Knit provides an AI‑driven clinical intelligence platform that ingests real‑world health records and applies research‑grade predictive models to deliver actionable, evidence‑based recommendations at the point of care.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare providers often lack real-time, evidence‑based insights derived from large-scale patient data, leading to missed treatment opportunities, inefficient care pathways, and suboptimal patient outcomes.
Solution
Knit delivers an AI‑driven clinical intelligence platform that ingests real‑world health records and applies research‑grade predictive models to surface actionable recommendations at the point of care. By identifying treatment gaps and forecasting patient trajectories, the system enables clinicians to make more informed decisions without disrupting workflow. The platform continuously learns from new data, ensuring that insights remain up‑to‑date with the latest clinical evidence. Integrated visualizations present risk scores, suggested interventions, and outcome projections in a concise format that supports both individual patient management and population‑level quality improvement.
Target Audience
Primary users are hospitals, health systems, and physician groups seeking to enhance clinical decision support and improve patient outcomes through data‑driven insights.
Features
- Secure ingestion and normalization of heterogeneous electronic health record data for comprehensive patient profiles
- Deployment of advanced causal and predictive AI models that estimate treatment effectiveness and future health outcomes
- Real‑time risk stratification dashboards that highlight gaps in care and recommend evidence‑based interventions
- Seamless integration with existing clinical information systems via standard APIs and HL7/FHIR compatibility
- Continuous model retraining pipeline that incorporates new clinical data to maintain accuracy and relevance
- Audit‑ready reporting tools that track recommendation adoption and impact on key performance metrics