Recoverx provides Evidium, a computational medical intelligence platform that converts clinical guidelines, real‑world evidence, and patient data into a unified, computable substrate. Using first‑order system modeling and large language models, it predicts current health status, future disease trajectories, and cost outcomes, delivering evidence‑backed, actionable recommendations while keeping client data isolated.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare organizations generate vast amounts of clinical and claims data, but the information remains largely unstructured and non‑computational, making it difficult to derive precise, actionable insights about patient health trajectories and associated costs.
Solution
Recoverx offers Evidium, a computational medical intelligence platform that transforms clinical knowledge and real‑world observations into a shared, computable substrate. By modeling patient dynamics as first‑order systems and leveraging large language models, Evidium can infer current health status, predict future disease trajectories, and evaluate the impact of potential interventions. All insights are linked to underlying clinical evidence and real‑world events, providing transparent, actionable recommendations that support expert decision‑making without replacing clinicians. The platform continuously refines its models through real‑world validation while ensuring that customer data is never used to train shared foundation models, preserving privacy across organizations.
Target Audience
Primary customers are health systems, payer organizations, and life‑science companies that need predictive, evidence‑based insights to improve clinical outcomes, manage costs, and guide therapeutic development.
Features
- Computational substrate that integrates clinical guidelines, real‑world evidence, and patient‑level data for unified reasoning
- Foundation and condition‑specific models that simulate patient state, disease progression, and treatment effects
- Predictive analytics that generate future health and cost trajectories with quantified uncertainty
- Actionable, evidence‑backed recommendations presented in clinician‑friendly formats
- Strict data isolation ensuring client data is not used to train or expose shared models
- Patented AI architecture protected by multiple issued and pending patents
- API and integration tools for embedding insights into EHRs, payer systems, and research workflows