ArticleMIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy...
Clinical Clarity versus Terminological Order - The Readiness of SNOMED CT Concept Descriptors for Primary Care.
Article in MIX-HS'12 : proceedings of the 2nd International Workshop on Managing Interoperability and Complexity in Health Systems October 29, 2012, Maui, Hawaii, USA. International Workshop on Managing Interoperability and Complexity in Health Sy.... The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- A comparative analysis of the density of the SNOMED CT conceptual content for semantic harmonization.Artificial intelligence in medicine · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
As SNOMED usage becomes more ingrained within applications, its range of concept descriptors, and particularly its synonym adequacy, becomes more important. A simulated clinical scenario involving various term-based concept searches is used to assess whether SNOMED's concept descriptors provide sufficient differentiation to enable possible concept selection between similar terms. Four random samples from different SNOMED concept populations are utilized. Of particular interest are concepts mapped duplicately into UMLS concepts due to shared term patterns. While overall synonym problems are rare (1%), some concept populations exhibited a high rate of potential problems for clinical use (17-62%). The vast majority of issues are due to SNOMED's inherent structure and fine granularity. Many findings hint at a lack of clear delineation between reference and interface terminological qualities. Closer attention should be given to practical clinical use-case scenarios. Reducing SNOMED's structural complexity may alleviate many of the described findings and encourage clinical adoption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.