Idealized Framework for Assisting Pharmacovigilance Reporting in an Ambulatory Primary Care and Chronic Disease Management Clinic

Pharmacovigilance approaches have conventionally focused on the use of epidemiological data to detect emergent adverse drug reactions (ADRs). Recent advances in the use and availability of real-world data have expanded opportunities to detect ADR signals in medical records. We provide a limited review of pharmacovigilance practices and tools we have specifically considered implementing into our comprehensive medication management clinic and associated research programs. Use of pharmacogenomic variants has proven useful only on a limited scale as such data are reliant on low-dimensional approaches matching variants to drugs, often with small effect sizes. As such, most ADRs go unrecognized, undocumented, and unactionable. We posit that an idealized pharmacovigilance framework that relies on artificial-intelligence-assisted reporting with adjudication by pharmacovigilance experts and new models of ambulatory pharmaceutical practice would establish the following attributes: (1) all metadata relating to medication use would be available in the medical record in computable and interoperable data models, (2) digital surveillance tools would detect most ADR events with attributed pharmacological contributions, (3) all events would be characterized using standard adjudication rubrics, and (4) all events would iteratively inform an ADR knowledgebase and improve models to advance detection and prediction of ADR during the course of patient care with a focus on having the necessary tools for clinicians to prevent ADRs. This review provides a limited and focused framework for more systematic documentation of ADRs and tactics to mitigate the idiopathic nature of most ADRs.