Clinical data is expressed in many hundreds of medical electronic record formats. To encourage interoperability, knowledge portability, and the development of a cross-platform health application ecosystem, many vendors, standard development organizations, and user groups concentrate on developing exchange formats like C-CDA and, more recently, FHIR. Yet, even with such standards, interoperability remains elusive. Some have proposed the use of well-defined clinical logical models and corresponding APIs to help address this challenge. Electronic medical record (EMR) formats are typically designed around highly regularized generic structures which are far from the mental models that clinicians have in their heads in their daily practice. Offering clinicians intuitive models allows them to participate in the production, approval, and long-term evaluation of clinical decision support rules. For instance, asking a clinician to write rules in terms of blood pressures containing both systolic and diastolic components will be generally much more productive than asking them to write rules about a FHIR resource with a resourceType of “Observation” and two components, one with a code of type CodeableConcept containing some Coding concept references for one of many deployed codes for “Systolic blood pressure”… To further complicate matters, resources may vary across versions of FHIR and may be modeled differently within a given version of FHIR. Thus, to support greater portability in health care, any given messaging model ought to be normalized into rules model, a form that can be generally consumed and produced by health applications thus reducing current degrees of freedom. ShexMap may support such normalization by converting local data models into a more representational form that can be readily consumed by knowledge applications. ShexMaps were used in a pilot to migrate patient data from the Department of Defence to the Veteran’s Administration. In another effort, in order to enable rules written over a natural object-oriented class hierarchy, clinical data had to migrate to that model. In a recent NIH grant 5-R01-EB030529-02, we annotated the FHIR ShEx schema for Observation with ShExMap Semantic Actions that were mapped to a rules schema. This enabled automatic instance data translation to many different clinical models used by the rules. This tutorial will introduce the problems involved in creating clinical models and translating local data into these forms, a step that is essential when developing and executing the CDS rules that run over them. Presenters will describe the landscape of existing models and provide hands-on training into the creation, debugging and execution of ShExMaps for translating from FHIR to sensible clinical models. Clinical decision support offers great returns in reducing clinician workloads or at least providing backup when overloaded clinicians fail to remember something or take it into account in daily care. We’re trying to help by creating intuitive tools for expressing and executing these rules.