Tennr, a healthcare technology company focused on streamlining healthcare operations, recently announced the receipt of $101 million in funding. While the investment signals growing confidence in AI-enabled administrative tools, Tennr’s real innovation lies in the technology it has developed to address one of healthcare’s most persistent inefficiencies: the referral process.
The company’s proprietary AI model, called RaeLM, is designed to automate the intake and routing of referrals, which are often delayed or mishandled due to fragmented workflows, manual processes, and unstructured documentation. Unlike generic large language models, RaeLM was specifically designed for healthcare administration. It has been trained on more than 100 million deidentified healthcare documents and over 8,000 payer-specific documentation requirements. This allows it to extract relevant information from scanned documents, handwritten forms, and PDFs, formats that typically require significant human input to review.
By using RaeLM, providers can automate critical front-end administrative tasks such as referral intake and documentation validation. The platform flags missing information before submission, routes referrals to the appropriate destination, and is designed to work within the tools providers already use, reducing the need for duplicate workflows or manual data entry.
Tennr has launched a centralized platform that provides real-time referral tracking for both referring and receiving providers, as well as for patients. This tool aims to enhance care coordination, improve patient adherence, and increase administrative visibility, while reducing the need for follow-up phone calls and status checks. Tennr has also expanded its product suite to support benefits verification, prior authorization review, and communication coordination, signaling a broader move toward becoming a full-service AI-powered workflow platform.
Tennr’s recent funding round underscores a larger trend: the growing role of healthcare-specific AI tools designed to solve operational inefficiencies. As platforms like RaeLM continue to evolve, healthcare organizations will need to evaluate not only the benefits of automation, but also the regulatory and contractual considerations that come with relying on artificial intelligence in clinical and administrative workflows.
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