
Truveta
Saving lives with data by providing regulatory-grade safety and effectiveness data.

Predictive AI and machine learning for precision healthcare risk management and underwriting.

Lumiata, now integrated into the AKASA ecosystem (specifically within the Retrace and Unified Revenue Orchestration platforms), is a specialized AI engine designed for the healthcare industry. Its technical architecture centers around a proprietary Clinical Knowledge Graph and deep learning models trained on billions of healthcare data points, including claims, clinical labs, and pharmacy data. In the 2026 market landscape, Lumiata positions itself as the backbone for healthcare payers and providers to move from reactive to proactive financial management. By utilizing multi-layered neural networks, it transforms fragmented healthcare data into high-fidelity risk scores and cost predictions. The platform's 'Health AI Lab' allows actuarial and clinical teams to build, test, and deploy custom models without the need for extensive data science infrastructure. Its primary value proposition lies in its ability to predict disease progression and associated costs with higher precision than traditional actuarial methods, directly impacting the accuracy of underwriting, stop-loss insurance, and value-based care contracts.
Lumiata, now integrated into the AKASA ecosystem (specifically within the Retrace and Unified Revenue Orchestration platforms), is a specialized AI engine designed for the healthcare industry.
Explore all tools that specialize in risk score calculation. This domain focus ensures Lumiata (acquired by AKASA) delivers optimized results for this specific requirement.
A multi-dimensional scoring engine that correlates clinical history with future cost projections using deep neural networks.
A low-code environment for health actuaries to build custom risk models using pre-cleansed healthcare features.
A relational database of over 150 million patient journeys used to contextualize individual claims.
An AI-driven decision engine for group and individual health underwriting.
Models specifically designed to predict catastrophic high-cost claimants for re-insurers.
Identification of patients likely to miss quality metrics using predictive profiling.
Analyzes engagement patterns and satisfaction signals to predict disenrollment.
Data Infrastructure Assessment - Evaluation of existing FHIR/EDI data pipelines.
API Gateway Configuration - Provisioning of secure OAuth2 credentials.
Data Ingestion - Upload of historical claims and clinical data for baseline modeling.
Mapping to Lumiata Clinical Schema - Normalizing proprietary data to the Clinical Knowledge Graph.
Model Calibration - Fine-tuning predictive models against specific plan demographics.
Security Audit - Review of HIPAA and SOC2 compliance protocols.
Pilot Testing - Running parallel risk assessments against legacy systems.
User Training - Onboarding actuarial and clinical teams to the Lumiata Dashboard.
Workflow Integration - Connecting Lumiata outputs to core underwriting or care management software.
Production Go-Live - Scaling API calls for real-time risk assessment.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its high predictive accuracy compared to legacy actuarial models, though some note the high level of technical data preparation required."
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