Paper Key : IRJ************517
Author: Okolue Chukwudi Anthony
Date Published: 03 Apr 2025
Abstract
The convergence of predictive analytics and electronic health records (EHRs) is reshaping the landscape of pharmaceutical care within population health management (PHM) frameworks. As healthcare systems shift from reactive treatment models toward proactive, data-driven approaches, the integration of EHRs with predictive analytics provides unprecedented opportunities to enhance medication safety, adherence, and personalized treatment planning. This paper presents a comprehensive overview of how predictive models, trained on longitudinal EHR data, can support pharmaceutical decision-making at both individual and population levels. From a broad perspective, the study analyzes the role of big data infrastructures and cloud-based EHR platforms in facilitating real-time access to patient-specific and population-wide health data. It then narrows the focus to predictive tools used for identifying high-risk patients for medication non-adherence, potential adverse drug reactions, and gaps in therapeutic interventions. Key techniques such as machine learning algorithms, risk stratification models, and medication optimization engines are evaluated for their clinical utility, accuracy, and scalability. The integration of predictive analytics into PHM platforms is also examined through the lens of care coordination, clinical workflow alignment, and patient engagement. Challenges including data silos, privacy concerns, algorithm transparency, and provider adoption are addressed, with proposed solutions involving interoperable standards, explainable AI, and robust clinical governance frameworks. Real-world case studies from integrated delivery networks demonstrate tangible outcomes such as reduced readmission rates, improved medication reconciliation, and targeted pharmaceutical interventions. Ultimately, the paper argues for a harmonized strategy that leverages predictive analytics within EHR ecosystems to transform pharmaceutical care, reduce health disparities, and enable population-level medication intelligence.
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