Concord is a semantic intelligence engine for healthcare. It translates meaning across EHRs, labs, imaging, pharmacy, and billing systems. It resolves patient identities without a master patient index. It catches data breaks before clinicians notice.
Talk to UsHealth systems spend millions connecting their tools. Integration engines route messages. APIs pass data. But when Epic calls a field "EncounterType" and Oracle Health calls it "VisitClass," the integration engine does not know they mean the same thing. A human figures that out. Manually. For every field. Across every system.
Then a vendor pushes an update. A field name changes. A code set shifts. The mapping breaks. Nobody knows until a dashboard shows wrong numbers or a clinician sees stale data.
That is not an integration problem. It is a comprehension problem. Your tools move data. Nothing understands it.
Hospital M&A activity hit $46 billion in 2025. Every deal creates the same problem: two (or more) EHR environments that store patient data differently. Epic and Oracle Health use different field names, coding systems, and data models. Manual mapping takes 9-12 months. Revenue cycle teams cannot produce unified reports until it is done.
Concord reads both schemas, builds a shared dictionary of meaning, and resolves patient identities across systems that share no common identifier. Unified reporting in weeks, not months.
CMS interoperability rules and the ONC Cures Act require FHIR R4 APIs. But most clinical data still flows through HL7 v2, a format designed in the 1980s. Translating between them is not a field-to-field mapping. It is a structural transformation. And every external lab, imaging center, and pharmacy sends HL7 v2 messages with slightly different field usage.
Concord translates by meaning, not position. It does not care that Lab A puts the ordering provider in OBR-16 and Lab B puts it in ORC-12. It understands both. When a source system changes its message format, drift detection flags it within hours.
Large health systems feed data from EHRs, labs, imaging, pharmacy, revenue cycle, and population health tools into a cloud data warehouse. Every source has its own schema. Every vendor update can break the pipeline. The data engineering team maintains a 2,000-row mapping spreadsheet. Three people know how to read it.
Concord sits between source and warehouse. It validates every data feed against a unified semantic model. When a source system changes a field, Concord catches it before bad data reaches a quality report, a readmission model, or a payer submission.
Point Concord at any clinical or operational data source. It ingests the schema and builds a conceptual model of what each field means. No manual mapping required.
Two patent-pending technologies power the engine. Semantic Translation maps meaning across systems, not just field names. Entity Resolution identifies the same patient, device, or event across systems that share no common identifier.
Drift Detection monitors every connected source. When a vendor update changes a field name, data type, or code set, Concord flags the change before downstream systems break. Your team reviews and approves the update in minutes instead of discovering it weeks later.
Concord does not replace Rhapsody, Corepoint, Redox, or your integration engine. It makes them smarter. Your existing infrastructure stays in place. Concord adds the intelligence layer on top.
Semantic Translation and Entity Resolution are patent-pending technologies. They eliminate the manual mapping spreadsheets that break every time a vendor pushes an update.
On-premises for PHI-sensitive environments. Hybrid for mixed workloads. Cloud for speed. You choose.
Concord was built by a team that spent decades inside regulated industries, managing security tools, compliance systems, and data pipelines that refused to talk to each other. The engine is proven in production with live POC validation. Two patent-pending technologies protect the core. Banking was the first vertical. Healthcare is next.
We are working with health systems that are tired of broken data feeds, stale patient records, and 12-month integration timelines. If that sounds like your organization, let's talk.
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