Terminology Server Research Articles

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

Building a Natural Language Interface for FHIR Clinical Terminology Server.

While Fast Healthcare Interoperability Resources (FHIR) clinical terminology server enables quick and easy search and retrieval of coded medical data, it still has some drawbacks. When searching, any typographical errors, variations in word forms, or deviations in word sequence might lead to incorrect search outcomes. For retrieval, queries to the server must strictly follow the FHIR application programming interface format, which requires users to know the syntax and remember the attribute codes they wish to retrieve. To improve its functionalities, a natural language interface was built, that harnesses the capabilities of two preeminent large language models, along with other cutting-edge technologies such as speech-to-text conversion, vector semantic searching, and conversational artificial intelligence. Preliminary evaluation shows promising results in building a natural language interface for the FHIR clinical terminology system.

TermX: A Game Changer in the Healthcare Interoperability.

The necessity for robust, enduring, and relevant healthcare interoperability is universal across all clinical domains. However, we identified a gap in the availability of open-source, no-cost, high-quality tools that offer multilingual support and an advanced graphical interface. To address this, we developed TermX, an open-source platform to harmonise terminology and support interoperability between healthcare institutions and systems. TermX incorporates a terminology server, a Wiki, a model designer, a transformation editor, and tools for authoring and publishing. TermX is designed to develop terminology and implementation guides for healthcare systems at both the national and regional levels. It aims to ensure open, standardised access to published data and guarantee semantic interoperability based on the FHIR standard.

The Brazilian international patient summary initiative

ABSTRACT This paper describes the development of the Brazilian version of the International Patient Summary (IPS). A joint technical team is developing the project from Syrian-Lebanese Hospital, São Paulo, and the Coordination of Innovation and Informatics in Health, Secretariat of Information and Digital Health, Brazilian Ministry of Health. The five-phased approach used in the development of Brazil IPS starts with the identification of the Brazilian Ministry of Health national terminologies used to represent concepts for problems, diagnosis and procedures, their upload in a terminology server and mapping to IPS ValueSets, the Fast Healthcare Interoperability Resources profiles customization and, finally, the complete Brazil IPS Implementation Guide is described.

ELGA Terminology Server for Clinical Decision Support: A Case-Study Using an Existing Knowledge Base, CDS Hooks and FHIR.

There are many medical knowledge bases with potential for supporting medical professionals in their decision-making during routine care, yet usage of these sources remains low. Standardized linking of Clinical Decision Support (CDS) applications and existing medical knowledge bases is not a common practice. Using existing eHealth standards to increase the utilization of knowledge bases and implement a prototype. Linking an existing online knowledge base via a FHIR CodeSystem supplement to the Austrian national EHR (ELGA) terminology server and accessing these data using CDS Hooks and FHIR. We tested the approach by incorporating photosensitivity data of medications into a local copy of the Austrian terminology server. These data are directly used by a CDS Hooks compliant CDS service. The Austrian Terminology Server could be an important interface to access existing knowledge bases from within EHR systems. FHIR and CDS Hooks could lead the way for a simple and open integration of CDS services into EHR systems.

Integrating a new knowledge organisation system for monoclonal antibodies for therapeutic use authorised in Europe into HeTOP terminology-ontology server

