AI-ENABLED HEALTH TECHNOLOGY ASSESSMENT AS AN ORGANISATIONAL CAPABILITY SYSTEM: MIXED-METHOD EVIDENCE FROM SAUDI PRIVATE HEALTHCARE

This study examines whether AI-enabled health technology assessment (HTA) improves decision-making effectiveness in Saudi private healthcare directly or mainly through organisational, data, institutional and stakeholder pathways. A sequential explanatory mixed-methods design combined PLS-SEM analysis of 86 survey responses with 15 semi-structured interviews with senior participants involved in strategy, clinical leadership, digital transformation, data, finance, regulation and HTA-related roles. Decision value was mainly mediated. Organisational resources and capabilities significantly strengthened HTA process automation (β = 0.7669, p = 0.0064). Access to quality data showed the strongest total effects and a strong direct effect on regulatory and institutional factors (β = 0.8820, p < 0.001). The direct HTA-process-to-decision path was negligible. Interview evidence showed that HTA functioned as a deliberative support platform whose value depended on data quality, governance legitimacy and stakeholder alignment. The study is cross-sectional and based on a moderate non-probability sample; selected downstream constructs were closely coupled, so the model should be interpreted as explanatory evidence rather than causal proof. Managers should treat AI-enabled HTA as capability building centred on data governance, interoperability, cross-functional governance and stakeholder participation. The article reframes AI-enabled HTA as an organisational capability system and shows that decision value is realised mainly through mediated capability pathways rather than through a simple direct technology effect.