Terminology management that unlocks health data integrity

Technical interoperability means data can move between systems. Semantic interoperability means that data means the same thing in every system it touches. Without it, a lab result coded one way in your EHR may be interpreted differently by your analytics platform, your payer system, or your AI model. The result is inconsistent reporting, unreliable insights, and downstream errors that are hard to trace. Rhapsody Semantic solves that by creating a single, normalized reference point for all clinical data across your environment. 

Rhapsody Semantic supports a broad range of clinical code sets and standards, including LOINC, SNOMED CT, USCD-I, ICD, and FHIR value sets. It provides a large library of standard content and mappings, with automated translation across sources, so your team isn’t building and maintaining mappings manually. 

AI models produce outputs that are only as reliable as the data they’re trained and run on. When clinical data isn’t normalized, AI models learn from inconsistent inputs and produce inconsistent, unreliable outputs. Rhapsody Semantic creates the clean, normalized data foundation that AI development requires. Zephyr AI used Rhapsody Semantic to go from data ingestion to production in under 30 days, creating more precise AI models at scale.

EHR-native and analytics-native terminology services are designed to serve that system’s internal needs. They don’t normalize data across your broader environment. Rhapsody Semantic is purpose-built to work across systems creating a shared clinical vocabulary that every application in your ecosystem can reference, regardless of vendor or standard. It also integrates directly with Rhapsody Integration and Corepoint, so normalization happens as part of your existing data flows. 

Rhapsody Semantic is built for any organization that needs to normalize clinical data across multiple systems. That includes health systems unifying data across EHRs and analytics platforms, health tech companies building AI models or clinical decision tools, HIEs and public health agencies aggregating data from many sources, and payers managing clinical data across networks.