Governing AI-driven digital transformation in public healthcare: assessing administrative readiness and institutional capacity in Egypt's health system

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Background Artificial intelligence (AI) and digital health technologies are increasingly shaping how public healthcare services are planned, delivered, and governed. In Egypt, national digital health reforms have created an urgent need to assess whether public healthcare institutions have the administrative readiness, institutional capacity, and governance arrangements required for AI-enabled digital health transformation. Methods A sequential explanatory mixed-methods design was employed across 24 Egyptian governorates. Phase 1 consisted of a cross-sectional survey of 387 healthcare administrators and policymakers from Ministry of Health and Population hospitals, university hospitals, primary healthcare units, and Health Insurance Organization facilities. The survey measured eight operationalized constructs: institutional capacity, administrative readiness, organizational governance, leadership support, IT infrastructure, staff training and skills, regulatory framework, and digital health transformation success. Phase 2 comprised semi-structured interviews with 18 senior policymakers, hospital leaders, digital health consultants, health information system managers, and health policy researchers. Quantitative findings were analyzed using descriptive statistics, confirmatory factor analysis, and structural equation modeling, while qualitative data were analyzed thematically and integrated with the survey results through explanatory joint display logic. Results Among survey respondents, 62.3% were male, 71.6% held a master's degree or higher, and 58.1% had more than 10 years of professional experience. The structural model showed acceptable fit (CMIN/DF = 2.14, CFI = .96, TLI = .95, RMSEA = .045, SRMR = .038) and explained 68% of the variance in perceived digital health transformation success. Institutional capacity, administrative readiness, and organizational governance were positively associated with transformation success. The specified indirect pathways through leadership support, IT infrastructure, staff training and skills, and regulatory framework were also significant and are interpreted as hypothesis-generating associations because of the cross-sectional design. Interview findings helped explain the quantitative patterns by identifying fragmented governance, weak infrastructure, workforce digital literacy gaps, regulatory ambiguity, and resource allocation constraints. Conclusions Egypt's public healthcare system faces interrelated institutional, administrative, workforce, infrastructure, and regulatory barriers to AI-enabled digital health transformation. Strengthening governance coordination, infrastructure equity, workforce capability, and AI-specific regulatory safeguards is essential before large-scale AI deployment can be reliably translated into public value.

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