ArticleFrontiers in digital health2026
Real-world evaluation of an ambient AI scribe in Spanish outpatient care after 2.3 million uses: impact on clinician experience, semantic agreement, and workflow efficiency.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Ambient artificial intelligence (AI) documentation systems have emerged as a promising strategy to reduce electronic health record burden and support patient-centered, value-based healthcare (VBHC). Early studies report gains in efficiency and clinician well-being, but large-scale evaluations outside North America are limited. We aimed here to assess the 16-month real-world implementation of an ambient AI documentation system (Scribe) in outpatient care in Spain, focusing on its adoption, transcription semantic agreement, efficiency, and clinician experience. Methods: A retrospective, multicenter observational analysis was performed using aggregated operational data from September 2024 to December 2025. Monthly Scribe adoption, consultation duration by modality, and cosine-similarity-based semantic agreement were evaluated. Professional experience was assessed through two cross-sectional surveys, including usage-intensity stratification. Statistical analyses included descriptive metrics, Welch tests, Hedges' g, FDR correction, and reliability and correlation analyses. Results: Scribe adoption increased from 2.7% to ∼31% of all outpatient visits, totalling more than 2.33 million assisted encounters. Consultation duration showed modest differences (15.01 vs. 14.65 min), with several months favouring Scribe and convergence toward parity as workflows matured. Semantic agreement remained high and stable (87.4-89.2%). Professional experience showed improvements in five of seven domains (g = 0.25-0.41) with excellent reliability (α > 0.93) and consistent gains across usage groups. Conclusion: Large-scale deployment of ambient AI documentation was feasible, showed high semantic agreement, was well-accepted and was associated with improvements in clinician experience. Maturation patterns suggest that efficiency gains may be reinvested into patient interaction and outcome-relevant care, consistent with a VBHC-aligned hypothesis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.