Identifying the Causal Factors of Digital Health Policymaking with an Artificial Intelligence Approach in Universities of Medical Sciences in Iran’s Macro-Region One
Keywords:
Digital Health Policymaking, Artificial Intelligence, Digital Health, Grounded Theory, Universities of Medical Sciences, Health GovernanceAbstract
This study aimed to identify and explain the causal factors influencing artificial intelligence-based digital health policymaking in universities of medical sciences located in Iran’s Macro-Region One. This applied qualitative study was conducted using grounded theory based on the paradigm model proposed by Strauss and Corbin. Participants comprised experts in health policymaking, health management, and health information technology who were recruited through purposive and snowball sampling. Data were collected through semi-structured interviews. Theoretical saturation was achieved after 11 interviews, and one additional interview was conducted to confirm saturation. The collected data were analyzed through open, axial, and selective coding using MAXQDA 2024. The trustworthiness of the findings was evaluated according to Lincoln and Guba’s criteria of credibility, transferability, dependability, and confirmability. Coding reliability was also confirmed through an inter-coder agreement exceeding 80%. The analysis generated 76 open codes, 11 axial categories, and one core category entitled “Causal Factors of Artificial Intelligence-Based Digital Health Policymaking.” The axial categories were organized into six dimensions: economic and financial, legal and policy, social and cultural, technological and data-driven, structural and human resources, and clinical and managerial factors. The principal causal drivers included increasing economic pressures and the need for cost control, public health crises, the rapid expansion of health data, changing patient expectations, regulatory requirements, fragmented information systems, shortages of specialized personnel, the need to reduce medical errors, international competitiveness, emerging intelligent technologies, and the growing complexity of clinical and managerial decision-making. Artificial intelligence-based digital health policymaking is a multidimensional phenomenon whose successful development requires the simultaneous consideration of economic, legal, social, technological, structural, and clinical requirements. The proposed framework can provide an indigenous basis for strategic decision-making, health-information integration, improved health governance, more efficient resource allocation, and the development of intelligent healthcare services in universities of medical sciences.
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