A new paradigm in adverse drug reaction reporting: consolidating the evidence for an intervention to improve reporting

ABSTRACT

Introduction

Adverse drug reaction (ADR) under-reporting is highly prevalent internationally and interventions created to address this problem have only been temporarily successful. This review aims to investigate how to leverage digital applications and automation across the healthcare industry to improve the quantity and quality of ADR reporting.

Areas covered

This review investigated the significance of ADR under-reporting, the barriers of reporting ADRs, and the magnitude of success of various interventions to improve ADR reporting by searching the EMBASE and MEDLINE databases to include studies published between January 2000 and February 2022. This data was integrated with a view to describe a future ADR reporting framework.

Expert opinion

Digital transformation has presented a significant opportunity with vast quantities of patient health data becoming available in electronic formats. The application of artificial intelligence to detect ADRs and then using automation to report these directly to regulatory agencies without human input would significantly enhance the quantity and quality of ADR reporting. Emphasis should be placed on ADRs identified for newly approved or black triangle medicines. Future studies are needed to measure the success of this ADR reporting framework in reducing the time taken to identify new safety issues and improving patient outcomes.

KEYWORDS:

• Pharmacovigilance
• digital
• drug safety
• automation

Article highlights

• Despite the implementation of various interventions, ADR under-reporting remains highly prevalent delaying the identification of new safety signals.

• The most common factors associated with ADR reporting that are mapped to a behavioural framework include knowledge, motivational factors/goals, and environmental constraints.

• Interventions to improve ADR reporting have only been modestly effective as their designs were not informed by behavioural change frameworks that would specifically address the identified barriers.

• A comprehensive multifaceted approach leveraging artificial intelligence to identify ADRs from health databases and applying automation to report these to regulatory agencies should be the gold standard of ADR reporting in the future.

• Significant challenges remain in the adoption of automation and artificial intelligence in ADR reporting due to the infancy of technology in this area and future research should be directed to help inform the design and features of

This box summarizes key points contained in the article.

Declaration of interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

Conception and design: R Li. Analysis and interpretation of data: R Li, K Curtis, C Van, STR Zaidi, R Castelino. Drafting of paper: R Li. Revising it critically for intellectual content: R Li, K Curtis, C Van, STR Zaidi, R Castelino. Final approval of version to be published: R Li, K Curtis, C Van, STR Zaidi, R Castelino. All authors agree to be accountable for all aspects of this publication.

Additional information

Funding

This paper was not funded.