ArticleJMIR formative research2020
Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis.
Article in JMIR formative research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake.Implementation science : IS · 2022Pooled it
- Identification of risk factors and development of a multivariable prognostic prediction model for incontinence-associated dermatitis in older nursing home residents.Scientific reports · 2026Article
- Diagnostic Concordance Between Clinical and Histopathologic Diagnoses of Oral Mucosal Lesions: A Retrospective Study.Cureus · 2025Article
- Development of a multivariable prognostic prediction model for skin tears in older nursing home residents.Scientific reports · 2025Article
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Implementing patient decision aids into general practice clinical decision support systems: Feasibility study in cardiovascular disease prevention.PEC innovation · 2023Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEffective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems.
objectiveThe aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems.
methodsTwo methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems.
resultsSNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs.
conclusionsNo single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.
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