Credential-Aware AI Matching for Healthcare Professionals Using Knowledge Graphs and Retrieval-Augmented Generation
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DOI:
https://doi.org/10.67228/30715717/IJDEIC-V9I3P102Published 08-10-2026
Artificial Intelligence, Healthcare Professionals, Credential-Aware Matching, Knowledge Graphs, Retrieval-Augmented Generation, Workforce Recommendation, Credential Verification, Explainable AI Issue
Section
ArticlesHow to Cite
[1]C. Butani, “Credential-Aware AI Matching for Healthcare Professionals Using Knowledge Graphs and Retrieval-Augmented Generation”, IJDEIC, vol. 9, no. 3, pp. 33–46, Aug. 2026, doi: 10.67228/30715717/IJDEIC-V9I3P102.Abstract
The growing complexity of healthcare workforce management requires intelligent systems capable of matching qualified professionals to clinical roles while accounting for credentials, specialties, competencies, experience, and regulatory requirements. This study proposes a credential-aware artificial intelligence matching framework that integrates Knowledge Graphs and Retrieval-Augmented Generation to improve the accuracy, transparency, and contextual relevance of healthcare professional matching. The framework represents professional credentials, licenses, certifications, clinical expertise, institutional requirements, and workforce relationships within a structured knowledge graph. Retrieval-Augmented Generation is then used to retrieve verified contextual information and support evidence-based matching and ranking decisions. Unlike conventional recommendation systems that rely mainly on profile similarity or historical interaction data, the proposed approach incorporates credential validity and professional eligibility directly into the matching process. The study evaluates the framework using relevance, precision, recall, F1-score, ranking quality, and explainability measures against conventional matching approaches. The proposed architecture is expected to reduce inappropriate matches, improve workforce allocation, and provide interpretable justification for recommendations. The study also addresses privacy, algorithmic bias, data quality, regulatory compliance, and credential verification challenges. The framework provides a foundation for safer, more reliable, and accountable AI-assisted healthcare workforce matching.
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How to Cite
[1]C. Butani, “Credential-Aware AI Matching for Healthcare Professionals Using Knowledge Graphs and Retrieval-Augmented Generation”, IJDEIC, vol. 9, no. 3, pp. 33–46, Aug. 2026, doi: 10.67228/30715717/IJDEIC-V9I3P102.