ArticleHealth systems (Basingstoke, England)2026
The combined use of natural language processing and electronic health records data to identify historical tolerances of β-lactams and promote clinician confidence in future use.
Article in Health systems (Basingstoke, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We used natural language processing (NLP) to improve the utility of clinical decision support (CDS) β-lactam allergy alerts and promote informed allergy evaluation. NLP was performed on a corpus of clinical notes from hospital-based encounters to identify previous tolerance of β-lactam products using a rule-based approach. Historical tolerance of β-lactams was then combined with structured electronic health records data to produce improved CDS alerts. A survey was used to evaluate the utility of the improved alerts compared to standard allergy alerts. The rule-based pipeline identified previous β-lactam tolerance in between 3% and 28.4% of clinical notes and performed with high positive predictive value (83.6-97.6%) and recall (71.2-79.4%). The surveyed clinicians (
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