How to manage the overwhelming amount of database-detected interactions? A focus group meeting study on the management of database-detected risks of drug interactions.

Clinical Snapshot

Key Findings

• Effect Size: Not applicable for qualitative framework development study

• Primary Outcome: Development of decision-making framework with key questions: evidence base, severity/documentation level, safer alternatives, confounding factors

• Nnt Or Sensitivity: Framework considered useful by participants with potential for clinical implementation

Clinical Application

Requires integration into clinical decision support systems and healthcare curricula for optimal implementation Relevant to Australian practice where drug interaction screening is routine; could integrate with existing clinical decision support tools and align with RACGP medication management guidelines Healthcare professionals managing patients with multiple medications and potential drug interactions

Abstract

OBJECTIVES: Database-assisted screening may help detect the risks of drug interactions, but less than 10% of flagged interactions have clinical manifestations, potentially leading to alert fatigue among healthcare providers. To address this issue, this study aimed to develop a concise framework to support the management of database-indicated interaction risks. METHODS: A grounded theory approach was used to design, evaluate and develop the framework. Participants included healthcare professionals and laypersons with experience using narrow therapeutic index medications, as well as individuals recommended by them. Two focus group discussions were conducted (N1=6 and N2=5), each lasting approximately 2 hours and following the same agenda. In one case, a follow-up one-on-one interview was held because the participant indicated further insights at the end of the session. RESULTS: Participants identified key decision-making questions for the framework, including the evidence base for interacting drugs, severity and documentation level of the interaction, availability of safer alternatives, and the presence of confounding factors. The framework was considered useful and, following refinement, potentially suitable for clinical implementation. A key insight was that integration into healthcare curricula is essential for achieving long-term impact. Overall, the proposed tool may assist in managing interaction risks in diverse clinical scenarios and reduce alert fatigue among healthcare professionals. CONCLUSION: Although further clinical validation is needed, the framework provides a foundation for improving the management of database-detected drug interactions.