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

Kasali, Kemisola and Oladapo, Rasaq (2025) Utilizing predictive analytics to improve healthcare access in the United States (U.S.). World Journal of Advanced Research and Reviews, 25 (3). pp. 1465-1470. ISSN 2581-9615

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.