Overview

Improving echocardiography lab efficiency is a growing operational priority as imaging volumes continue to rise and sonographer workforce pressures intensify. While AI-guided echocardiographic interpretation is widely hypothesised to save time, its real-world impact on clinical throughput and sonographer quality of life has remained poorly studied. This mixed-methods time-motion study from the Endeavor Health Cardiovascular Institute at NorthShore University HealthSystem in Glenview, Illinois, set out to objectively evaluate whether an AI-driven workflow optimises both echo lab efficiency and sonographer job satisfaction in routine clinical practice.

 

Study Design

Standard adult transthoracic echocardiograms performed by three independent sonographers of varying experience levels were tracked across two workflow arms: an AI-assisted workflow using Us2.ai, and a traditional manual contouring protocol. Quantitative acquisition-to-completion study durations were measured for 47 TTEs in total. Subgroup analyses were stratified by clinical setting, comparing inpatient and outpatient cohorts.

Quantitative time measurements were paired with a post-study qualitative user experience survey assessing perceived click burden, impact on scanning flow, and preference for future adoption, making this one of the few studies to capture both the operational and experiential dimensions of AI integration in an echo lab.

 

Key Results

Study Duration:

  • The AI workflow reduced total study duration from 31 minutes 39 seconds to 20 minutes 35 seconds, a mean saving of 11 minutes 04 seconds per study (35.0% time saving, p<0.0001)
  • Time savings were consistent across clinical settings, with a 39.9% reduction in the inpatient cohort (p=0.0021) and a 32.5% reduction in the outpatient cohort (p=0.0029)

Sonographer Experience:

  • 100% of participating sonographers reported a much lower click burden with the AI workflow
  • All three sonographers reported that AI enhanced both their natural scanning flow and overall echo lab efficiency
  • All three expressed a preference for future adoption of the AI-based workflow

 

Why This Matters

This study is notable for going beyond time measurements to capture the sonographer experience, an aspect of AI integration that is rarely quantified. The finding that all participating sonographers preferred the AI workflow and reported meaningfully lower click burden addresses a real concern in echo lab adoption: that AI tools add complexity rather than reduce it.

A 35% reduction in study duration represents a substantial operational gain in a busy academic tertiary care hospital. Across a high-volume echo lab, this translates to significantly increased daily capacity without additional staffing, faster report turnaround, and a reduction in the administrative burden that contributes to sonographer burnout.

The authors note that structuring clinical workflows around validated AI tools may preserve sonographer autonomy while saving time and improving echo lab throughput, a finding with direct implications for how echo labs approach AI integration.

 

Conclusion

Implementation of an AI-driven echocardiography workflow using Us2.ai objectively reduces total study time by over one-third in a large academic tertiary care hospital across both inpatient and outpatient settings, while reducing sonographer click burden and enhancing scanning flow. These findings support the role of AI in improving both the operational efficiency and the working experience of echocardiography laboratories.

 

View our interview with Dr. Ilya Karagodin on his experience using Us2.ai in clinical workflows at Endeavor Health →

 

Mylavarapu, M., Stoilova, M., Lugova, O., Sullivan, M., Sanagala, T., & Karagodin, I. (2026). Artificial Intelligence Optimizes Efficiency and Sonographer Workflow in Routine Adult Transthoracic Echocardiography: A Mixed-Methods Time-Motion Study. Journal of the American Society of Echocardiography. https://doi.org/10.1016/j.echo.2026.08.016