Abstract
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.
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.
| Original language | English |
|---|---|
| Article number | e70554 |
| Number of pages | 20 |
| Journal | Journal of Evaluation in Clinical Practice |
| Volume | 32 |
| Issue number | 5 |
| Early online date | 13 Aug 2026 |
| DOIs | |
| Publication status | Published - 13 Aug 2026 |
Bibliographical note
Copyright © 2026 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Data Access Statement
The data sets generated and analysed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request.Keywords
- Artificial Intelligence/standards
- Documentation/standards
- Electronic Health Records/organization & administration
- Humans
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