ArticleStudies in health technology and informatics2017
Interoperability of Medication Classification Systems: Lessons Learned Mapping Established Pharmacologic Classes (EPCs) to SNOMED CT.
Article in Studies in health technology and informatics, 2017. 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.
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Who cites it
4 citing papers in PubMed.
- Problems associated with the ATC system of drug classification.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Toward Interoperable Digital Medication Records on Fast Healthcare Interoperability Resources: Development and Technical Validation of a Minimal Core Dataset.JMIR medical informatics · 2025Article
- Rebuilding the Standing Prescription Renewal Orders.Applied clinical informatics · 2019Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Interoperability among medication classification systems is known to be limited. We investigated the mapping of the Established Pharmacologic Classes (EPCs) to SNOMED CT. We compared lexical and instance-based methods to an expert-reviewed reference standard to evaluate contributions of these methods. Of the 543 EPCs, 284 had an equivalent SNOMED CT class, 205 were more specific, and 54 could not be mapped. Precision, recall, and F1 score were 0.416, 0.620, and 0.498 for lexical mapping and 0.616, 0.504, and 0.554 for instance-based mapping. Each automatic method has strengths, weaknesses, and unique contributions in mapping between medication classification systems. In our experience, it was beneficial to consider the mapping provided by both automated methods for identifying potential matches, gaps, inconsistencies, and opportunities for quality improvement between classifications. However, manual review by subject matter experts is still needed to select the most relevant mappings.
Indexed as
Identifiers
29295234PMC5881380What 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.