Practicing at vRad
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Your patients are in good hands. All vRad radiologists participate in the most comprehensive, AI-supported quality assurance program in radiology.
vRad’s accuracy rate of 99.87% has remained consistently high for many years. With just 1.3 major misses per 1,000 reads, our error rate is lower than published benchmarks like the Wilson Wong study (JACR, 2005).
Mistakes happen in healthcare. Minimizing them or catching them early can significantly reduce the impact on patient care.
Since 2004, vRad’s Quality Assurance (QA) Program has focused on real performance improvement. Peer reviews and standardized discrepancy coding provide each radiologist with personalized but objective feedback.
vRad QA is central to our mission of delivering exceptional care.
Potential discrepancies are identified through client submission and vRad’s routine internal review, which overreads 2% of final interpretations. Clients submit cases via our online QA portal.
Discrepancy cases are reviewed by the interpreting radiologist plus at least one member of the QA Committee, ensuring a thorough and standardized assessment.
Clients are promptly notified of QA submissions, with clear updates on the case status. Case resolutions are communicated by fax or email and can be made verbally upon request.
Discrepancies are coded and standardized for benchmarking, with deidentified data accessible to clients for quality reviews and reporting.
Radiologist performance is regularly reviewed using standardized, objective metrics to maintain excellence. Ongoing Professional Performance Evaluations (OPPE) and Focused Professional Performance Evaluations (FPPE) address issues as needed, driving high standards and accountability.
Led by Dr. Julie Shaffrey for more than two decades, vRad’s QA Committee is composed of top radiologists across subspecialties.
Julie Shaffrey, MD
Director of Quality Assurance
Michael Novick, MD
Associate Director of Quality Assurance
Sara Banerjee, MD
Neuroradiology
Donald Bitto, MD
Nuclear Medicine
Yulia Bronstein, MD
Neuroradiology, Body Imaging
Kelcey Elsass, MD
General Radiology
Liat Kaplan, MD
Musculoskeletal
John Krol, MD
General Radiology
Joshua Morais, MD
Neuroradiology
Michael Rethy, MD
MSK
Christian VanKirk, MD
General Radiology
vRad AI models for quality assurance augment our robust radiologist QA program. AI compares the radiology report to the images immediately after radiologist sign-off, flagging potential missed critical findings for rapid review by a QA radiologist. If an error is confirmed, a radiologist quickly notifies the care team to ensure patient safety and addends the report.