Harnessing predictive analytics to enhance medication adherence: A strategic model for public health impact
Syracuse University, NY, USA.
Review
Open Access Research Journal of Life Sciences, 2024, 08(02), 008–016.
Article DOI: 10.53022/oarjls.2024.8.2.0034
Publication history:
Received on 24 September 2024; revised on 01 November 2024; accepted on 04 November 2024
Abstract:
This review paper explores the critical role of predictive analytics in enhancing medication adherence, a significant challenge impacting global public health and healthcare systems. Medication non-adherence is linked to increased healthcare costs, poor health outcomes, and reduced quality of life for patients. By leveraging predictive analytics, healthcare providers can identify patients at risk of non-compliance, enabling targeted interventions that address individual barriers to adherence. The paper outlines a strategic framework for integrating predictive analytics into healthcare systems, emphasizing the importance of data sources, algorithm selection, and patient engagement. Furthermore, it discusses the broader implications of improved medication adherence for public health, including potential reductions in healthcare costs and enhancements in system efficiency. The conclusion offers strategic recommendations for policymakers and healthcare providers, highlighting the need for further research and technological advancements in predictive analytics to fully realize its potential in improving medication adherence. This review underscores the importance of data-driven decision-making in healthcare and its role in fostering a culture of adherence that benefits patients and communities alike.
Keywords:
Predictive Analytics; Medication Adherence; Public Health; Healthcare Costs; Patient Engagement; Data-Driven Decision-Making
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0