Evidence map›Paper›PMID 35036557›Full record

ArticleLearning health systems2022

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.

Lisa Bastarache, Jeffrey S Brown, James J Cimino, David A Dorr, Peter J Embi, Philip R O Payne, Adam B Wilcox, Mark G Weiner

Abstract read
In one paragraph

Article in Learning health systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

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

  1. Pooled it
  2. Article
  3. Distinct Patterns of Mobility Recovery After Stroke Using Routine Clinical Data.medRxiv : the preprint server for health sciences · 2026
    Article
  4. Extending the Fundamental Theorem of Biomedical Informatics for the AI era.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  5. Article
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  16. Article
  17. Article
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  19. Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.Genetics in medicine : official journal of the American College of Medical Genetics · 2023
    Article
  20. Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.Journal of the American Medical Informatics Association : JAMIA · 2023
    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.

Lisa BastaracheDepartment of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA.
Jeffrey S BrownDepartment of Population Medicine Harvard Medical School and Harvard Pilgrim Health Care Institute Boston Massachusetts USA.ORCID https://orcid.org/0000-0002-9340-7189
James J CiminoInformatics Institute, University of Alabama at Birmingham Birmingham Alabama USA.ORCID https://orcid.org/0000-0003-4101-1622
David A DorrDepartment of Medical Informatics and Clinical Epidemiology Oregon Health Sciences University Portland Oregon USA.
Peter J EmbiCenter for Biomedical Informatics Regenstrief Institute Indianapolis Indiana USA.
Philip R O PayneInstitute for Informatics, Washington University in St. Louis St. Louis Missouri USA.ORCID https://orcid.org/0000-0002-9532-2998
Adam B WilcoxInstitute for Informatics Washington University in St. Louis School of Medicine St. Louis Missouri USA.
Mark G WeinerDepartment of Population Health Sciences Weill Cornell Medicine New York New York USA.ORCID https://orcid.org/0000-0001-5586-9940

Funding

From GWAS to PheWAS: Scanning the EMR Phenome for Gene-disease AssociationsR01LM010685 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BASTARACHE, LISA · 2011 to 2022
$6.3M
NLM NIH HHS R01 LM010685
6 · The paper itself

Abstract

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Indexed as

data sciencereal‐world datareal‐world evidence

Identifiers

PMID35036557
PMCPMC8753316

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.