Proactive deep learning-facilitated inpatient penicillin allergy delabelling: An implementation study.

Abstract

Introduction: Erroneous 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.

Methods: This prospective implementation study utilised a deep learning-guided proactive consultation service, which utilized 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.

Results: There were 439 patients included in the study, of whom 121 were identified by the algorithm as suitable for penicillin allergy interrogation. 16.5% of those identified by the algorithm 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 was $1,170,617.16.

Conclusion: Deep 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.