ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Identifying inconsistencies in SNOMED CT problem lists using structural indicators.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- A tribal abstraction network for SNOMED CT target hierarchies without attribute relationships.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies.Journal of the American Medical Informatics Association : JAMIA · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The National Library of Medicine has published the CORE and the VA/KP problem lists to facilitate the usage of SNOMED CT for encoding diagnoses and clinical data of patients in electronic health records. Therefore, it is essential for the content of the problem lists to be as accurate and consistent as possible. This study assesses the effectiveness of using a concept's word length and number of parents, two structural indicators for measuring concept complexity, to identify inconsistencies with high probability. The method is able to isolate concepts with over 40% expected of being erroneous. A structural indicator for concepts which is able to identify 52% of the examined concepts as having errors in synonyms is also presented. The results demonstrate that the concepts in problem lists are not free of inconsistencies and further quality assurance is needed to improve the quality of these concepts.
Indexed as
Identifiers
24551319PMC3900119What OpenQuestion holds
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.