Automating Clinical Documentation with Digital Scribes: Understanding the Impact on Physicians
Bibliographic record
Abstract
Recently, digital scribe systems have been gaining popularity as a possible work-around solution to the Electronic Medical Record (EMR) documentation burden that affects many physicians. The proposed system would automate the clinical summary physicians take by capturing and extracting the patient-physician conversation during the consultation. While promising in concept, how this system would apply to real-world use and its limitations are still not well understood. To examine these issues, we designed a digital scribe prototype to generate notes of different qualities ranging from the reality of current state-of-the-art technology to the potential of future implementations. We conducted a ”Wizard of Oz” study with 24 primary care physicians using our digital scribe prototype in 4 simulated medical encounters followed by a semi-structured interview. This exploratory study provides an understanding of physicians’ interaction with digitally scribed notes, their perceptions on note quality, their perceived workflow impact and several directions for improvements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".