Limbix (by BigHealth)
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Predictive clinical and operational intelligence to fight death and waste in healthcare.
KenSci, now integrated into the Advata platform, represents a sophisticated AI-first approach to healthcare operations and clinical outcomes. Built natively on Microsoft Azure, the platform leverages machine learning to ingest disparate EHR, claims, and socio-economic data to provide real-time risk stratification. As of 2026, its technical architecture focuses on 'Explainable AI' (XAI), ensuring that clinical predictions—such as sepsis onset or readmission risks—are accompanied by transparent data drivers for physician trust. The market position for KenSci/Advata is centered on the 'Value-Based Care' model, where its predictive engines help hospital systems reduce the cost of care while improving patient outcomes. The platform operates as a managed intelligence layer that bridges the gap between raw data lakes and actionable bedside insights. It utilizes advanced FHIR-based data pipelines to ensure interoperability and low-latency inference, making it a critical infrastructure component for large-scale health systems looking to automate operational throughput and mitigate clinical risks before they manifest.
Provides the specific features and weights that led to a clinical risk score, accessible within the clinician's workflow.
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Predictive medical data and clinical insights for streamlined enterprise underwriting.
The professional medical network for clinicians, providing HIPAA-compliant AI and telehealth solutions.
Precision Medical Diagnostics and Predictive Clinical Decision Support.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A high-throughput pipeline that processes Fast Healthcare Interoperability Resources data in real-time.
Automated scanning of models to detect and mitigate demographic or socio-economic biases in risk predictions.
Uses longitudinal data to forecast patient census and nurse staffing requirements 24-48 hours in advance.
Calculates the probability of readmission within 30 days based on discharge summaries and social determinants.
Monitors vitals and lab results to predict sepsis up to 48 hours before clinical manifestation.
Predicts claim denials and identifies high-risk billing codes before submission.
Hospitals face financial penalties for high readmission rates which are often preventable with better post-discharge planning.
Registry Updated:2/7/2026
Monitor post-discharge follow-up compliance.
ED overcrowding leads to patient dissatisfaction and poor outcomes.
Sepsis is a leading cause of hospital death; every hour of delayed treatment increases mortality.