# An Interoperable UMLS Terminology Service Using FHIR
## 7 Discussion  
Applications employ semantic standards to annotate the data with semantics, which allows users (humans and machines) a uniform understanding of the data. There exists many semantic standards in the domain of biomedical and healthcare informatics that captured knowledge related to various health specializations. For example, ICD, a classification system, classifies diseases, a variety of signs, symptoms, abnormal findings, complaints, and external causes. SNOMED, a computer-processable collection of the medical terms (in both human and veterinary medicine), provides codes, terms, synonyms, and definitions for diseases, findings, procedures, substances, etc. LOINC provides codes for identifying laboratory and clinical observations, and Disease Ontology, which is a standard ontology that represents human diseases. Generally, there is an overlap of knowledge captured by these standards. The UMLS system has successfully encircled current and legacy semantics standards under one roof and integrated the collective knowledge to build a single semantic reference system. Integrating the standards into UMLS allows a unified format and the distribution of biomedical and healthcare knowledge. However, the users are required to understand UMLS's complex knowledge structure and its inner workings, and also need strong technical skills to interact with UMLS. Representing UMLS knowledge in an interoperable format would be easier to consume and understand for users--both humans and machines. Our research was motivated by the need to represent UMLS knowledge in an interoperable format using standards that are widely accepted and allow easy access to the knowledge without the need to understand its inner working. As such, the research developed a terminology server that allows easy access to UMLS knowledge structured using the FHIR standard. The UMLS FHIR terminology server, along with its API, acts as a semantic facade that hides the composite intricacies related to UMLS. As the UMLS knowledge is structured using the FHIR standard, a widely adopted standard in the industry, the users are immune to UMLS's native knowledge structure and changes. The users need to know the FHIR standard, resources, and its features to use the terminology server. For any user in healthcare, being popular with the ability to represent a variety of healthcare data in an interoperable format, and applicable across the healthcare domain, the users gain much more learning from the FHIR than the UMLS native knowledge structure. Further, any FHIR-compliant application can easily consume the UMLS knowledge structured as FHIR resources, as the application is already capable of handling FHIR's structure data. In succession, the application developers need not write  
Figure 11: Profiled ConceptMap representing relationships of Atrial Fibrillation (CUI C0004238). custom software modules to query UMLS and parse its knowledge to be used in the application. Even with the availability of many different healthcare standards (HL7 CDA, openEHR, etc. ), FHIR is _the_ interoperability standard, and has gained incredible support from researchers, healthcare organizations, governments, and healthcare application (EHR, PHR, etc.) vendors. This supports our choice of the standard and the goal-representing UMLS knowledge using FHIR. There exist different terminology servers and services, such as the VOSER vocabulary server [38], the UMLS Knowledge Source Server [32], UMLS API [6], GALEN terminology server [39] and OntoServer [28], to name a few. The UMLS API and OntoServer are REST APIs, while for the others, the user needs to install and configure the software on the server. As UMLS FHIR API is a REST-based service, the applications query the endpoints programmatically, as required, and use the knowledge. As the results are formatted using FHIR resources, users can use open-source FHIR libraries, such as HAPI, to get the knowledge from the structure.