PROTOCOL FOR DEVELOPMENT OF A TOOL FOR ASSESSING THE DIGITAL MATURITY AND ECG DATA READINESS OF HEALTH FACILITIES FOR ARTIFICIAL INTELLIGENCE USING THE DELPHI METHODOLOGY.

Methodology: A mixed-methods study in three phases will be conducted. Phase I: A scoping review of PubMed, IEEE Xplore, and Google Scholar to identify domains and indicators for digital maturity for AI in various clinical fields. Phase II: A modified Delphi consensus process with 15–20 multidisciplinary experts in cardiology, biomedical engineering, AI, health informatics, and digital health. Consensus defined as median ≥7, ≥80% agreement on 7–9, and IQR ≤2 over 2–3 rounds. Phase III: Field validation of the AI-ECG Digital Maturity and ECG Data Readiness Assessment Tool (AI-ECG DMAT) in purposively selected public and private secondary and tertiary hospitals in Abuja, Nigeria. Reliability, validity, and feasibility will be assessed using Cronbach’s alpha, ICC, and stakeholder feedback.