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Migrating existing clinical content from ICD-9 to SNOMED.
J Am Med Inform Assoc 2010 Sep; 17(5):602-607
OBJECTIVE: To identify challenges in mapping internal International Classification of Disease, 9th edition, Clinical Modification (ICD-9-CM) encoded legacy data to Systematic Nomenclature of Medicine (SNOMED), using SNOMED-prescribed compositional approaches where appropriate, and to explore the mapping coverage provided by the US National Library of Medicine (NLM)'s SNOMED clinical core subset. DESIGN: This study selected ICD-CM codes that occurred at least 100 times in the organization's problem list or diagnosis data in 2008. After eliminating codes whose exact mappings were already available in UMLS, the remainder were mapped manually with software assistance. RESULTS: Of the 2194 codes, 784 (35.7%) required manual mapping. 435 of these represented concept types documented in SNOMED as deprecated: these included the qualifying phrases such as 'not elsewhere classified'. A third of the codes were composite, requiring multiple SNOMED code to map. Representing 45 composite concepts required introducing disjunction ('or') or set-difference ('without') operators, which are not currently defined in SNOMED. Only 47% of the concepts required for composition were present in the clinical core subset. Search of SNOMED for the correct concepts often required extensive application of knowledge of both English and medical synonymy. CONCLUSION: Strategies to deal with legacy ICD data must address the issue of codes created by non-taxonomist users. The NLM core subset possibly needs augmentation with concepts from certain SNOMED hierarchies, notably qualifiers, body structures, substances/products and organisms. Concept-matching software needs to utilize query expansion strategies, but these may be effective in production settings only if a large but non-redundant SNOMED subset that minimizes the proportion of extensively pre-coordinated concepts is also available.
Data-processing; Medical-monitoring; Medical-research; Medical-surveys Qualitative-analysis; Information-processing; Information-retrieval-systems; Information-systems
Dr Prakash M Nadkarni, Yale Center for Medical Informatics, 300 George St, New Haven, CT 06511
Issue of Publication
Journal of the American Medical Informatics Association
Mount Sinai School of Medicine of New York University
Page last reviewed: March 11, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division