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Journal of the American Medical Informatics Association, Vol 3, 224-233, Copyright © 1996 by American Medical Informatics Association
ARTICLES |
CG Chute, SP Cohn, KE Campbell, DE Oliver and JR Campbell
Section of Medical Information Resources, Mayo Foundation, Rochester, MN 55905, USA. chute@mayo.edu
BACKGROUND AND OBJECTIVE: Patient conditions and events are the core of patient record content. Computer-based records will require standard vocabularies to represent these data consistently, thereby facilitating clinical decision support, research, and efficient care delivery. To address whether existing major coding systems can serve this function, the authors evaluated major clinical classifications for their content coverage. METHODS: Clinical text from four medical centers was sampled from inpatient and outpatient settings. The resultant corpus of 14,247 words was parsed into 3,061 distinct concepts. These concepts were grouped into Diagnoses, Modifiers, Findings, Treatments and Procedures, and Other. Each concept was coded into ICD-9-CM, ICD-10, CPT, SNOMED III, Read V2, UMLS 1.3, and NANDA; a secondary reviewer ensured consistency. While coding, the information was scored: 0 = no match, 1 = fair match, 2 = complete match. RESULTS: ICD-9-CM had an overall mean score of 0.77 out of 2; its highest subscore was 1.61 for Diagnoses. ICD-10 scored 1.60 for Diagnoses, and 0.62 overall. The overall score of ICD-9-CM augmented by CPT was not materially improved at 0.82. The SNOMED International system demonstrated the highest score in every category, including Diagnoses (1.90), and had an overall score of 1.74. CONCLUSION: No classification captured all concepts, although SNOMED did notably the most complete job. The systems in major use in the United States, ICD-9-CM and CPT, fail to capture substantial clinical content. ICD-10 does not perform better than ICD-9-CM. The major clinical classifications in use today incompletely cover the clinical content of patient records; thus analytic conclusions that depend on these systems may be suspect.
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