Highest Quality in Radiology – Positive Impact on Patient Care

vRad is not your typical radiology group. Our robust quality assurance program includes strict performance accountability through quarterly reviews of major and minor misses by modality. Our technology platform even automatically routes studies to the appropriate subspecialist. 

The result? An industry-leading accuracy rate of 99.8% that leads to fewer misdiagnoses and better patient care.
 

Quality Assurance at vRad

A robust quality assurance program has been the foundation of vRad quality since the program was developed in 2004.
 

Quality Assurance Committee
Fourteen radiologists, representing a cross-section of subspecialties under the direction of Julie Shaffrey, MD, make up the vRad Quality Assurance (QA) Committee. The QA Committee:

• Reviews every reported discrepancy with the reading radiologist
• Codes the reported discrepancy using multiple, standardized parameters
• Enters the data into a learning database going back to 2004

 
Regular Radiologist Performance Evaluation
The massive learning database we have accumulated allows vRad to closely monitor and improve the performance of our radiologists.

• All radiologists receive a monthly, anonymous report so they know exactly where they stand within the practice. This promotes an atmosphere of professional pride and internal competition.
• Quarterly performance reviews against practice benchmarks ensure accountability and initiate Professional Improvement Plans (additional training, etc.) or in some cases, dismissal.

 
Positive Impact on Patient Care
The model of continuous practice improvement employed at vRad produces the highest accuracy rate in radiology and a positive impact on patient care: 99.8%. Independent studies put radiology accuracy rates somewhere between 97%¹ and 98.9%².  The difference between these rates and vRad’s 99.8% rate may seem small until you consider the impact on patient care. In fact, an improvement of 1.8% in a single year would mean thousands of fewer misdiagnosed patients for some large hospitals. The costs of inaccuracy, in terms of patient time-to-diagnosis, patient and physician satisfaction, and other key hospital metrics, are significant.

 ¹ Soffa, D. (2004). Disagreement in interpretation: A method for the development of benchmarks for quality assurance in imaging. Journal of the American College of Radiology, 1(3), 212-217.
 ² Wong, W. (2005). Outsourced teleradiology imaging services: an analysis of discordant interpretation in 124,870 cases. Journal of the American College of Radiology, 2(6), 478-484.