Currently submitted to: JMIR Medical Informatics
Date Submitted: May 9, 2026
Open Peer Review Period: May 21, 2026 - Jul 16, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Implementation Readiness and Adoption of AI-Enabled Voice Electronic Medical Records in Resource-Constrained African Health Systems: A Qualitative Study
ABSTRACT
Background:
Artificial intelligence (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 healthcare professionals’ perceptions, anticipated benefits, and concerns regarding AI voice-enabled EMRs in Ethiopia to inform context-sensitive implementation strategies.
Methods:
We conducted a qualitative, multi-site study using semi-structured written responses from 43 Ethiopian healthcare professionals recruited via purposive and maximum variation sampling between January and February 2025. Data were analyzed in Taguette using a hybrid deductive-inductive approach integrating three 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:
Three 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, 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-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. Clinical Trial: none
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