United Kingdom
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13Highlander Health, Dallas, Texas
JAMA. Published online August 5, 2024. doi:10.1001/jama.2024.0268
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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.
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Research, Methods, Statistics Ophthalmology Neonatology Pediatrics Reproductive Health
Citation
Franklin JB, Marra C, Abebe KZ, et al. Modernizing the Data Infrastructure for Clinical Research to Meet Evolving Demands for Evidence. JAMA. Published online August 05, 2024. doi:10.1001/jama.2024.0268
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