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. 2023 May 25;3(4):100337.
doi: 10.1016/j.xops.2023.100337. eCollection 2023 Dec.

Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11

Kiana Tavakoli  1   2 , Fritz Gerald P Kalaw  1   2 , Sonali Bhanvadia  1   2 , Michael Hogarth  2 , Sally L Baxter  1   2
Affiliations

Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11

Kiana Tavakoli et al. Ophthalmol Sci. .

Abstract

Purpose: Widespread electronic health record adoption has generated a large volume of data and emphasized the need for standardized terminology to describe clinical concepts. Here, we undertook a systematic concept coverage analysis to determine the representation of clinical concepts in ophthalmic infection and ophthalmic trauma among standardized medical terminologies, including the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), the International Classification of Diseases (ICD) version 10 with clinical modifications (ICD-10-CM), and ICD version 11 (ICD-11).

Design: Extraction of concepts related to ophthalmic infection and ophthalmic trauma and structured search in terminology browsers.

Data sources: The American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser.

Methods: Concepts pertaining to ophthalmic infection and ophthalmic trauma were extracted from the 2022 BCSC free text and index terms. We searched terminology browsers to identify corresponding codes and classified the extent of semantic alignment as equal, wide, narrow, or unmatched in each terminology. The overlap of equal concepts in each terminology was represented in a Venn diagram.

Main outcome measures: Proportions of clinical concepts with corresponding codes at various levels of semantic alignment.

Results: A total of 443 concepts were identified: 304 concepts related to ophthalmic infection and 139 concepts related to ophthalmic trauma. The SNOMED-CT had the highest proportion of equal coverage, with 82.0% (249 of 304) among concepts related to ophthalmic infection and 82.0% (115 of 139) among concepts related to ophthalmic trauma. Across all concepts, 28% (124 of 443) were classified as equal in ICD-10-CM and 52.8% (234 of 443) were classified as equal in ICD-11.

Conclusions: The SNOMED-CT had significantly better semantic alignment than ICD-10-CM and ICD-11 for ophthalmic infections and ophthalmic trauma. This demonstrates opportunity for continuing advancement of representation of ophthalmic concepts in standardized medical terminologies.

Keywords: Data standards; Electronic health records; ICD; Ophthalmology; SNOMED.

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Figures

Figure 1
Figure 1
Venn diagrams of number of concepts in ophthalmic trauma (top) and infection (bottom) from the 2022 Basic and Clinical Science Course with equal/matching codes in standardized medical terminologies.
See this image and copyright information in PMC

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References

    1. Travers D.A., Haas S.W. Unified medical language system coverage of emergency-medicine chief complaints. Acad Emerg Med. 2006;13:1319–1323. - PubMed
    1. Hripcsak G., Albers D.J. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013;20:117–121. - PMC - PubMed
    1. Pathak J., Bailey K.R., Beebe C.E., et al. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc. 2013;20:e341–e348. - PMC - PubMed
    1. Klann J.G., Phillips L.C., Turchin A., et al. A numerical similarity approach for using retired current procedural terminology (CPT) codes for electronic phenotyping in the scalable collaborative infrastructure for a learning health system (SCILHS) BMC Med Inform Decis Mak. 2015;15:104. - PMC - PubMed
    1. Amith M., He Z., Bian J., et al. Assessing the practice of biomedical ontology evaluation: gaps and opportunities. J Biomed Inform. 2018;80:1–13. - PMC - PubMed