Toward an Adaptive AI/ML-Based QA Framework with HRM Integration for Inclusive and Secure Healthcare Solutions in Edge Environments

Abstract

In the evolving landscape of digital healthcare, ensuring quality, inclusivity, and security in service delivery remains a critical challenge. This paper proposes an adaptive Artificial Intelligence (AI) and Machine Learning (ML)-based Quality Assurance (QA) framework that integrates Human Resource Management (HRM) principles to address these challenges in edge computing environments. The framework is designed to support inclusive healthcare solutions that respond dynamically to contextual demands, resource constraints, and human factors. By embedding HRM strategies into the QA loop, the system enhances decision-making, accountability, and personnel responsiveness, ensuring a more human-centered approach to digital health service validation and monitoring. Edge computing is leveraged to enable real-time processing and decentralized intelligence, reducing latency and supporting secure, context-aware analytics at the point of care. The integration of adaptive AI/ML models ensures the system can learn from real-world data, detect anomalies, and respond to emerging threats or inefficiencies. This research contributes a novel interdisciplinary approach that aligns technical efficiency with human and ethical considerations in healthcare. The proposed framework was evaluated through simulations and qualitative analysis, demonstrating its potential to improve operational trust, inclusivity, and overall system robustness in resource-constrained healthcare environments.