Innovation Group, IT Department, Neudesic LLC, an IBM Company, United States.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 3191-3195
Received on 08 November 2024; revised on 24 December 2024; accepted on 27 December 2024
Healthcare networks grapple with the complexities of managing vast, disparate provider data while ensuring efficient operations and maintaining high-quality patient care. AI-powered provider profiling offers a transformative solution, leveraging advanced machine learning and data integration techniques to unify provider data, track affiliations, and enhance network reliability. This white paper explores the application of AI models in provider profiling, the integration of Fast Healthcare Interoperability Resources (FHIR) standards to ensure interoperability, and the resultant impact on patient care delivery. By addressing key challenges and providing actionable insights, this paper outlines a future-forward roadmap for healthcare systems striving to achieve greater transparency, efficiency, and patient-centric outcomes.
AI in Healthcare; Provider Profiling; Healthcare Network Efficiency; Data Integration; Machine Learning (ML); Provider Affiliation Tracking; Healthcare Data Unification; Patient-Centric Care
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Chandra Prakash Singh. AI-powered provider profiling: enhancing healthcare network efficiency and transparency. World Journal of Advanced Research and Reviews, 2024, 24(3), 3191-3195. Article DOI: https://doi.org/10.30574/wjarr.2024.24.3.3941