ArticleArthroplasty today2026
Optimizing Hip and Knee Arthroplasty Clinic Flow: A Prospective Evaluation of Artificial Intelligence Scribe Technology.
Article in Arthroplasty today, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The increasing burden of clinical documentation contributes to physician inefficiency and burnout, with electronic health record implementation significantly increasing documentation time from 16% to 28% of clinical time. While human medical scribes have shown benefits, artificial intelligence (AI) scribes represent a promising solution. This study evaluated the impact of AI scribe technology on clinical encounter duration and physician workload in a high-volume hip and knee arthroplasty clinic. Methods: This prospective quality-improvement cohort study was conducted by a single surgeon at one institution from November 2024 to February 2025. Hip and knee arthroplasty patients were divided into 2 cohorts: a control group using traditional physician and medical assistant documentation within the electronic medical record and an intervention group using AI-powered clinical documentation. Clinical encounter duration was assessed by 3 independent reviewers using direct observation and stopwatch timing. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) was completed after each clinic day to quantify subjective physician workload across 6 domains on a 20-point scale. Results: A total of 112 patient encounters were analyzed: 55 with AI scribes and 57 with traditional documentation. Encounter time was significantly shorter in the AI group, with a mean of 12.03 minutes (range, 4.01-22.13) compared to 15.06 minutes (range, 6.20-32.27) for traditional documentation ( Conclusions: AI scribe implementation significantly reduced clinical encounter duration by approximately 20% and demonstrated promising improvements in physician workload metrics.
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.