Principal Research Scientist
Massachusetts Institute of Technology Clinical Research Director, Laboratory of Computational Physiology
Leading the charge in prior authorization innovation
Revolutionizing healthcare with our advanced prior authorization platform.
Industry Standard efficient and precise AI model
Our proprietary authorization engine reduces costs associated with PA and suggests pre-approved and personalized care pathways that plans may choose to offer members, ensuring both medical appropriateness and cost-effectiveness. This approach not only optimizes healthcare spending but also ensures that members receive timely care.
Basys encodes plan policies and EHRs, distilling hundreds of pages of documentation into simple checklists of criteria for coverage eligibility. The algorithm eliminates unnecessary PA requests, collects missing information from providers, and highlights important criteria so that health plan admin save time reviewing requests.
Trained with data from over 10 million patient medical and claims records from Mayo Clinic and other partners, and drug insights from Eli Lilly & Co., our engine resolves prior auth with 98% accuracy. Basys developed a self-learning mechanism that continually reviews decision accuracy, improving quality of care and decision efficiency.
Our engine resolves prior auth queries in seconds, reducing average decision time by 8 days. Basys also assists health plans in providing members and providers with clear and evidence-based explanations for PA decisions by automatically outlining health plan criteria, EHRs, and relevant literature to objectively support determinations.
AI Co-pilot for Healthcare:
Assisting with Prior Authorization and Utilization Management
Reduced administrative burden
Frictionless provider relationships
Better cost with clinically appropriate guidelines
Reduced operational burden
Automated prior auth
Bringing more transparency to the health plan policies