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eISSN: 2581-9615 || CODEN (USA): WJARAI || Impact Factor: 8.2 || ISSN Approved Journal

Utilizing predictive analytics to improve healthcare access in the United States (U.S.)

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  • Utilizing predictive analytics to improve healthcare access in the United States (U.S.)

Kemisola Kasali 1, *, Balikis Y. Alarape 2, Ifiala Agwu Ifiala 3, Peter O. Alawiye 4, Chukwudike Eric Enem 5,
Ifechukwu Jeffrey Enem 6, and Rasaq Oladapo 7

1 College of Business, Health, and Human Services, Department of Management, Marketing, and Technology, University of Arkansas at Little Rock, USA.
2 Department of Business Administration and Management, Monroe University, New Rochelle, New York, USA
3 Department of Life Science Laboratories. IALS, University of Massachusetts Amherst, Massachusetts, USA
4 Department of Business Administration, New Mexico Highlands University, Las Vegas, New Mexico, USA
5 Department of Chemistry and Biochemistry, Lamar University, Beaumont, Texas, USA
6 Department of Chemistry and Biochemistry, Western Kentucky University, Kentucky, USA
7 Population Health Sciences, Bristol Medical School, University of Bristol, UK.

Research Article

World Journal of Advanced Research and Reviews, 2025, 25(03), 1465-1470

Article DOI: 10.30574/wjarr.2025.25.3.0899

DOI url: https://doi.org/10.30574/wjarr.2025.25.3.0899

Received on 11 February 2025; revised on 15 March 2025; accepted on 17 March 2025

The United States (U.S.) healthcare system faces persistent disparities in access, affordability, and quality, driven by systemic barriers, provider shortages, and rising costs. Despite federal interventions such as the Affordable Care Act (ACA), Medicaid expansion, and value-based care models, millions remain uninsured, and inefficiencies continue to burden the system. Predictive analytics, a data-driven approach leveraging machine learning and statistical models, offers a transformative method to enhance healthcare accessibility, optimize resource allocation, and reduce inefficiencies. By analyzing historical and real-time patient data, predictive analytics can anticipate access failures, forecast provider shortages, and improve care coordination. Studies show its ability to reduce hospital readmissions, shorten hospital stays, and generate significant cost savings. However, regulatory gaps, funding constraints, and integration challenges limit widespread adoption. Also, concerns about algorithmic bias, data privacy, and the need for significant infrastructure investment present notable implementation challenges(1,2). Addressing the concerns around bias and equity in AI-driven models is essential to ensure fair and ethical implementation. To fully leverage predictive analytics, policymakers must establish real-time AI-driven monitoring systems, regulatory frameworks for algorithmic transparency, and AI-driven provider incentives. A federal AI oversight board and AI infrastructure grant program should support ethical and equitable implementation that ensures data interoperability, risk-based Medicaid expansion, and optimized telehealth services. Ethical governance frameworks must be developed alongside technical solutions to ensure predictive models don't perpetuate existing healthcare disparities(3). By embedding predictive analytics into national healthcare policies, the U.S. can transition to a proactive, cost-efficient, and equitable healthcare system that prioritizes preventive care and long-term sustainability.

Healthcare access and disparities; Artificial Intelligence (AI); Predictive Analytics; Algorithmic Bias; Healthcare Policy

https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-0899.pdf

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Kemisola Kasali, Balikis Y. Alarape, Ifiala Agwu Ifiala, Peter O. Alawiye, Chukwudike Eric Enem,
Ifechukwu Jeffrey Enem, and Rasaq Oladapo. Utilizing predictive analytics to improve healthcare access in the United States (U.S.). World Journal of Advanced Research and Reviews, 2025, 25(03), 1465-1470. Article DOI: https://doi.org/10.30574/wjarr.2025.25.3.0899.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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