Evidence map›Paper›PMID 40979389›Full record

ReviewFrontiers in drug safety and regulation2024

Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review.

Julie V Kim, Sharon E Davis, Michael E Matheny, Joshua C Smith

Abstract readReview
In one paragraph

Review in Frontiers in drug safety and regulation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
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

4 authors.

Julie V KimDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
Sharon E DavisDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
Michael E MathenyDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
Joshua C SmithDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.

Funding

Overall: Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Center at VanderbiltP50HD103537 · NICHD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Jeffrey L Neul · 2020 to 2026
$10.3M
NICHD NIH HHS P50 HD103537
6 · The paper itself

Abstract

Electronic health records (EHRs) have emerged as resources for both the identification of adverse drug events (ADEs) and general population health surveillance, however questions remain around how best to utilize EHR data for drug safety signal identification. While the majority of signal identification research has utilized spontaneous reports and health insurance claims, these data also have limitations. Pharmacovigilance approaches combining EHR data with other data sources have the potential to address many of the shortcomings of individual sources. This mini-review seeks to provide an overview of some of the recent research leveraging EHR data in combination with spontaneous reports, claims data, and other pharmacovigilance data sources for drug safety signal identification. Studies have shown that combining EHR data with these and other sources is often beneficial compared to the use of a single source alone, however the synergism or friction introduced is insufficiently explored in current literature. Our review explores how EHR data benefits signal identification when used in combination with other sources, what methods have been applied, and what considerations have previously been noted. Finally, we identify gaps in current research and highlight important considerations for future work using multiple real world data sources for drug safety surveillance.

Indexed as

adverse drug eventselectronic health recordspharmacovigilancesafety signal identificationspontaneous reporting systems

Identifiers

PMID40979389
PMCPMC12443085

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.