Resident physician perspectives on ambient AI scribing in academic family medicine
Bibliographic record
Abstract
ABSTRACT While ambient artificial intelligence (AI) scribes have been received positively by primary care physicians, the perceptions of resident physicians are not yet unclear. We conducted a qualitative study involving focus groups with first and second-year family resident physicians from a single urban academic family health team to gauge their understanding of ambient AI scribing and their perceptions of its potential impact on patient care. Seven resident physicians participated in two focus groups. Sessions were audio recorded and transcribed verbatim, then analyzed inductively to identify themes. We categorized the findings into five themes: 1) understanding of and exposure to AI, 2) perceived impact of ambient AI scribing on the practice of family medicine, 3) perceived impact on the cognitive load of charting, 4) performance and accuracy of ambient AI scribes, and 5) implications for adoption. Residents in this study reported minimal exposure to AI and concerns regarding the impacts of ambient AI scribing on the documentation process and quality of notes. Future research should explore the potential effects of ambient scribes on resident documentation prior to testing in practice.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".