Harnessing AI to Elevate Healthcare Quality Ratings: Transforming Provider Performance and Patient Outcomes - Boyapati, Phanindra Sai; Godavarthi, Kranthi (International Journal of Computing and Engineering, 2025)
AI-driven solutions are transforming healthcare quality-of-care ratings by addressing challenges such as fragmented data, inconsistent scoring, and reliance on manual processes. Traditional rating systems incorporate diverse measures, including clinical outcomes, adherence to best practices, and patient experiences, but face inefficiencies due to interoperability issues and data silos. AI offers a unified approach by integrating and standardizing data from multiple sources, enabling automated analysis, real-time monitoring, and predictive insights. AI-driven sentiment analysis further enhances objectivity by processing unstructured patient feedback. Implementing AI in quality assessments can improve accuracy, provider accountability, and patient outcomes while reducing costs. To maximize its potential, healthcare organizations must invest in AI infrastructure, enforce standardized protocols, train staff, and ensure ethical data use. Collaboration among healthcare entities will further refine AI-driven assessments, advancing a patient-centered, data-driven approach to quality ratings.