BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly used for type 2 diabetes mellitus (T2DM) due to their multifaceted benefits, including glycemic control and cardiovascular protection. However, variations in prescribing practices and potential adverse drug events (ADEs) necessitate standardized prescription review protocols to ensure medication safety and efficacy.
OBJECTIVE: This study aimed to develop and implement GLP-1RA prescription review rules through a multidisciplinary information pharmacist team, evaluate their impact on prescription rationality, and identify ADE risk signals using real-world data, thereby promoting rational clinical medication use and ensuring patient safety.
METHODS: A multidisciplinary information pharmacist team was established at a tertiary hospital to develop GLP-1RA prescription review protocols. China-approved GLP-1RA formulations and their clinical parameters were systematically reviewed to establish standardized prescribing criteria, including indications, dosing, and safety considerations. Adverse drug events were analyzed using FDA Adverse Event Reporting System data (2018-2023) to identify risk patterns. A dual review system integrating prospective prescription screening and retrospective evaluation was implemented. The intervention's efficacy was evaluated by comparing prescription approval rates pre- (2022) and post-implementation (2023).
RESULTS: The GLP-1RA audit rules represented by liraglutide and semaglutide were successfully created, as well as their potential adverse event signals were successfully obtained. The process of prescription review and medication monitoring enabled them to be put into clinical practice. After the rules were put in place in 2023, the pass rates of GLP-1RA prescriptions significantly improved, and the rationalization of these prescriptions was also notably enhanced compared to the same period in 2022 (p < 0.001).
CONCLUSIONS: Standardized GLP-1RA review rules enhanced prescription rationality and ADE risk awareness, demonstrating the value of information pharmacists in optimizing clinical decision-making. This model is scalable for other high-risk medications, promoting safer drug use and pharmacist-led innovation in healthcare.