Health Care Professionals’ Experiences and Opinions About Generative AI and Ambient Scribes in Clinical Documentation: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Generative artificial intelligence (GenAI) leverages large language models (LLMs) that are transforming health care. Specialized ambient GenAI tools, like Nuance Dax, Speke, and Tandem Health, "listen" to consultations and generate clinical notes. Medical-focused models, like Med-PaLM, provide tailored health care insights. GenAI's capability to summarize complex data and generate responses in various conversational styles or literacy levels makes it particularly valuable since it has the potential to alleviate the burden of clinical documentation on health care professionals (HCPs). While GenAI may prove to be helpful, offering novel benefits, it comes with its own set of challenges. The quality of the source data can introduce biases, leading to skewed recommendations or outright false information (so-called hallucinations). In addition, due to the conversational nature of chatbot responses, users may be susceptible to misinformation, posing risks to both safety and privacy. Therefore, careful implementation and rigorous oversight are essential to ensure accuracy, ethical integrity, and alignment with clinical standards. Despite these advances, currently, no review has investigated HCPs' experiences and opinions about GenAI in clinical documentation. Yet, such a perspective is crucial to better understand how these technologies can be safely and ethically adopted and implemented in clinical practice. OBJECTIVE: We aim to present the protocol for a scoping review exploring HCPs' experiences and opinions about GenAI and ambient scribes in clinical documentation. METHODS: This scoping review will be carried out following the methodological framework of Arksey and O'Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews) checklist. Relevant papers will be searched for in PubMed, IEEE Xplore, APA PsycInfo, CINAHL, and Web of Science. The review will include studies published between January 2023 and September 2025. Studies will be included that represent original peer-reviewed work that explores HCPs' experiences and opinions about the use of GenAI or ambient scribes for clinical documentation. Data extraction will include publication type, country, sample characteristics, clinical setting, study aim, study design, research question, and key findings. Study quality will be assessed using the Mixed Methods Appraisal Tool. RESULTS: The results will be presented as a narrative synthesis structured along the key themes of the evidence mapped. Data will be collated and presented in charts and tabular format. Findings will be reported in a peer-reviewed scoping review. CONCLUSIONS: This will be the first scoping review that considers HCPs' experiences and opinions about GenAI and ambient scribes in clinical documentation. The results will clarify how HCPs use-or avoid using-GenAI in daily health care work. This insight will help address perceived benefits, risks, expectations, and uncertainties. It may also reveal key research gaps in the field. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73602.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.129 | 0.200 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".