Clinical data exchange with unambiguous meaning, semantic interoperability, is essential for learning health systems. Existing data standardization efforts, often uncoordinated, necessitate a single common reference point to ensure consistency and avoid duplication. HL7 FHIR?, for example, progresses towards improving and expediting system interoperability, but the explosion of FHIR Implementation Guides shows inadequate coordination produces standards falling short of true sematic interoperability. Efforts such as the Office of the National Coordinator United States Core Data for Interoperability (ONC USCDI) contribute health data classes and constituent data elements towards improved data interoperability, but foundational logical models such as Clinical Element Models (CEMs) are needed for semantically interoperable data representations in health IT systems. Carefully designed and defined CEMs reduce documentation burden by requiring well defined sets of data structures. Clinical Health IT and reporting requirement costs are reduced, and research is enhanced as clearly defined data elements become provably aligned between systems. Further, inconsistent representations reducing utility, and injection of unintended bias where data is combined across sources, is more easily controlled. This enables accurate industry wide access to data for clinicians at all levels while reducing documentation burden. Systematic, repeatable, scalable review of logical models by knowledgeable clinicians substantiates and reenforces clinical model credibility. Obtaining broad clinical review of logical models is challenging. New functionality added to the CEM browser at openCEM.org allows consistent, substantive, scalable reviews using repeatable processes. This workshop exposes clinicians to shareable/reusable logical models (CEMs), demonstrates review processes, and describes clinician involvement in model development and maturation.