AI-Driven Precision for the Future of Care

The problem

Prior authorization is one of healthcare's most frustrating bottlenecks. Staff spend hours gathering clinical documentation, interpreting payer-specific requirements, and chasing approvals, while patients wait days or weeks for care. The average prior auth takes 5-7 days and costs $31 per transaction in administrative overhead. Denials and delays lead to care abandonment and worse patient outcomes.

How we approach it

Automate the entire prior authorization workflow with AI that understands clinical documentation, payer policies, and approval criteria. Our system automatically extracts relevant clinical data from the EHR, matches it against payer requirements, identifies coverage gaps, and submits complete authorization requests, reducing turnaround from days to hours.

The problem

Physicians spend 2+ hours per day on documentation, often after clinic hours, leading to burnout and reduced time with patients. The cognitive load of simultaneously caring for patients while documenting reduces both care quality and note accuracy. EHR-driven documentation has become one of the top drivers of physician dissatisfaction and turnover.

How we approach it

Deploy ambient AI that listens to physician-patient conversations and automatically generates structured clinical notes in real-time. The system captures the full clinical encounter, extracts relevant information, and produces compliant documentation that physicians simply review and sign, dramatically reducing documentation burden while improving note completeness.

The problem

Hospital readmissions within 30 days affect 15-20% of Medicare patients, costing $26 billion annually, with $17 billion considered avoidable. Hospitals face CMS penalties for excess readmissions, yet traditional risk assessment happens too late or relies on incomplete data. Care managers cannot focus limited resources on patients who need the most support.

How we approach it

Predict readmission risk at admission and continuously update throughout the stay using machine learning that analyzes clinical data, social determinants, and care patterns. Enable proactive intervention with high-risk patients through targeted discharge planning, transitional care, and post-discharge monitoring, preventing readmissions before they happen.