Evidence map›Paper›PMID 31444871›Full record

ArticleJMIR medical informatics2019

Common Data Elements for Acute Coronary Syndrome: Analysis Based on the Unified Medical Language System.

Markus Kentgen, Julian Varghese, Alexander Samol, Johannes Waltenberger, Martin Dugas

Abstract read
In one paragraph

Article in JMIR medical informatics, 2019. 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.

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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.

Markus KentgenInstitute of Medical Informatics, University of Münster, Münster, Germany.ORCID http://orcid.org/0000-0001-7900-3738
Julian VargheseInstitute of Medical Informatics, University of Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-7206-3719
Alexander SamolMedical Faculty, University Hospital of Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-0143-3217
Johannes WaltenbergerMedical Faculty, University Hospital of Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-2417-9880
Martin DugasInstitute of Medical Informatics, University of Münster, Münster, Germany.ORCID http://orcid.org/0000-0001-9740-0788

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStandardization in clinical documentation can increase efficiency and can save time and resources.

objectiveThe objectives of this work are to compare documentation forms for acute coronary syndrome (ACS), check for standardization, and generate a list of the most common data elements using semantic form annotation with the Unified Medical Language System (UMLS).

methodsForms from registries, studies, risk scores, quality assurance, official guidelines, and routine documentation from four hospitals in Germany were semantically annotated using UMLS. This allowed for automatic comparison of concept frequencies and the generation of a list of the most common concepts.

resultsA total of 3710 forms items from 86 sources were semantically annotated using 842 unique UMLS concepts. Half of all medical concept occurrences were covered by 60 unique concepts, which suggests the existence of a core dataset of relevant concepts. Overlap percentages between forms were relatively low, hinting at inconsistent documentation structures and lack of standardization.

conclusionsThis analysis shows a lack of standardized and semantically enriched documentation for patients with ACS. Efforts made by official institutions like the European Society for Cardiology have not yet been fully implemented. Utilizing a standardized and annotated core dataset of the most important data concepts could make export and automatic reuse of data easier. The generated list of common data elements is an exemplary implementation suggestion of the concepts to use in a standardized approach.

Indexed as

acute coronary syndromecommon data elementsdocumentationstandardization

Identifiers

PMID31444871
PMCPMC6729118

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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.