When Trey Holterman, Diego Baugh, and Tyler Johnson met as freshmen at Stanford University, they shared a vision—one rooted in machine learning and the drive to solve real-world problems. After years of collaboration and exploration in the field of AI, the trio launched Tennr in 2021, setting up headquarters in the heart of New York City. Their mission was simple yet ambitious: to alleviate the mounting administrative burden in healthcare, focusing on streamlining document processing and workflow optimization for providers grappling with outdated systems.
In the evolving healthcare landscape, Tennr saw an opportunity to tackle one of the industry's biggest pain points—an overreliance on manual processes for patient referrals, insurance claims, and document handling. By leveraging machine learning, the Tennr team set out to develop AI-powered tools to reduce wait times, eliminate administrative errors, and create efficiencies that would ripple through the healthcare system.
Technology to Automate Healthcare Workflows
Tennr’s platform is grounded in a specialized approach to machine learning, crafted specifically for healthcare’s intricate requirements. Unlike general-purpose AI tools, Tennr’s platform handles multiple healthcare-specific tasks:
- Document Processing: The platform reads, parses, and processes complex medical documents, enabling faster turnaround times for patients.
- Automated Patient Intake: Tennr automates patient referral intake, ensuring crucial details are captured and processed efficiently.
- Clinical Audits and Reviews: It assists in conducting detailed audits on clinical documents, a traditionally time-intensive task.
- Prior Authorization Requests: The platform manages prior authorizations, navigating complex insurance requirements to speed up processing.
- Eligibility & Benefit Checks: Tennr verifies insurance eligibility, helping providers avoid errors and streamline approvals.
- Medical Record Management: The platform organizes patient records seamlessly, reducing administrative strain.
- Claims Processing: Tennr’s technology expedites claims submissions, cutting down delays and rejections.
At the core of Tennr’s system is an advanced large language model, RaeLLM™ 7B, which uses deep learning to tackle the complexities of unstructured data in healthcare. Its “multi-patient model” efficiently parses through documents, including those with multiple patient data points, while the unique "checkbox reading" capability allows accurate processing of forms—tasks that traditional AI solutions often struggle with. With this specialized technology, Tennr’s AI reads incoming faxes, extracts relevant information, and routes it appropriately, reducing both error rates and processing times.
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