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Optimizing Hip and Knee Arthroplasty Clinic Flow: A Prospective Evaluation of Artificial Intelligence Scribe
Zachary Grand1, Jonathan Brutti1, Klaudia Greer1
1Florida International University Herbert Wertheim College of Medicine, Miami, FL, USA.
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 (P < .001). Overall, physician workload measured by NASA-TLX was lower with AI assistance, with a mean total score of 30.0 (range, 26-40) vs 40.2 (range, 11-73) for traditional documentation (P = .485). The AI group showed consistently lower mean scores across all NASA-TLX subdomains, including temporal demand (5.8 vs 10.4), effort (6.8 vs 10.0), and frustration (3.4 vs 5.4).
Conclusions:
AI scribe implementation significantly reduced clinical encounter duration by approximately 20% and demonstrated promising improvements in physician workload metrics.