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

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 (N = 9) reported increased confidence in using β-lactam products despite the presence of a documented β-lactam allergy when using the information presented by the NLP-enriched CDS alerts, and all surveyed clinicians indicated the alerts would improve the care of their patients. NLP of clinical notes shows potential to improve the utility of CDS allergy alerts. Clinicians were receptive to allergy alerts containing NLP-derived information. Allergy-related CDS alerts should be improved to provide additional information such as historical tolerance of relevant products to empower providers to make informed decisions regarding patient allergies.