Artificial intelligence–enabled penicillin allergy delabelling: an implementation study

Brandon Stretton, Melinda Jiang, Joshua Kovoor, Joshua M. Inglis, Lydia Lam, Sheryn Tan, Chino Yuson, William Smith, Sepehr Shakib, Stephen Bacchi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Inaccurate penicillin allergy labels may be delabelled following evaluation. The intervention in this study was an email-based notification system regarding the appropriateness for penicillin allergy evaluation, with a view to delabelling, as identified by a deep learning artificial intelligence algorithm. Of the intervention group (n = 59), three (5.1%) individuals had their penicillin allergies delabelled, which was significantly more than the control group (0%, P = 0.002). Further research to optimise such approaches is required.

Original languageEnglish
Pages (from-to)2119-2122
Number of pages4
JournalInternal Medicine Journal
Volume53
Issue number11
DOIs
Publication statusPublished - Nov 2023

Keywords

  • antibiotic
  • efficiency
  • intolerance
  • machine learning
  • outcomes

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