Predictive Modeling for Personalized Healthcare Plan Selection
Year: 2025
Type: article
Abstract: Predictive modeling for personalized healthcare plan selection represents a transformative solution to addressing the complexities of healthcare decision-making. This advancement leverages artificial intelligence and cloud-based analytics to interpret historical data on service approvals, denials, and utilization patterns, enabling individuals to make informed choices about their healthcare coverage. The integration of sophisticated algorithms facilitates the evaluation of demographic information, medical history, prescription usage, and previous claims to forecast future healthcare needs with enhanced accuracy. These technologies offer comprehensive insights through personalized plan recommendations, detailed cost projections, intelligent risk stratification, network optimization, and sentiment evaluation. Implementing these predictive systems empowers consumers by providing them with tailored guidance that aligns with their specific health profiles and financial considerations. Additionally, applying these technologies benefits insurers and employers by enabling more precise risk assessment and plan design. As predictive modeling continues to evolve, it promises to significantly improve plan satisfaction, reduce unexpected expenses, promote preventive care, and ultimately contribute to more efficient healthcare resource allocation and improved patient outcomes.
Author: Raphael Shobi Andhikad Thomas
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6968207243
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