Eos AI

Healthcare practices, MSOs/DSOs, and health systems run operational and revenue-cycle work such as eligibility checks, referrals, scheduling, billing, and follow-up across disconnected systems like EHRs, imaging archives, labs, scheduling, and billing platforms, each with its own identifiers and partial records. Clinical events are also recorded inconsistently across hospitals, scanners, and documentation styles, making historical cases hard to compare. Staff currently have to manually search charts, check coverage, chase missing documents like MRIs for referrals, and review schedules for insurance issues, pulling time away from patient care.

Eos connects to the systems a healthcare organization already uses, resolves patient identities across sites and encounters, and links records into a continuous longitudinal history without moving the underlying data. Two components, VERA and LUCIA, standardize imaging (aligning distributions across sites and scanners, said to improve model performance by up to 18% and cut deployment from months to days) and text (normalizing free text, ICD, SNOMED, labs, diagnoses, and procedures) into consistent patient trajectories. On top of this harmonized record, Eos hosts customer or Eos-built models and agents in house for tasks like eligibility, referrals, claims, and care coordination, and a predictive intelligence layer turns model output into finished actions such as attaching a missing MRI to a referral or flagging insurance issues, rather than just producing an alert. The product can run live alongside a staff member on their desktop for in-the-moment requests, or independently in the background on delegated tasks like reviewing tomorrow's new-patient schedule.