Understanding systemic barriers to AI-human collaboration integration for quality improvement in public health...

Health systems globally are under increasing pressure due to pandemics, resource constraints, and rising demand for quality and equitable care. The integration of artificial intelligence (AI) with quality improvement methodologies such as lean six sigma (LSS) offers significant potential to enhance efficiency, decision-making, and service delivery in public health systems. However, the adoption of Human-AI collaboration in such contexts remains limited due to systemic barriers. This study investigates the interrelated challenges to Human-AI collaboration in LSS-based quality assurance, with implications for resilient and sustainable public health systems. Drawing on the Technology-Organization-Environment (TOE) framework, the study conceptualizes barriers as part of a complex socio-technical system. Using a Fuzzy DEMATEL approach, expert opinions were analyzed to identify and prioritize 16 barriers. Findings reveal that data quality and integration, system interoperability, lack of leadership vision, and insufficient stakeholder engagement are key causal barriers that significantly influence downstream challenges such as resistance to change and lack of trust in AI. These findings provide important insights for designing resilient, equitable, and data-driven public health systems in line with global health priorities. The study contributes to the literature by bridging operations management and public health system resilience, offering actionable strategies for policymakers and healthcare organizations to enhance AI-enabled quality improvement.