Effects of medical scribes on patients, physicians, and safety: A scoping review
Bibliographic record
Abstract
A scoping review was conducted to investigate the effects of medical scribes on physician and patient satisfaction, physician burnout, the educational experience of medical students and residents, risk, and safety. The databases PubMed, EMBASE, and CINAHL were searched for the years 2000-2020. Relevant studies were analyzed qualitatively. Literature analysis found that medical scribes increase physician satisfaction and decrease physician burnout, while having minimal impact on patient satisfaction. Patient impressions of scribes tend to be neutral to positive. The effects of scribes on medical student and resident education appear positive in preliminary results. Scribe-generated notes seem to be of equal or greater quality compared to physician-generated notes, though few studies have examined this issue. The impact of scribes on risk and safety has not been fully studied. Few studies of medical scribes have been conducted in Canada, and only one has been published in a peer-reviewed journal. Medical scribes are a promising solution to the growing challenge of physician documentation-related burden fueled by electronic health records and electronic medical records. Studies on the impact of scribes in countries other than the United States are needed. Administrative hurdles to the implementation of scribes in Canadian hospitals could be a barrier to pilot studies in Canada.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".