At vRad, AI is helping manage regulatory compliance and adherence to best practices to ensure quality care, which should avoid unnecessary medical expenses and mitigate malpractice risk.
How AI operates in reporting
Using natural language processing (NLP), the vRad Imaging Platform reviews all radiology reports, flagging potential clinical improvements or compliance issues. For example, a radiologist will be alerted if the pathology has a specific reporting requirement — such as Fleischner criteria for a lung nodule, or a LI-RAD score for a liver lesion — or when intelligent validation detects left/right or male/female anatomic discrepancies.
What’s more, our AI-enabled imaging platform employs NLP in real time. As the report is being created, the system will prompt the radiologist for required information based on report content.
Watch the Recording
AI IN RADIOLOGY - BEYOND THEORY
with Imad B. Najim CIO, vRad, Benjamin W. Strong, CMO, vRad, and partner Mandy Long, IBM Watson Health
Streamlining the radiologist’s experience
AI streamlines the reporting process while helping ensure accuracy. If an issue is detected, the system prompts the radiologist with steps to address it. Where reporting requirements are needed, the radiologist can choose to link to the relevant grading or coding system for the pathology presented. For example: A radiologist creates a report on a thyroid ultrasound describing a thyroid nodule. The system will check the report for a TI-RAD score. If missing, it will prompt the radiologist to “Please include a TI-RAD score,” and include a link to how a TI-RAD score is determined, if needed.
Greater efficiency and reduced risk
AI has enabled vRad to scale our radiology practice without compromising the quality and veracity of each patient diagnosis. AI helps us more effectively manage compliance with evolving regulatory requirements, without burdening our radiologists.
By actively checking each report, AI significantly reduces the risk of a delayed result or incomplete record for billing based on a misplaced keystroke, transcription error, omitted merit- based incentive data or individual mistake — a huge advantage for a practice processing nearly 7.5 million studies annually.