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Modernizing the Data Infrastructure for Clinical Research to Meet Evolving Demands for Evidence.
Franklin, Joseph B; Marra, Caroline; Abebe, Kaleab Z; Butte, Atul J; Cook, Deborah J; Esserman, Laura; Fleisher, Lee A; Grossman, Cynthia I; Kass, Nancy E; Krumholz, Harlan M; Rowan, Kathy; Abernethy, Amy P.
Afiliação
  • Franklin JB; Verily Life Sciences, South San Francisco, California.
  • Marra C; Verily Life Sciences, South San Francisco, California.
  • Abebe KZ; Center for Biostatistics & Qualitative Methodology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Butte AJ; Bakar Computational Health Sciences Institute, University of California, San Francisco.
  • Cook DJ; Center for Data-Driven Insights and Innovation, University of California Health, Oakland.
  • Esserman L; Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
  • Fleisher LA; Departments of Surgery and Radiology and Institute for Health Policy Studies, University of California, San Francisco.
  • Grossman CI; Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia.
  • Kass NE; Biogen, Boston, Massachusetts.
  • Krumholz HM; Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
  • Rowan K; Yale University School of Medicine, New Haven, Connecticut.
  • Abernethy AP; National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research Programme, London, United Kingdom.
JAMA ; 2024 08 05.
Article em En | MEDLINE | ID: mdl-39102333
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article