GenHealth.ai utilizes a Large Medical Model (LMM) trained on medical codes and events from over 100 million patients to automate prior authorization and enhance healthcare analytics. This technology improves accuracy in adjudicating prior authorization cases by 94% and triples efficiency for utilization management teams, significantly reducing administrative burdens in healthcare operations.
Funding
Funding not disclosed
PAFounders
Product
Problem
The prior authorization process in healthcare is often slow, manual, and costly, leading to delays in patient care and increased administrative burdens for providers and payers. Traditional healthcare analytics methods also struggle to accurately predict costs and provide actionable insights from complex patient data.
Solution
GenHealth.ai offers a suite of generative AI-powered solutions designed to automate utilization management and enhance healthcare analytics. At the core of these solutions is a Large Medical Model (LMM) trained on medical codes and events from over 100 million patients. This LMM enables the automation of prior authorizations with high accuracy, significantly reducing administrative overhead for utilization management teams. Additionally, GenHealth.ai provides a healthcare analytics chatbot that allows users to interact with patient data to generate insights, predict healthcare costs, and understand medical event likelihood. The platform natively integrates with healthcare standards like FHIR, HL7, and X12, as well as unstructured documents like PDFs, ensuring seamless data integration and interoperability.
Target Audience
GenHealth.ai primarily targets health insurance plans (payers), providers and health systems, and DME/HME suppliers seeking to streamline operations, reduce costs, and improve patient outcomes through AI-driven automation and analytics.
Features
- AI Auto Auth: Automates prior authorization processes, improving accuracy in adjudicating cases.
- G-Mode: A healthcare analytics chatbot that enables users to query patient data and generate actionable insights.
- Large Medical Model (LMM): A proprietary foundation model trained on a vocabulary of medical codes and events.
- Cost Prediction: Predicts healthcare costs with greater accuracy compared to traditional methods.
- Integration: Natively integrates with healthcare standards like FHIR, HL7, and X12, as well as unstructured documents like PDFs.
- Intake Automation: Automates intake operations, including PDF routing, data extraction, and medical necessity determination.