Evidence map›Paper›PMID 30322834›Full record

ArticleJournal of medical Internet research2018

Web-Based Information Infrastructure Increases the Interrater Reliability of Medical Coders: Quasi-Experimental Study.

Julian Varghese, Sarah Sandmann, Martin Dugas

Abstract read
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Article in Journal of medical Internet research, 2018. 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.

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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

3 authors.

Julian VargheseInstitute of Medical Informatics, University of Münster, Münster, Germany.ORCID 0000-0002-7206-3719
Sarah SandmannInstitute of Medical Informatics, University of Münster, Münster, Germany.ORCID 0000-0002-5011-0641
Martin DugasInstitute of Medical Informatics, European Research Center for Information Systems, Münster, Germany.ORCID 0000-0001-9740-0788

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical coding is essential for standardized communication and integration of clinical data. The Unified Medical Language System by the National Library of Medicine is the largest clinical terminology system for medical coders and Natural Language Processing tools. However, the abundance of ambiguous codes leads to low rates of uniform coding among different coders.

objectiveThe objective of our study was to measure uniform coding among different medical experts in terms of interrater reliability and analyze the effect on interrater reliability using an expert- and Web-based code suggestion system.

methodsWe conducted a quasi-experimental study in which 6 medical experts coded 602 medical items from structured quality assurance forms or free-text eligibility criteria of 20 different clinical trials. The medical item content was selected on the basis of mortality-leading diseases according to World Health Organization data. The intervention comprised using a semiautomatic code suggestion tool that is linked to a European information infrastructure providing a large medical text corpus of >300,000 medical form items with expert-assigned semantic codes. Krippendorff alpha (K

resultsThe intervention improved interrater reliability in structured quality assurance form items (from K

conclusionsThe Web-based code suggestion mechanism improved interrater reliability toward moderate or even substantial intercoder agreement. Precoordination and the use of structured versus free-text data elements are key drivers of higher interrater reliability.

Indexed as

Clinical CodingHumansInternetNatural Language ProcessingNon-Randomized Controlled Trials as TopicReproducibility of ResultsUnified Medical Language Systemclinical codingeligibility criteriahealth information interoperabilityUnified Medical Language System

Identifiers

PMID30322834
PMCPMC6231825

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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.