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

被引:6
作者
Tavakoli, Kiana [1 ,2 ,3 ]
Kalaw, Fritz Gerald P. [1 ,2 ,3 ]
Bhanvadia, Sonali [1 ,2 ,3 ]
Hogarth, Michael [3 ]
Baxter, Sally L. [1 ,2 ,3 ,4 ]
机构
[1] Univ Calif San Diego, Viterbi Family Dept Ophthalmol, Div Ophthalmol Informat & Data Sci, La Jolla, CA USA
[2] Univ Calif San Diego, Shiley Eye Inst, La Jolla, CA USA
[3] Univ Calif San Diego, Dept Med, Div Biomed Informat, La Jolla, CA USA
[4] 9415 Campus Point Dr MC0946, La Jolla, CA 92093 USA
来源
OPHTHALMOLOGY SCIENCE | 2023年 / 3卷 / 04期
基金
美国国家卫生研究院;
关键词
Data standards; Electronic health records; ICD; Ophthalmology; SNOMED; ELECTRONIC HEALTH RECORDS; INTERNATIONAL CLASSIFICATION; INTELLIGENT-RESEARCH; SYSTEMS; REPRESENTATION;
D O I
10.1016/j.xops.2023.100337
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Purpose: Widespread electronic health record adoption has generated a large volume of data and empha-sized 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.& COPY; 2023 by the American Academy of Ophthalmology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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页数:7
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