Policy, Financing, and Regulatory Barriers to Adopting AI-Powered Electrocardiography Interpretation Clinical Decision Support System in Ethiopia: A Qualitative Study

Abstract

Cardiovascular diseases are a growing public health challenge in Ethiopia, worsened by limited access to diagnostics, including ECG, and shortages of specialized expertise. AI-powered ECG offers potential to improve diagnostic accuracy, efficiency, and access in resource-limited settings, but its adoption is influenced by policy, regulatory, financing, and governance factors, which are not well understood in Ethiopia. This study explored these system-level determinants using qualitative methods from September to October 2025 across federal institutions, four regions, and five tertiary hospitals. Twenty-five stakeholders, including policymakers, regulators, digital health experts, and hospital leaders, were interviewed. Data were transcribed verbatim, coded inductively, and analyzed thematically. Six themes emerged: policy and governance, regulatory frameworks, financing and cost considerations, data governance and bias, integration barriers, and sustainability recommendations. Findings showed AI-powered ECG interpretation aligns with Ethiopia's digital health and noncommunicable disease priorities, but the country lacks AI-specific health policies, clear regulations, and dedicated budgets. Financing is largely donor-dependent, data governance and algorithmic bias remain concerns, and infrastructure gaps and digital skill shortages limit readiness. Study participants recommended learning from prior digital health projects, coordinated scale-up, phased implementation, and continuous monitoring. Effective adoption will require context-specific policies, sustainable financing, robust regulation, strong data governance, and careful system integration to ensure equitable, responsible, and sustainable use.