Evaluation of an AI medical scribe after 236 153 notes generated across care levels in a European health system (Preprint)
Bibliographic record
Abstract
Background: Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. Artificial intelligence (AI) medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous, and data from European health systems are especially scarce. This evaluation combines 2 complementary data sources: objective editing metadata from 236,153 notes generated by 1295 clinicians, describing operational editing behavior within the AI medical scribe, and paired self-reported survey responses from 177 fully onboarded clinicians, capturing perceived change in documentation time and clinician experience. Objective: This study aimed to evaluate the association of an AI medical scribe on documentation time and clinician experience. Methods: This observational real-world evaluation was conducted between April 26, 2024, and October 27, 2025, using retrospective paired ratings. The study was carried out across multiple specialties in primary, secondary, and hospital care within Capio Ramsay Santé, a large integrated health care provider operating in Sweden. Eligibility was limited to fully onboarded users, defined as clinicians who had used the scribe for at least 3 months, created more than 100 notes, generated at least 1 document or certificate, and used the conversational edit ("Add or adjust") feature at least once. Results: Following the introduction of the AI medical scribe, the estimated time spent on documentation per note was lower than before (4.72 vs 6.69 minutes; -29%, P<.001). On a 5-point Likert scale, ratings for the ability to work without stress related to administrative tasks were higher after introduction than before (mean 3.14 vs 2.41; P<.001; median change 0 points, 95% CI 0-1), as were ratings for perceived presence with patients (mean 4.33 vs 3.73; P<.001; median change 0 points, 95% CI 0-1). The median editing time was 93 seconds, and it did not decrease significantly over continued use. Conclusions: Among sustained, fully onboarded adopters in a European health care system, use of an AI medical scribe was associated with reductions in self-reported documentation time, administrative stress, and increase of presence with patients, consistent with findings from prior US-based studies. Because the survey cohort represents a highly selected subgroup of users who adopted and continued using the tool mainly in general practice, these associations may not generalize to clinicians who discontinued use or never fully adopted the scribe, and the generalizability across specialties remains unverified. The single-arm observational design and reliance on retrospective self-report are important considerations when interpreting these associations. A limitation of this analysis is that 138,196 notes were excluded because their recorded editing time was 0; these notes may have been used as generated, used as a starting point and later modified in the medical record system, or discarded, which limits the operational interpretation of the editing-time findings.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".