Evidence map›Paper›PMID 33948239›Full record

ArticleJournal of clinical and translational science2020

Development of an electronic health records datamart to support clinical and population health research.

Jillian H Hurst, Yaxing Liu, Pamela J Maxson, Sallie R Permar, L Ebony Boulware, Benjamin A Goldstein

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

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  9. Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
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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

6 authors.

Jillian H HurstDuke Children's Health and Discovery Institute, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.ORCID https://orcid.org/0000-0001-5079-9920
Yaxing LiuDuke Health Technology Solutions, Durham, NC, USA.
Pamela J MaxsonDuke Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC, USA.
Sallie R PermarDuke Children's Health and Discovery Institute, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.
L Ebony BoulwareDuke Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC, USA.
Benjamin A GoldsteinDuke Children's Health and Discovery Institute, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
Multifactorial spatiotemporal analyses to evaluate environmental triggers and patient-level clinical characteristics of severe asthma exacerbations in childrenR21HL145415 · NHLBI · DUKE UNIVERSITY · PI GOLDSTEIN, BENJAMIN ALAN, LANG, JASON E. · 2019 to 2020
$241k
NCATS NIH HHS UL1 TR002553NHLBI NIH HHS R21 HL145415
6 · The paper itself

Abstract

introductionElectronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators.

methodsThe CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information.

resultsWe provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource.

conclusionsThe CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.

Indexed as

common data modelElectronic health recordsPCORnet

Identifiers

PMID33948239
PMCPMC8057430

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.