Evaluation of Socio‐Technical Mechanisms Shaping AI Scribe Documentation Failures: A Netnographic Study

BACKGROUND: Artificial Intelligence (AI) scribes are increasingly adopted to address electronic health record (EHR) documentation burden. Although early evaluations report perceived efficiency gains and reduced after-hours work, findings on documentation quality and safety remain mixed. Reported issues, including omissions, attribution mistakes, and hallucinated content, raise concerns about potential clinical, administrative and medico-legal risks. Existing evaluations largely focus on performance metrics, offering limited insight into the socio-technical conditions shaping real-world experiences and outcomes. AIM: To examine how interacting socio-technical conditions influence AI scribe use, reported problems and associated risk implications in clinical documentation. METHODOLOGY: A netnographic analysis was conducted of 2267 relevant data segments from 952 documents across 162 Reddit threads (2023-2025) drawn from clinician-oriented communities. Data were collected using a structured query design via the Python Reddit API Wrapper. Data were analysed using an inductive-abductive qualitative approach and organised through a socio-technical lens across technology, organisation, person and environment domains. RESULTS: Contributors' accounts suggested that reported AI scribe problems were associated with interacting technological constraints, including template rigidity, integration gaps and reliability issues; organisational governance and billing pressures; environmental time constraints; and individual verification practices. These conditions appeared to operate through three mediating mechanisms: adoption and configuration practices, workflow coupling and documentation targets. Reported problems included content-related issues, such as misattribution, hallucinations and omissions, as well as workflow disruptions, including latency, crashes and copy-and-paste friction. Clinicians described potential clinical, administrative and medico-legal risks as contingent on integration quality, governance clarity and review capacity. CONCLUSION: AI scribe safety is not solely a function of model accuracy. The findings suggest that documentation problems may arise through socio-technical interactions that influence whether errors are identified, corrected or carried forward. Safe deployment requires strengthening integration, governance and verification processes alongside technical performance.