# An Interoperable UMLS Terminology Service Using FHIR
## 2 Literature Review  
Structural and Semantic standards have played a crucial role in digitalizing healthcare data. A structural standard such as HL7 FHIR provides a structure to represent healthcare data, while a semantic standard such as LOINC complements the data with semantics added to it. Separately, UMLS, a semantic terminology, and FHIR, an interoperability standard, are well-known and widely used for various healthcare research and industrial applications. For example, UMLS is extensively used in clinical NLP [14; 15; 16; 17; 18], where UMLS is used as a reference terminology to identify medical terms in clinical notes. This improves UMLS, where UMLS is again used as a reference terminology to identify new medical terms or relationship from medical corpora (e.g., journals) using existing medical terms and then adding the new knowledge to be included in UMLS. As new knowledge is frequently added to UMLS [19; 20; 21; 22], there are multiple audits and quality checks to ensure the quality of UMLS knowledge [23]. Clinical decision systems [15; 24; 25] have used UMLS in the past to extract similar medical cases for clinical references, etc. FHIR is primarily used to achieve data interoperability [7] across diverse healthcare systems, design customized Electronic Health Records (EHRs) and Personal Health Records (PHRs) [26] to meet physician and patient requirements, enable the easy access to and adoption of medical vocabulary [27; 28; 29], create computable case forms to identify patients suitable to cancer trails [30], capture genomic data [31], etc. A detailed discussion of the potential uses of UMLS or FHIR, separately, is beyond the scope of this research article.  
Currently, there are a few terminology services and servers that allow access to semantic standards to satisfy diverse healthcare needs, and only one service that uses FHIR to serve the semantic content. The NLM UMLS API, a REST-based service, serves UMLS knowledge to a registered user via multiple endpoints. As discussed (Figure 1), the barrier is that the knowledge structure is directly related to the UMLS native knowledge structure, a non-interoperable format. Before UMLS API, NLM developed the UMLS Knowledge Source Server [32]. The authors used the innovative technology available at that time to service UMLS knowledge via the web. The server was implemented as a collection of Java servlets. The servlets queried UMLS using the RMI API. The query results, in XML format, are returned to the servlets that then apply the XSLT stylesheets to transform those results into HTML for display purposes. The server put UMLS on the web. [PERSON] et al. have developed OntoServer [28]. The Ontoserver is a terminology server that uses FHIR terminology resources to expresses the semantic knowledge encapsulated by SNOMED CT terminology, LOINC codes, and select OWL ontologies, such as the Human Phenotype Ontology (HPO). Similarly, the LONIC FHIR Terminology Server [29] serves LONIC terminology codes using FHIR terminology resources. This research is developing a terminology service using FHIR to express UMLS knowledge, which encompasses SNOMED CT and LONIC along with 209 other internationally known terminologies. [PERSON] et al. proposed to develop a vocabulary mapping service that maps Observational Health Data Sciences and Informatics (OHDISI) vocabularies to FHIR resources [21]. However, the author did not take the proposal beyond the conceptual idea. Currently, there are no efforts, based on the authors' knowledge, that leverage the FHIR standard and its terminology resources for representing UMLS knowledge.