Evidence map›Paper›PMID 39493629›Full record

ArticleDigital health

Are ICD codes reliable for observational studies? Assessing coding consistency for data quality.

Stuart J Nelson, Ying Yin, Eduardo A Trujillo Rivera, Yijun Shao, Phillip Ma, Mark S Tuttle, Jennifer Garvin, Qing Zeng-Treitler

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 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

8 authors.

Stuart J NelsonBiomedical Informatics Center, George Washington University, Washington, DC, USA.ORCID https://orcid.org/0000-0002-8756-0179
Ying YinBiomedical Informatics Center, George Washington University, Washington, DC, USA.ORCID https://orcid.org/0000-0003-2156-4588
Eduardo A Trujillo RiveraBiomedical Informatics Center, George Washington University, Washington, DC, USA.
Yijun ShaoBiomedical Informatics Center, George Washington University, Washington, DC, USA.
Phillip MaBiomedical Informatics Center, George Washington University, Washington, DC, USA.ORCID https://orcid.org/0000-0002-8302-5021
Mark S TuttleApelon, Inc., Hingham, MA, USA.
Jennifer GarvinSchool of Health and Rehabilitation Sciences, Ohio State University, Columbus, OH, USA.
Qing Zeng-TreitlerBiomedical Informatics Center, George Washington University, Washington, DC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: International Classification of Diseases (ICD) codes recorded in electronic health records (EHRs) are frequently used to create patient cohorts or define phenotypes. Inconsistent assignment of codes may reduce the utility of such cohorts. We assessed the reliability across time and location of the assignment of ICD codes in a US health system at the time of the transition from ICD-9-CM (ICD, 9th Revision, Clinical Modification) to ICD-10-CM (ICD, 10th Revision, Clinical Modification). Materials and methods: Using clusters of equivalent codes derived from the US Centers for Disease Control and Prevention General Equivalence Mapping (GEM) tables, ICD assignments occurring during the ICD-9-CM to ICD-10-CM transition were investigated in EHR data from the US Veterans Administration Central Data Warehouse using deep learning and statistical models. These models were then used to detect abrupt changes across the transition; additionally, changes at each VA station were examined. Results: Many of the 687 most-used code clusters had ICD-10-CM assignments differing greatly from that predicted from the codes used in ICD-9-CM. Manual reviews of a random sample found that 66% of the clusters showed problematic changes, with 37% having no apparent explanations. Notably, the observed pattern of changes varied widely across care locations. Discussion and conclusion: The observed coding variability across time and across location suggests that ICD codes in EHRs are insufficient to establish a semantically reliable cohort or phenotype. While some variations might be expected with a changing in coding structure, the inconsistency across locations suggests other difficulties. Researchers should consider carefully how cohorts and phenotypes of interest are selected and defined.

Indexed as

clinical codingdata accuracyInternational classification of diseases

Identifiers

PMID39493629
PMCPMC11528819

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Registered trials

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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.