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Lynqsys
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AI-Native, Cloud-First Data Refinery Engine. Born in the cloud and built for the AI era, Lynqsys transforms fragmented healthcare data into clean, real-time, AI-ready pipelines. Unlike legacy HIE platforms, it doesn’t just store data — it refines, enriches, and streams Intelligent Assets that fuel predictive analytics, portals, and AI copilots across the healthcare ecosystem.
Lynqsys™ is powered by patented recursive data governance technology (U.S. Patent Pending). This ensures every message entering your data ecosystem is validated, refined, and contextually governed before becoming part of your longitudinal record.
Overview
Lynqsys born in cloud, AI-native: The Heart of Intelligent Healthcare Data
Real-Time. AI-Designed. Purpose-Built. Lynqsys isn’t just another health data platform, it’s the intelligence engine behind Interstella’s revolution in healthcare interoperability. Built from the ground up to replace the batch-based, siloed systems of yesterday, Lynqsys transforms raw data into curated, AI-optimized Intelligent Assets in real time.
Whether you’re a state HIE, a health system CIO, a Medicaid plan executive, or a startup building with FHIR APIs, Lynqsys delivers the clarity, speed, and structure your mission demands. It’s not a dashboard. It’s not an ETL tool. It’s a Data Refinery as a Service (DRaaS) platform for the age of value-based care.
What Makes Lynqsys Different?
Streaming Ingestion in Seconds
Live data cleaned, normalized, deduplicated, and enriched as it flows. We turn fragmented records into Intelligent Assets with context-aware tagging and metadata structuring.
AI-Ready by Design
Our outputs don’t need to be “prepared” for AI, they are born for it. Lynqsys data feeds copilots, predictive analytics, genomic dashboards, and real-time alerts out-of-the-box.
Plug-and-Play API Ecosystem
Seamless integration into your existing systems: EHRs, HIEs, portals, apps, CRMs, or payer systems. You choose the format: FHIR, HL7v2, or custom events.
TEFCA-Aligned and Compliance-First
HIPAA, GINA, CMS quality measures, 21st Century Cures, baked into every node of the pipeline.
Why It Matters Now
Why This Is Possible Now—And Why It Matters
Twenty years ago, when most healthcare data platforms were built, real-time data refinement at scale wasn't practical. Cloud infrastructure was nascent. Processing 1M+ messages daily with comprehensive quality controls would have been prohibitively expensive.
Technology has matured: Cloud-native architectures, real-time processing, and sophisticated algorithms are now accessible and cost-effective.
But there's a more urgent reason this matters today: AI.
Healthcare is racing to deploy AI for clinical decision support, risk prediction, population health, and operational efficiency. But AI built on unreliable data doesn't just underperform—it fails catastrophically.
The challenge: AI demands validated, consistent, longitudinally stable data. Raw healthcare data—with its inconsistent semantics, identity issues, and missing context—causes AI models to produce unreliable results, eroding trust and delaying adoption.
The opportunity: Organizations that build their AI on refined, governed data foundations will win. Those that don't will struggle with failed pilots, compliance concerns, and competitive disadvantage.
The question isn't whether the technology is ready—it is. The question is whether your organization is ready to build the data foundation that AI demands.