Evidence map›Paper›PMID 38740298›Full record

ArticleContemporary clinical trials2024

Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials - A case study.

Denise Esserman, Erich J Greene, Nancy K Latham, Michael Kane, Charles Lu, Peter N Peduzzi, Thomas M Gill, David A Ganz

Registry-linked trialAbstract read
In one paragraph

Article in Contemporary clinical trials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02475850 (Randomized Trial of a Multifactorial Fall Injury Prevention Strategy), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT02475850 phase3completednot on this map

Randomized Trial of a Multifactorial Fall Injury Prevention Strategy

TypeinterventionalSponsorBrigham and Women's HospitalRan2015 to 2020Enrolled5,451ConditionsAccidental Falls, Wounds and InjuriesArmsEvidence-based tailored fall prevention, Usual care
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Denise EssermanDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA. Electronic address: denise.esserman@yale.edu.
Erich J GreeneDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Nancy K LathamBoston Claude D. Pepper Older Americans Independence Center; Research Program in Men's Health: Aging and Metabolism; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Michael KaneDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Charles LuBiomedical Informatics and Data Science, Yale School of Medicine, USA.
Peter N PeduzziDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Thomas M GillDepartment of Internal Medicine, Yale School of Medicine, USA.
David A GanzDepartment of Medicine, David Geffen School of Medicine at UCLA, Panama; Geriatric Research, Education and Clinical Center, VA Greater Los Angeles Healthcare System, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Albert C Shaw · 2002 to 2026
$37.9M
Randomized Trial of a Multifactorial Fall Injury Prevention Strategy-SupplementU01AG048270 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BHASIN, SHALENDER, GILL, THOMAS MICHAEL · 2014 to 2019
$34.8M
Enhancing the Efficiency of Pragmatic Clinical Trials Using Administrative Data: Analysis of the STRIDE StudyR01AG071528 · NIA · YALE UNIVERSITY · PI ESSERMAN, DENISE, GANZ, DAVID · 2022 to 2025
$2.0M
NCATS NIH HHS UL1 TR001863NIA NIH HHS P30 AG021342NIA NIH HHS R01 AG071528NIA NIH HHS U01 AG048270
6 · The paper itself

Abstract

backgroundVariable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach.

methodsWe propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites).

resultsThe site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site.

conclusionsWe suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials. ORIGINAL TRIAL REGISTRATION NUMBER: NCT02475850.

Indexed as

ChecklistElectronic Health RecordsAgedClinical Trials as TopicData AccuracyHumansPrimary Health CareChecklistElectronic health recordsOutcome ascertainmentPragmatic clinical trialsSTRIDE trial

Identifiers

PMID38740298
PMCPMC11654751

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Registered trials

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.