New Collaboration Offers Promise of Automating Prior Authorizations in Radiology with AI
In addition to a variety of tools to promote radiology workflow efficiencies, the integration of the Gravity AI tools into the PowerServer RIS platform may reduce time-consuming prior authorizations to minutes for completion.
Offering improved functionality of existing radiology information systems (RIS), a new partnership between RamSoft and Alpha Nodus may significantly enhance radiology workflow and address the time-consuming nature of prior authorizations.
RamSoft said the integration of the Gravity AI (Alpha Nodus) tools into its RIS offerings will streamline radiology workflows through increased automation. Alpha Nodus maintained that artificial intelligence (AI)-powered automation will reduce the burden of prior authorizations in radiology.
“Prior authorizations once took hours or even days," said Shamit Patel, the CEO of Alpha Nodus. "With Gravity, now integrated into PowerServer, we've fully automated or reduced that to just minutes, improving efficiency and accelerating the patient journey."
The companies said key benefits of the integration will include:
• real-time verification of payer requirements to prevent delays
• AI-enabled patient scheduling to reduce no-shows
• up to a 25 percent reduction of claim denial rates through compliance checks
RamSoft and Alpha Nodus will be showcasing the updated software at the Radiology Business Management Association (RBMA) Paradigm 2025 conference through March 27.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room Podcast: Emerging Trends in the Radiology Workforce
February 11th 2022Richard Duszak, MD, and Mina Makary, MD, discuss a number of issues, ranging from demographic trends and NPRPs to physician burnout and medical student recruitment, that figure to impact the radiology workforce now and in the near future.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
