ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2008
Identification of inactive medications in narrative medical text.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Modeling drug exposure data in electronic medical records: an application to warfarin.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- Extracting medication information from clinical text.Journal of the American Medical Informatics Association : JAMIAArticle
- Extracting Rx information from clinical narrative.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discontinued medications are frequently not removed from EMR medication lists - a patient safety risk. We developed an algorithm to identify inactive medications using in the text of narrative notes in the EMR. The algorithm was evaluated against manual review of 297 randomly selected notes. One in five notes documented inactive medications. Sensitivity and precision of 87.7% and 80.7%, respectively, on per-note basis and 66.3% and 80.0%, respectively, on per-medication basis. When medication names missing from the dictionary were excluded, the algorithm achieved sensitivity of 91.4%. Using real clinical data, the algorithm identified inactive medications documented in the note but still listed as active on the patients medication list in more than one in ten notes. Documentation of inactive medications is common in narrative provider notes and can be computationally extracted. This technology could be employed in real-time patient care as well as for research and quality of care monitoring.
Indexed as
Identifiers
18999079PMC2655977What 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.