Clinician Perspectives on Ambient AI Scribes in the ICU: A Qualitative Study of Acceptability and Integration in Team-Based Communication and Documentation (Preprint)
Bibliographic record
Abstract
Background: In intensive care unit (ICU) settings, structured team-based communication, such as multidisciplinary rounds, handoffs, and goals-of-care discussions, is foundational to high-quality care. However, accurately documenting these complex discussions in the medical record remains a challenge due to time pressures, documentation burdens, and competing clinical demands. Ambient artificial intelligence (AI) scribes, which passively transcribe and summarize spoken interactions, offer a potential solution to assist ICU clinicians with documentation. Yet, little is known about how ICU clinicians perceive the integration of these tools into their high-stakes, collaborative workflows. Objective: This study explores clinicians' perceptions of integrating ambient AI scribes into structured team-based ICU discussions, including multidisciplinary rounds, handoffs and transitions of care, and goals-of-care discussions, with the broader goal of informing the implementation of these scribes into real-world ICU clinical workflows. Methods: Interviews and focus groups were conducted with ICU clinicians, including nurses, attendings, trainees (residents/fellows), respiratory therapists, and advanced practice practitioners, who routinely participate in structured ICU discussions. Transcripts were analyzed using grounded theory to identify documentation needs, barriers to documentation, and considerations for the implementation of ambient AI scribes in the ICU setting. Results: A total of 52 individuals, including 18 ICU attendings, 5 advanced practice practitioners, 10 ICU trainees, 9 ICU nurses, and 10 ICU respiratory therapists, participated. Clinicians emphasized the importance of accurate documentation, but noted persistent barriers such as time constraints, documentation burden, and competing teaching and patient care responsibilities. Clinicians expressed enthusiasm about ambient AI scribes' potential to reduce documentation burden and improve quality, but requested personalization of outputs, robust consent protocols, and transparency around data use. Participants viewed ambient AI scribes as a promising tool to enhance both documentation fidelity and communication quality in ICU settings. Successful implementation may be contingent upon transparent data governance, specialty-specific customization, and sustained efforts to build clinician trust. Conclusions: ICU clinicians were optimistic about the potential of ambient AI scribes to ease documentation burden and improve the capture of critical clinical discussions, but expressed concerns over transparency regarding data use. Successful implementation may depend on clinician training, customization of output, and transparent institutional policies on data use and consent.
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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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".