Dataset Information

Migrating existing clinical content from ICD-9 to SNOMED.

ABSTRACT:

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.

SUBMITTER: Nadkarni PM

PROVIDER: S-EPMC2995664 | biostudies-literature | 2010 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

Migrating existing clinical content from ICD-9 to SNOMED.

Nadkarni Prakash M PM Darer Jonathan A JA

Journal of the American Medical Informatics Association : JAMIA 20100901 5

<h4>Objective</h4>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.<h4>Design</h4>This study selected ICD-CM codes that occurred at least 100 times in the organiz ...[more]

PMID: 20819871

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Project description:ObjectiveThe study sought to assess the feasibility of replacing the International Classification of Diseases-Tenth Revision-Clinical Modification (ICD-10-CM) with the International Classification of Diseases-11th Revision (ICD-11) for morbidity coding based on content analysis.Materials and methodsThe most frequently used ICD-10-CM codes from each chapter covering 60% of patients were identified from Medicare claims and hospital data. Each ICD-10-CM code was recoded in the ICD-11, using postcoordination (combination of codes) if necessary. Recoding was performed by 2 terminologists independently. Failure analysis was done for cases where full representation was not achieved even with postcoordination. After recoding, the coding guidance (inclusions, exclusions, and index) of the ICD-10-CM and ICD-11 codes were reviewed for conflict.ResultsOverall, 23.5% of 943 codes could be fully represented by the ICD-11 without postcoordination. Postcoordination is the potential game changer. It supports the full representation of 8.6% of 943 codes. Moreover, with the addition of only 9 extension codes, postcoordination supports the full representation of 35.2% of 943 codes. Coding guidance review identified potential conflicts in 10% of codes, but mostly not affecting recoding. The majority of the conflicts resulted from differences in granularity and default coding assumptions between the ICD-11 and ICD-10-CM.ConclusionsWith some minor enhancements to postcoordination, the ICD-11 can fully represent almost 60% of the most frequently used ICD-10-CM codes. Even without postcoordination, 23.5% full representation is comparable to the 24.3% of ICD-9-CM codes with exact match in the ICD-10-CM, so migrating from the ICD-10-CM to the ICD-11 is not necessarily more disruptive than from the International Classification of Diseases-Ninth Revision-Clinical Modification to the ICD-10-CM. Therefore, the ICD-11 (without a CM) should be considered as a candidate to replace the ICD-10-CM for morbidity coding.

Project description:BACKGROUND:Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. OBJECTIVE:This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. METHODS:We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. RESULTS:A total of 125 condition-defining "standard" SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th-75th percentiles: 1-5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th-75th percentiles: 8-85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th-75th percentiles: 63-976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. CONCLUSION:SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, "smarter" EHR feature configuration, and clinical-translational research employing EHR-derived data.

Project description:MotivationIn this paper we demonstrate the usage of RIO; a framework for detecting syntactic regularities using cluster analysis of the entities in the signature of an ontology. Quality assurance in ontologies is vital for their use in real applications, as well as a complex and difficult task. It is also important to have such methods and tools when the ontology lacks documentation and the user cannot consult the ontology developers to understand its construction. One aspect of quality assurance is checking how well an ontology complies with established 'coding standards'; is the ontology regular in how descriptions of different types of entities are axiomatised? Is there a similar way to describe them and are there any corner cases that are not covered by a pattern? Detection of regularities and irregularities in axiom patterns should provide ontology authors and quality inspectors with a level of abstraction such that compliance to coding standards can be automated. However, there is a lack of such reverse ontology engineering methods and tools.ResultsRIO framework allows regularities to be detected in an OWL ontology, i.e. repetitive structures in the axioms of an ontology. We describe the use of standard machine learning approaches to make clusters of similar entities and generalise over their axioms to find regularities. This abstraction allows matches to, and deviations from, an ontology's patterns to be shown. We demonstrate its usage with the inspection of three modules from SNOMED-CT, a large medical terminology, that cover "Present" and "Absent" findings, as well as "Chronic" and "Acute" findings. The module sizes are 5 065, 20 688 and 19 812 asserted axioms. They are analysed in terms of their types and number of regularities and irregularities in the asserted axioms of the ontology. The analysis showed that some modules of the terminology, which were expected to instantiate a pattern described in the SNOMED-CT technical guide, were found to have a high number of regularity deviations. A subset of these were categorised as "design defects" by verifying them with past work on the quality assurance of SNOMED-CT. These were mainly incomplete descriptions. In the worst case, the expected patterns described in the technical guide were followed by only 5% of the axioms in the module.ConclusionIt is possible to automatically detect regularities and then inspect irregularities in an ontology. We argue that RIO is a tool to find and report such matches and mismatches, for evaluations by the domain experts. We have demonstrated that standard clustering techniques from machine learning can offer a tool in the drive for quality assurance in ontologies.Availabilityhttp://riotool.sourceforge.net/Contacthttp://eleni.mikroyannidi@manchester.ac.uk, http://robert.stevens@manchehster.ac.uk.

Dataset Information

Migrating existing clinical content from ICD-9 to SNOMED.

Objective

Design

Results

Conclusion

Publications

Migrating existing clinical content from ICD-9 to SNOMED.

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