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

AI-ENABLED HEALTH TECHNOLOGY ASSESSMENT AS AN ORGANISATIONAL CAPABILITY SYSTEM: MIXED-METHOD EVIDENCE FROM SAUDI PRIVATE HEALTHCARE | Zenodo Skip to main Communities My dashboard Log in Sign up Zenodo is currently experiencing slowness and intermittent outages due to heavy automated traffic from bots and AI crawlers. We are aware of the problem, and our team is focused on stabilizing the service. Thank you for your patience. Grow with Scientific Culture Published May 11, 2026 | Version v1 Journal article Open AI-ENABLED HEALTH TECHNOLOGY ASSESSMENT AS AN ORGANISATIONAL CAPABILITY SYSTEM: MIXED-METHOD EVIDENCE FROM SAUDI PRIVATE HEALTHCARE Authors/Creators Eng. Khalil Rafiq Al-Battat, Yousef Rafiq Al-Battat, Shankar Subramanian Iyer (Contact person) 1 Show affiliations 1.

DBA Researcher, The Catholic University of Murcia (UCAM), Guadalupe, Murcia, Spain Master of Electrical Engineering Graduate, Warsaw University of Technology (Politechnika Warszawska), Poland DBA, Faculty, Westford University College, Sharjah, UAE Description 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. Files 649.SC20260420102631319_EA_117_volume+12_issue+4_2026+(1)_SC_Foreign+Ready+final+1.pdf Files (520.9 kB) Name Size Download all 649.SC20260420102631319_EA_117_volume+12_issue+4_2026+(1)_SC_Foreign+Ready+final+1.pdf md5:4f59889dedcbb8827462074e7d9bf960 520.9 kB Preview Download 42 Views 20 Downloads Show more details All versions This version Views Total views 42 42 Downloads Total downloads 20 20 Data volume Total data volume 12.5 MB 12.5 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords AI-enabled HTA; Health Technology Assessment; Organisational Capability; Healthcare Management; Saudi Arabia; Private Healthcare; Mixed Methods Details DOI DOI Badge DOI 10.5281/zenodo.20126509 Markdown [](https://doi.org/10.5281/zenodo.20126509) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.20126509.svg :target: https://doi.org/10.5281/zenodo.20126509 HTML <a href="https://doi.org/10.5281/zenodo.20126509"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.20126509.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.20126509.svg Target URL https://doi.org/10.5281/zenodo.20126509 Resource type Journal article Publisher Zenodo Published in SCIENTIFIC CULTURE, 12(2.1), ISSN: 2407-9529, 2026. Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Citation Export Technical metadata Created May 11, 2026 Modified May 11, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies

Related documents

• Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.#265412
• Cybersecurity in health sector: a systematic review of the literature#1179100
• Strengthening health systems in low and middle-income countries: performance determinants and pathways to universal health coverage.#330862
• Patient Perceptions of Artificial Intelligence-Supported Shared Decision-Making in UK Primary Care for Multiple Long-Term Conditions: Qualitative Study.#342642
• HOME FIRST Pilot: a Study of Early Supported Discharge in Patients With Lower Respiratory Tract Infections#833359