Manage all your terminology concepts and mappings
Terminologies and ontologies are essential for encoding and classifying clinical and life sciences data, and to enable semantic data interoperability. Unfortunately, existing terminology vendors only offer solutions for a limited number of domains (e.g. problems, medications, procedures, laboratory tests). The lack of a comprehensive and integrated terminology solution creates important shortcomings for EHR users and those involved with data-centric processes, including researchers and data scientists. Semedy’s Terminology solution is designed to support large-scale data engineering efforts where terminologies, ontologies, and code systems from multiple sources are integrated and managed. This solution allows an organization to:
Use extensible models to represent terminology assets (e.g. concepts, relationships, mappings, terms and synonyms in multiple languages) and their associated metadata
Create and manage different types of terminology content, including local dictionaries, reference vocabularies, taxonomies, ontologies, and code systems
Develop configurable methods to group, classify, and cross-reference (map) terminology assets
Represent and interconnect commonly used reference terminologies (e.g. SNOMED CT, LOINC, ICD-10-CM, NCI Thesaurus, RxNorm, UMLS, CVX, MVX, UCUM, etc.), and obtain periodic updates where items added or modified can be easily identified and analyzed
Integrate terminologies with other asset types (e.g. orders, problems, procedures, value sets, information models, etc.)
Proactively manage dependencies on terminology assets to prevent malfunctions and poor data quality
Semedy’s Terminology solution, implemented using our Clinical Knowledge Management System (CKMS), includes preconfigured models to represent reference terminologies and different types of terminology assets, ETL pipelines for import and/or export, configurable views, queries, and reports. Local and reference terminologies can be easily represented and periodically updated using our ETL framework. Commonly used reference sources are maintained and distributed by Semedy, along with guidance on how to integrate with local CDS rules, eMeasures, patient cohorts, and value sets. Terminology mappings and value sets can also be represented, extended, and systematically maintained within CKMS.
Using our CKMS platform, the demonstration will showcase how terminologies can be represented, searched, visualized, and cross-referenced using examples created by Semedy and from commonly used reference sources. We will demonstrate how to search and compare similar LOINC concepts, confirm if a concept is a member of existing value sets, and verify if a deprecated concept has been replaced by a newer one. Semantic facets will be used to find LOINC concepts with a specific component, method, and sample type, also illustrating how LOINC parts, answers, and lists are represented. Using UMLS concepts, we will illustrate how mappings and terms in multiple languages can be represented and used. A simple model to represent diagnoses will be used to demonstrate how new models can be created, along with examples of more complex taxonomies to represent therapeutic plans and clinical disorders with detailed cross-references to reference terminologies. We will also illustrate the use of configurable queries to classify concepts and to identify obsolete concepts that might be referenced by other assets.