Socio-technical risks of clinical speech-to-text systems: Transparency, privacy, and reliability challenges in AI-driven documentation

BACKGROUND: AI-driven speech-to-text (STT) documentation systems are increasingly adopted in clinical settings to reduce documentation burden and improve workflow efficiency. However, adoption has outpaced the systematic evaluation of socio-technical risks related to transparency, reliability, patient autonomy, and organizational accountability. OBJECTIVE: To develop a socio-technical framework for identifying and governing risks associated with the implementation of clinical speech-to-text systems. METHODS: This study synthesizes interdisciplinary evidence from technical automatic speech recognition research, clinical workflow and human factors studies, ethical guidance on consent and patient autonomy, and regulatory and organizational governance sources. Using a structured narrative synthesis approach, relevant literature was iteratively reviewed and thematically analyzed to identify recurring socio-technical risk mechanisms. The synthesis was used to develop a layered conceptual framework for evaluating and governing clinical speech-to-text systems. RESULTS: Findings show that clinical STT systems operate within tightly coupled socio-technical environments where model performance, audio capture conditions, clinician oversight, patient understanding, workflow design, and institutional governance are interdependent. Key risks include inconsistent disclosure and consent practices, performance disparities for accented speech and speech/voice disorders, accuracy degradation under real clinical acoustics, automation complacency and variable clinician review, unclear accountability across vendors and healthcare organizations. These risk domains informed a six-layer socio-technical governance model spanning technical, human/workflow, ethical, organizational, regulatory, and sociocultural dimensions. CONCLUSION: The study proposes a socio-technical governance framework and implementation roadmap to support the responsible deployment of clinical STT systems. The framework emphasizes transparency, patient autonomy, documentation integrity, and accountable governance to enable safe and equitable adoption of speech-based documentation technologies.