Evidence map›Paper›PMID 39827874›Full record

ArticleInternational archives of allergy and immunology2025

Proactive Deep Learning-Facilitated Inpatient Penicillin Allergy Delabelling: An Implementation Study.

Melinda Jiang, Brandon Stretton, Joshua Kovoor, Joshua M Inglis, Sophia Thompkins, Carlo Yuson, Sepehr Shakib, William B Smith, Stephen Bacchi

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PubMed Full text at PMC Publisher
In one paragraph

Article in International archives of allergy and immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Melinda JiangRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Brandon StrettonRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Joshua KovoorUniversity of Adelaide, Adelaide, South Australia, Australia.
Joshua M InglisFlinders University, Adelaide, South Australia, Australia.
Sophia ThompkinsRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Carlo YusonRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Sepehr ShakibUniversity of Adelaide, Adelaide, South Australia, Australia.
William B SmithRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Stephen BacchiFlinders University, Adelaide, South Australia, Australia, stephenbacchi@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionErroneous penicillin allergy labels are associated with significant health and economic costs. This study aimed to determine whether deep learning-facilitated proactive consultation to facilitate delabelling may further enhance inpatient penicillin allergy delabelling.

methodsThis prospective implementation study utilised a deep learning-guided proactive consultation service, which utilised an inpatient penicillin allergy delabelling protocol. The intervention group comprised all admitted inpatients with a penicillin allergy over the course of a 14-week period in a tertiary hospital. The rate of penicillin allergy delabelling in the intervention group was compared to that of a historical control group.

resultsThere were 439 patients included in the study, of whom 121 were identified by the algorithm as suitable for penicillin allergy interrogation. Of those identified by the algorithm, 16.5% were successfully delabelled in the inpatient setting within the same admission and 9.9% were referred for outpatient testing. This result was statistically significantly greater compared to the rate of delabelling in the historical control group (0%, p = 0.00001). There were no adverse reactions. The projected annual savings associated with the program over a 12-month period were AUD 1,170,617.16.

conclusionDeep learning-facilitated proactive inpatient penicillin allergy delabelling was effective, safe, and economical in this single-centre implementation study. Further studies should seek to examine this approach in diverse centres.

Indexed as

Anti-Bacterial AgentsDeep LearningDrug HypersensitivityPenicillinsAdultAgedAlgorithmsFemaleHumansInpatientsMaleMiddle AgedProspective StudiesAnti-Bacterial AgentsPenicillinsAdverse reactionAntimicrobial coverageArtificial intelligenceIntoleranceMachine learning

Identifiers

PMID39827874
PMCPMC12324796

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