Use case
Population Health Analytics & Quality Reporting
Hydra connect unifies clinical, claims, lab, and SDOH data into trusted population insights. Identify high-risk patients, automate quality reporting, and drive proactive care decisions with harmonized, analytics-ready data that powers value-based performance and better outcomes.
The Challenge
Data fragmentation usually drives healthcare bodies to the inability to aggregate and analyze patient data for population health insights and quality measures. When clinical, claims, lab, and SDOH data live in silos—each with different identifiers and vocabularies—there's no reliable longitudinal view of patients or populations.
Late, inconsistent and untrusted indicators have huge consequences on regulators and hospital managers alike. Without proper harmonized, analytics-ready data and consistent measure logic, teams can't confidently compute HEDIS/CMS metrics, surface care gaps in time, or direct resources where they'll have the greatest impact.
The Solution
TietAI generate trustworthy population statistics end-to-end: Multi-Source Data Aggregation consolidates clinical, claims, lab, and SDOH data into unified patient profiles and a consistent population table. The platform's AI-powered Risk Stratification Engine identifies high-risk patients and predicts potential health outcomes, enabling proactive care management.
Multi-Source Data Aggregation
Combine clinical, claims, lab, and social determinants of health (SDOH) data from multiple sources into unified patient profiles.
Risk Stratification Engine
AI-powered risk scoring to identify high-risk patients and predict potential health outcomes.
Automated Quality Reporting
Generate HEDIS, CMS Star Ratings, and other quality-measure reports with real-time data updates.
The Impact
A unified and reliable view in hands of health planners, turns a biases partial outlook into a full picture of their populations. Clinical and operational teams can track quality measures in real time, identify care gaps before they escalate, and target interventions where they matter most. The result: faster, more accurate reporting, stronger performance in value-based programs, and measurable improvements in population health outcomes.