Evidence map›Paper›PMID 32308887›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019

Towards augmenting structured EHR data: a comparison of manual chart review and patient self-report.

Nicole G Weiskopf, Aaron M Cohen, Joely Hannan, Thad Jarmon, David A Dorr

Open access · greenAbstract readComparative Study
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 6% of its field
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

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.

  1. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Pooled it
  2. Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023
    Pooled it
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  14. Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.Journal of the American Medical Informatics Association : JAMIA · 2023
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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

5 authors at 1 institution in 1 country.

Nicole G WeiskopfDepartment of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.
Aaron M CohenDepartment of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.
Joely HannanDepartment of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.
Thad JarmonDepartment of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.
David A DorrDepartment of Medical Informatics and Clinical Epidemiology, OHSU, Portland, OR.
University of Portland · US

Funding

Measuring and improving data quality for clinical quality measure reliabilityK01LM012738 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WEISKOPF, NICOLE GRAY · 2017 to 2019
$462k
NLM NIH HHS K01 LM012738
6 · The paper itself

Abstract

Structured electronic health record (EHR) data are often used for quality measurement and improvement, clinical research, and other secondary uses. These data, however, are known to suffer from quality problems. There may be value in augmenting structured EHR data to improve data quality, thereby improving the reliability and validity of the conclusions drawn from those data. Focusing on five diagnoses related to cardiovascular care, this paper considers the added value of two alternative data sources: manual chart abstraction and patient self-report. We assess the overall agreement between structured EHR problem list data, abstracted EHR data, and patient self- report; and explore possible causes of disagreement between those sources. Our findings suggest that both chart abstraction and patient self-report contain significantly more diagnoses than the problem list, but that the information they capture is different. Methods for collecting and validating self-reported medical data require further consideration and exploration.

Indexed as

Electronic Health RecordsInformation Storage and RetrievalSelf ReportAdultAgedAged, 80 and overData AccuracyFemaleHumansMaleMedical Records, Problem-OrientedMiddle AgedReproducibility of ResultsYoung Adult

Identifiers

PMID32308887
PMCPMC7153078
OpenAlexW3028148690

What OpenQuestion holds

Textmetadata
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.