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

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 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.

RESULTS

There 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.

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.