Evidence map›Paper›PMID 39102333›Full record

ArticleJAMA2024

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 Esserman, Lee A Fleisher, Cynthia I Grossman, Nancy E Kass, Harlan M Krumholz and 3 more

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In one paragraph

Article in JAMA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Improved Survival and Prognostication in Melanoma Patients With Brain Metastases: An Update of the Melanoma Graded Prognostic Assessment.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2025
    Article
  9. Review
  10. Article
  11. Article
  12. Advances, Overuse, and Shared Decision-Making in Abdominal Wall Surgery.Journal of abdominal wall surgery : JAWS · 2025
    Article
  13. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Joseph B FranklinVerily Life Sciences, South San Francisco, California.
Caroline MarraVerily Life Sciences, South San Francisco, California.
Kaleab Z AbebeCenter for Biostatistics & Qualitative Methodology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco.
Deborah J CookFaculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Laura EssermanDepartments of Surgery and Radiology and Institute for Health Policy Studies, University of California, San Francisco.
Lee A FleisherAnesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia.
Cynthia I GrossmanBiogen, Boston, Massachusetts.
Nancy E KassJohns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Harlan M KrumholzYale University School of Medicine, New Haven, Connecticut.
Kathy RowanNational Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research Programme, London, United Kingdom.
Amy P AbernethyHighlander Health, Dallas, Texas.
JAMA Summit on Clinical Trials Participants

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID39102333

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.