Evidence map›Paper›PMID 39040857›Full record

ArticleFrontiers in public health2024

Data quality and timeliness analysis for post-vaccination adverse event cases reported through healthcare data exchange to FDA BEST pilot platform.

Matthew Deady, Ray Duncan, Lance D Jones, Arianna Sang, Brian Goodness, Abhishek Pandey, Sylvia Cho, Richard A Forshee, Steven A Anderson, Hussein Ezzeldin

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

10 authors.

Matthew DeadyIBM Consulting, Washington, DC, United States.
Ray DuncanDepartment of Enterprise Information Services and Pediatrics, Los Angeles, Cedars-Sinai Health System, CA, United States.
Lance D JonesIBM Consulting, Washington, DC, United States.
Arianna SangIBM Consulting, Washington, DC, United States.
Brian GoodnessIBM Consulting, Washington, DC, United States.
Abhishek PandeyIBM Consulting, Washington, DC, United States.
Sylvia ChoCenter for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States.
Richard A ForsheeCenter for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States.
Steven A AndersonCenter for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States.
Hussein EzzeldinCenter for Biologics Evaluation and Research, United States Food and Drug Administration, Silver Spring, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality. Materials and methods: The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR. Results: The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met. Discussion: This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

Indexed as

Data AccuracyUnited States Food and Drug AdministrationAdolescentAdultAdverse Drug Reaction Reporting SystemsElectronic Health RecordsFemaleHealth Information ExchangeHumansMaleMiddle AgedPilot ProjectsProduct Surveillance, PostmarketingTime FactorsUnited StatesVaccinationadverse event (AE)data qualityelectronic health record (EHR)fast healthcare interoperability resources (FHIR)health information technologyinteroperabilitypublic healthreal-world data (RWD)

Identifiers

PMID39040857
PMCPMC11260708

What OpenQuestion holds

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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.