Resources
To learn more about the technical components of IKE, please refer to the resources below:
Learning Series
The Learning Series is a general introduction to many of the concepts, ideologies, and components of Tinkar, ANF, and Komet.
Documentation
A Blueprint for Trustworthy Data: How a Collaborative Knowledge Foundation Can Elevate Everyday Data for More Accurate, Actionable, and Trusted Healthcare Insights is a textbook-style resource with a large collection of select information and documentation, developed throughout our work on IKM and Komet, that builds upon content described in the learning series but is not overly technical so that it may appeal to a wide audience.
The Volumes segment out information and documentation from A Blueprint for Trustworthy Data covering 12 distinct areas of knowledge and provide in-depth information on the following subjects.
Komet User Guide
The Komet User Guide provides instructions for first-time Komet start-up and directions, with screen shots, for how to perform common functions within Komet.
Developer Onboarding Guide
The Developer Onboarding Guide provides Java developers information and instructions to gain the necessary permission and access to the various repositories, tools, and resources that are required to submit code, bug fixes, and other GitHub actions.
IKE Starter Data
The IKE Starter Data is a foundational collection of core concepts, patterns, and semantics that provides a consistent baseline across all data sets and lays the groundwork for further development within Komet.
For instruction on how to use IKE Starter Data, please refer to the Komet user guide.
IKE Glossary
The IKE Glossary presents an overview of frequently used terms and their definitions, making it easy to reference and understand key ideas.
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Integrated Knowledge Exchange (IKE) is an open, collaborative hub that provides tools for managing healthcare knowledge in a consistent, standards-aligned way. By turning disparate information into reusable, trusted assets, IKE promotes data quality, interoperability, and knowledge sharing across the healthcare ecosystem.
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Integrated Knowledge Management (IKM) is the fusion of healthcare terminology knowledge resources under a unified representation framework. It brings together version control, standardized viewing, collaborative editing, and extension development within a collaborative environment, emphasizing building on existing work rather than starting from scratch.
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Komet is the authoring and transformation environment within IKE, designed to let experts create, refine, and share healthcare knowledge in a way that is accurate, reusable, standards-aligned, and builds upon existing systems. It integrates existing sources like SNOMED CT®, LOINC®, and RxNorm, and GUDID with new inputs, giving users the ability to import, structure, extend, and export content in formats that fit their systems. To learn more about Komet, please reference the Tools section.
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Terminology Knowledge Architecture (Tinkar) is a logical model designed to integrate local data management systems and EHRs with medical and laboratory terminologies such as SNOMED CT®, LOINC®, and RxNorm, enabling effective communication of clinical information across multiple systems. It supports standard terminology modules, value sets, coding systems, local terms, and equivalence mappings, and is intended to work alongside HL7® efforts and exchange standards like FHIR®. In addition to harmonizing the representation of terminologies, Tinkar captures historical changes to ensure high-quality data transfer.
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ANF is an HL7® balloted model for clinical statement representation, designed to safely and reliably support data analysis and aggregate data from any standard or non-standard input or exchange mechanism. The ANF Reference Model, part of the Clinical Information Model Initiative (CIMI) library of logical models, describes a standard normal form for clinical statements. ANF’s design principles help standardize statement representation, making it simple, consistent, reusable, and less variable.
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Our Knowledge Architecture is a framework for representing clinical information with Separation of Concerns (SoC) principles, organizing clinical information into distinct layers so that each higher layer relies on artifacts from the lower layer. Each architectural layer can be reused, developed, and updated independently. Our Knowledge Architecture allows issues within each layer to be resolved individually, rather than requiring changes to the whole system, resulting in more agile improvements.
