A Framework for Personalized Pharmacovigilance in Polypharmacy Patients

Thesis Abstract

Polypharmacy presents a significant challenge to patient safety due to the increased risk of adverse drug reactions (ADRs) and drug-drug interactions, especially among elderly and chronic disease populations. Traditional pharmacovigilance systems often lack personalized approaches, which limits their effectiveness in detecting and preventing ADRs tailored to individual patient profiles. This study aims to develop a comprehensive framework for personalized pharmacovigilance specifically targeting polypharmacy patients, with the goal of improving adverse event detection, risk stratification, and clinical decision-making. The primary objectives include identifying patient-specific factors influencing ADR risk, conceptualizing a model integrating electronic health records (EHRs), pharmacogenomic data, and real-time monitoring, and validating this framework within a real-world clinical setting. Employing a mixed-methods research design, the study combines qualitative interviews with healthcare professionals to explore current pharmacovigilance practices and barriers to personalization, alongside quantitative analysis of patient data. The population consists of 500 polypharmacy patients aged 65 and above receiving treatment within a tertiary healthcare system, with purposive sampling used to ensure diverse representation across comorbidities and medication regimens. Data collection instruments include structured surveys for clinicians, extraction of anonymized EHRs, pharmacogenomic profiles, and patient-reported outcome measures. Quantitative data analysis involves multivariate regression models, machine learning algorithms such as random forests for ADR risk prediction, and survival analysis to assess adverse event timing. The qualitative data are analyzed using thematic analysis, guided by the Health Belief Model and the Data-Information-Knowledge-Wisdom (DIKW) hierarchy, facilitating an understanding of contextual factors influencing pharmacovigilance adoption. Key anticipated findings include the identification of critical patient-specific variables—such as genetic markers, medication adherence patterns, comorbidities, and social determinants—that influence ADR susceptibility. The integration of these variables into a decision-support framework is expected to enhance the precision of ADR prediction and enable proactive monitoring tailored to individual risk profiles. The developed model is projected to demonstrate improved sensitivity and specificity compared to existing standard pharmacovigilance practices, as validated through receiver operating characteristic (ROC) curve analysis, with an estimated increase in early ADR detection rate by 25%. The findings will underscore the necessity of incorporating pharmacogenomic data and real-time monitoring devices into routine pharmacovigilance processes and highlight the potential for clinical pathways that embed these personalized approaches. This research contributes novel insights to the field of pharmacovigilance by proposing a comprehensive, patient-centered framework that synthesizes current technological advances with clinical processes. It advances theoretical understanding by empirically validating the applicability of established behavioral and information processing theories within a pharmacovigilance context. The study's proposed model offers a practical tool for healthcare practitioners, policy-makers, and computer scientists to optimize medication safety strategies in complex polypharmacy scenarios. In conclusion, the study advocates for systemic adoption of personalized pharmacovigilance frameworks to mitigate ADR risks among polypharmacy patients. Recommendations include policy reforms to integrate pharmacogenomic testing into routine care, development of user-friendly decision-support systems, and ongoing training for healthcare providers in personalized risk assessment. Future research should focus on large-scale implementation and evaluation across diverse healthcare settings, with an emphasis on long-term clinical and economic outcomes. This study thus positions itself as a foundational effort towards precision medicine-driven pharmacovigilance, fostering safer medication use in increasingly complex patient populations.