Health data analytics for population health management: A review of best practices and challenges
1 Independent Researcher, Abuja, Nigeria.
2 Department of Business Administration, Texas A&M University Commerce, TX, USA.
3 Etihuku Pty Ltd, Midrand, Gauteng, South Africa.
Review Article
International Journal of Frontiers in Medicine and Surgery Research, 2024, 06(02), 106–116.
Article DOI: 10.53294/ijfmsr.2024.6.2.0050
Publication history:
Received on 08 October 2024; revised on 14 November 2024; accepted on 17 November 2024
Abstract:
Health data analytics has become an indispensable tool for optimizing population health management by offering data-driven insights that improve healthcare outcomes, enhance preventive care, and inform policy-making. This review explores best practices in health data analytics, including predictive analytics, risk stratification, patient segmentation, and data integration. It highlights successful implementations in healthcare systems that demonstrate the positive impact of these practices on care coordination, resource allocation, and disease prevention. However, the paper also addresses several challenges hindering the widespread adoption of health data analytics, such as data privacy concerns, interoperability issues, and integrating diverse data sources. Ethical, technical, and operational barriers further complicate its effective use. The review concludes with recommendations for improving the adoption and effectiveness of health data analytics, emphasizing the need for strong data governance, improved interoperability, enhanced data quality, and workforce development. These strategies can maximize the benefits of health data analytics in advancing population health management.
Keywords:
Health data analytics; Population health management; Predictive analytics; Data privacy; Interoperability; Risk stratification
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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