First Fully Autonomous Generative AI Personalized Medical Authorizations System Reduces Care Delay

The medical authorizations market is plagued by inefficiencies, leading to delays in care, resource waste, overuse of services, and inconvenience for all parties involved. All that could change with the launch of the first patented autonomous clinically objective, Generative AI, case-by-case review system that is designed to improve the utilization and prior-authorization processes before medical procedures.

Serenus.AI’s (Tel Aviv, Israel) innovative system streamlines medical authorizations by automatically extracting anonymous clinical information from the patient's medical record and analyzing it using its AI engine, which is based on the latest research, guidelines, and best practices. This process enables the system to provide an immediate personalized authorization if the procedure is indicated. If the procedure is not indicated, the utilization manager receives a comprehensive, real-time report highlighting the related medical factors and alternative conservative pathways. Serenus.AI's solution is currently available for most common elective surgeries and oncology treatments.

“Today's prior authorizations are reported to create care delays which can lead to serious adverse events. Serenus.AI's end-to-end personalized medical authorizations system, overcomes this reality by automating the medical authorizations processes while providing personalized detailed pathways,” said Hillary Harel, Serenus.AI CEO. “Serenus.AI's solution advances care quality and effectiveness, boosts satisfaction across the board and reduces both medical and administrative costs. Our system, in alignment with the value-based care model, improves patient outcomes twofold: those who do not need the procedure are saved from unnecessary pain and complications while those waiting for a critical procedure are quickly approved. This transparency saves the providers and the payers variable time and administrative burden of the existing back-and-forth process prevalent in the review models today.”

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