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Implementation Readiness and Adoption of AI-Enabled Voice Electronic Medical Records in Resource-Constrained African
Melika Desalegn1, Hocheol Lee2, Dawit Wondifraw Tilahun3
1Department of Health Administration, Yonsei University, Wonju City, Gangwon, Republic of Korea.
Background:
AI-enabled voice electronic medical records (EMRs) are increasingly promoted as tools to reduce clinician documentation burden; however, empirical evidence from multilingual, resource-constrained health systems in sub-Saharan Africa remains limited.
Objective:
This study examined health care professionals' perceptions, anticipated benefits, and concerns regarding AI voice-enabled EMRs in Ethiopia to inform context-sensitive implementation strategies.
Methods:
We conducted a qualitative, multisite study using semistructured written responses from 43 Ethiopian health care professionals recruited via purposive and maximum variation sampling between April 11, 2026, and April 21, 2026. Data were analyzed in Taguette using a hybrid deductive-inductive approach integrating 3 complementary frameworks: the Consolidated Framework for Implementation Research (CFIR), the technology acceptance model (TAM), and Normalization Process Theory (NPT). Analytical rigor was strengthened through independent dual coding, structured reconciliation, reflexive memos, and a version-controlled audit trail.
Results:
Four overarching themes were identified. First, participants anticipated clear clinical benefits, including reduced typing burden, improved documentation continuity, and enhanced patient interaction, yet expressed substantial concerns about automation errors, accent-related transcription failures, and persistent infrastructural instability. Second, usability barriers, including interface complexity, inadequate training, and digital anxiety, shaped technology acceptance across cadres. Third, participants anticipated shifts in workflow, task distribution, and clinical collaboration as documentation practices evolved. Finally, ethical and governance concerns, particularly regarding data confidentiality, unclear consent procedures, and fear of surveillance, emerged as major determinants of trust. Cross-framework synthesis revealed that adoption readiness was jointly shaped by organizational capacity, usability perceptions, emotional and cognitive responses, and evolving workflow expectations.
Conclusions:
Successful implementation of AI voice-enabled EMRs in Ethiopia requires coordinated investments in digital infrastructure, locally adapted language models, strengthened data governance, and iterative user onboarding. These findings underscore the urgency of context-sensitive and ethically grounded approaches when deploying speech-based AI in low-resource health systems.
