Maverick Medical AI Announces Completion of The Implementation of Its Real-Time Autonomous Medical Coding Solution at RadNet
Successful deployment achieved across all RadNet sites and 10 million medical reports processed to date
Maverick Medical AI announced today the completion of a successful deployment of its real-time autonomous AI-powered medical coding solution across all 399 sites of RadNet, Inc. (NASDAQ:RDNT), a national leader in providing high-quality, cost-effective outpatient diagnostic imaging services.
RadNet integrated Maverick Medical AI's mCoder solution to automate the medical coding process. Using deep learning and synthetic data generation, mCoder analyzes reports to accurately generate reimbursement codes in real-time. RadNet has successfully processed more than 10 million reports using Maverick's technology to date.
"When medical coding is performed in real-time, it helps us improve revenue cycle efficiency. Maverick's solution enables us to reduce errors, avoid billing backlogs, and ease the administrative burden on our radiologists. Maverick's medical coding is delivered to our systems in seconds," said Susan Hollabaugh, RadNet's senior vice-president of I.T.
In the near future, RadNet intends to implement the recently launched Maverick CodePilot™ into radiologists' reporting workflow, providing an interactive real-time interface for radiologists. Once integrated, Radiologists will be able to resolve coding-related omissions before signing their reports, thereby reducing the need for addenda and manual follow-up with radiologists which often result in billing delays.
"Our implementation of Maverick's solutions has resulted in a significant increase in our direct-to-bill rate, a testament to this technology's effectiveness," adds Hollabaugh. "We look forward to providing our radiologists with real-time coding assistance, reducing the need for coding-related addenda."
"RadNet is the country's largest outpatient imaging provider, and our solutions enable RadNet to harness deep learning and AI capabilities in medical coding to realize significant operational improvements. From radiologist workflow to revenue cycle operations, our real-time autonomous coding solution delivers benefits across the enterprise. We look forward to further collaboration," said Maverick AI CEO Yossi Shahak.
About Maverick Medical AI
Maverick Medical AI revolutionizes medical coding for healthcare providers with its AI-powered real-time Autonomous Medical Coding platform. By combining in-depth knowledge of medical coding with advanced large language models and deep learning technologies, Maverick's platform autonomously analyzes clinical notes and reports to generate accurate medical codes.
We overcome revenue cycle challenges by seamlessly integrating Medical Coding operations with Point-of-Care in real-time to optimize reimbursement. Maverick significantly surpasses industry standards with an 85% Direct-to-Bill rate, automating the medical coding process to enhance efficiency, reduce the workload on human coders, and minimize errors. This automation ensures a smoother, more reliable, and consistent revenue cycle process for healthcare providers.
For more information, visit: www.maverick-ai.com/
About RadNet
RadNet, Inc., is the leading national provider of freestanding, fixed-site diagnostic imaging services and related information technology solutions (including artificial intelligence) in the United States based on the number of locations and annual imaging revenue. RadNet has a network of 399 owned and/or operated outpatient imaging centers. RadNet's markets include Arizona, California, Delaware, Florida, Maryland, New Jersey, New York and Texas. In addition, RadNet provides radiology information technology and artificial intelligence solutions marketed under the DeepHealth brand and other related products and services to customers in the diagnostic imaging industry. Together with affiliated radiologists, and inclusive of full-time and per diem employees and technologists, RadNet has a total of over 10,000 employees. For more information, visit http://www.radnet.com.
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