Monoclonal antibodies (MAs) are increasingly used in the therapeutic arsenal. Clinical Data Warehouses (CDWs) offer unprecedented opportunities for research on real-word data. The objective of this work is to develop a knowledge organization system on MAs for therapeutic use (MATUs) applicable in Europe to query CDWs from a multi-terminology server (HeTOP). After expert consensus, three main health thesauri were selected: the MeSH thesaurus, the National Cancer Institute thesaurus (NCIt) and the SNOMED CT. These thesauri contain 1,723 MAs concepts, but only 99 (5.7 %) are identified as MATUs.The knowledge organisation system proposed in this article is a six-level hierarchical system according to their main therapeutic target. It includes 193 different concepts organised in a cross lingual terminology server, which will allow the inclusion of semantic extensions. Ninety nine (51.3 %) MATUs concepts and 94 (48.7 %) hierarchical concepts composed the knowledge organisation system. Two separates groups (an expert group and a validation group) carried out the selection, creation and validation processes.Queries identify, for unstructured data, 83 out of 99 (83.8 %) MATUs corresponding to 45,262 patients, 347,035 hospital stays and 427,544 health documents, and for structured data, 61 out of 99 (61.6 %) MATUs corresponding to 9,218 patients, 59,643 hospital stays and 104,737 hospital prescriptions. The volume of data in the CDW demonstrated the potential for using these data in clinical research, although not all MATUs are present in the CDW (16 missing for unstructured data and 38 for structured data).The knowledge organisation system proposed here improves the understanding of MATUs, the quality of queries and helps clinical researchers retrieve relevant medical information. The use of this model in CDW allows for the rapid identification of a large number of patients and health documents, either directly by a MATU of interest (e.g. Rituximab) but also by searching for parent concepts (e.g. Anti-CD20 Monoclonal Antibody).

Implementation of clinical alerts using a Terminology Server with SNOMED CT in Graph Databases

This paper described the application of a terminology server (TS) within a health institution in Uruguay, whose architecture is based on SNOMED CT and graph databases (NoSQL). The aim of this project is to apply the use of terminological service from a clinical, statistical, managerial, decision support and research perspective, among others, without compromising performance. Particularly, this article exposes the use of the TS as support for alert systems associated to diabetes mellitus patients.

TermiCron - Bridging the Gap Between FHIR Terminology Servers and Metadata Repositories.

The large variability of data models, specifications, and interpretations of data elements is particular to the healthcare domain. Achieving semantic interoperability is the first step to enable reuse of healthcare data. To ensure interoperability, metadata repositories (MDR) are increasingly used to manage data elements on a structural level, while terminology servers (TS) manage the ontologies, terminologies, coding systems and value sets on a semantic level. In practice, however, this strict separation is not always followed; instead, semantical information is stored and maintained directly in the MDR, as a link between both systems is missing. This may be reasonable up to a certain level of complexity, but it quickly reaches its limitations with increasing complexity. The goal of this approach is to combine both components in a compatible manner. We present TermiCron, a synchronization engine that provides synchronized value sets from TS in MDRs, including versioning and annotations. Prototypical results were shown for the terminology server Ontoserver and two established MDR systems. Bridging the semantic and structural gap between the two infrastructure components, this approach enables shared use of metadata and reuse of corresponding health information by establishing a clear separation of the two systems and thus serves to strengthen reuse as well as to increase quality.

Qualitative Evaluation of a Drug Terminology Server.

Although the drug is finished, identifiable, there is no universally accepted standard for naming them. The objective of this work is to evaluate qualitatively the HeTOP drug terminology server by two categories of students: (a) pharmacy students and (b) a control group. A formal evaluation was built to measure the perception of users about the HeTOP drug server, using the three mains questions about "teaching interest", "skill interest" (or competence) and "ergonomics". The three pharmacy student subgroups gave the best and the worst score to the same categories. All three criteria are rated above 6.5 out of 10. The HeTOP drug terminology server is freely available to "non drug" specialists (URL: www.hetop.eu/hetop/drugs/).

Mapping of ICD-O Tuples to OncoTree Codes Using SNOMED CT Post-Coordination.

Around 500,000 oncological diseases are diagnosed in Germany every year which are documented using the International Classification of Diseases for Oncology (ICD-O). Apart from this, another classification for oncology, OncoTree, is often used for the integration of new research findings in oncology. For this purpose, a semi-automatic mapping of ICD-O tuples to OncoTree codes was developed. The implementation uses a FHIR terminology server, pre-coordinated or post-coordinated SNOMED CT expressions, and subsumption testing. Various validations have been applied. The results were compared with reference data of scientific papers and manually evaluated by a senior pathologist, confirming the applicability of SNOMED CT in general and its post-coordinated expressions in particular as a viable intermediate mapping step. Resulting in an agreement of 84,00 % between the newly developed approach and the manual mapping, it becomes obvious that the present approach has the potential to be used in everyday medical practice.

Semi-Automated Approach to Map Clinical Concepts to SNOMED CT Terms by Using Terminology Server.

SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. Snowstorm is a terminology server developed by SNOMED International. In this work, the feasibility of using Snowstorm to automate the data retrieval and mapping has been discussed.

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for Federated Feasibility Queries in the Context of COVID-19: Feasibility Study

BackgroundThe COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries.ObjectiveThis study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries.MethodsWe used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a UI. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process.ResultsIn the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in 6 cases due to different versions used to generate the test data and the UI profiles, the support for specific code systems, and the evaluation of postcoordinated Systematized Nomenclature of Medicine (SNOMED) codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions.ConclusionsWe developed an automatic process to generate ontology and mapping files for FHIR-formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles and a terminology server, making it extendable to other FHIR profiles and demonstrating that automatic ontology generation on FHIR profiles is feasible.

Providing ART-DECOR ValueSets via FHIR Terminology Servers - A Technical Report.

To ensure semantic interoperability within healthcare systems, using common, curated terminological systems to identify relevant concepts is of fundamental importance. The HL7 FHIR standard specifies means of modelling terminological systems and appropriate ways of accessing and querying these artefacts within a terminology server. Hence, initiatives towards healthcare interoperability like IHE specify not only software interfaces, but also common codes in the form of value sets and code systems. The way in which these coding tables are provided is not necessarily compatible to the current version of the HL7 FHIR specification and therefore cannot be used with current HL7 FHIR-based terminology servers. This work demonstrates a conversion of terminological resources specified by the Integrating the Healthcare Initiative in the ART-DECOR platform, partly available in HL7 FHIR, to ensure that they can be used within a HL7 FHIR-based terminological server. The approach itself can be used for other terminological resources specified within ART-DECOR but can also be used as the basis for other code-driven conversions of proprietary coding schemes.

Medical Terminology Server for the Hospital of Clinics of Paraguay using Apache Lucene and the UMLS Metathesaurus

The current process of medical terminology coding in health standards at the Hospital of Clinics of Paraguay is performed by physicians, normally interns or residents. They search the medical terminology code in impressed coding manuals or in internet by using their cell phones. This search process takes a lot time to be done during the medical consultation. This work proposes and evaluates a friendly medical terminology server thought web services, using Apache Lucene, as a search engine library, and the Metathesaurus of Unified System of Medical Language (UMLS), as an information source. The server is developed for Spanish speakers. Results show that physicians can find medical terminology code with the terminology server, using friendly or familiar terms, 18 times faster than with the current search process. The user satisfaction degree is ``Good” according to an adjective rating of the System Usability Scale (SUS). In addition, a comparison with a search engine of medical terminology called Metamorphosys shows that the implemented terminology server is quite competitive and it responses in similar average time

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters.

ObjectivesThe secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine.MethodsWe tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction “proton-pump inhibitor [PPI] use and osteoporosis”. Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard.ResultsPrecision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%.ConclusionWe could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period.

ABCD 3.0 Ready to Use

The TDWG standard ABCD (Access to Biological Collections Data task group 2007) was aimed at harmonizing terminologies used for modelling biological collection information and is used as a comprehensive data format for transferring collection and observation data between software components. The project ABCD 3.0 (A community platform for the development and documentation of the ABCD standard for natural history collections) was financed by the German Research Council (DFG). It addressed the transformation of ABCD into a semantic web-compliant ontology by deconstructing the XML-schema into individually addressable RDF (Resource Description Framework) resources published via the TDWG Terms Wiki (https://terms.tdwg.org/wiki/ABCD_2). In a second step, informal properties and concept-relations described by the original ABCD-schema were transformed into a machine-readable ontology and revised (Güntsch et al. 2016). The project was successfully finished in January 2019. The ABCD 3 setup allows for the creation of standard-conforming application schemas. The XML variant of ABCD 3.0 was restructured, simplified and made more consistent in terms of element names and types as compared to version 2.x. The XML elements are connected to their semantic concepts using the W3C SAWSDL (Semantic Annotation for Web Services Description Language and XML Schema) standard. The creation of specialized applications schemas is encouraged, the first use case was the application schema for zoology. It will also be possible to generate application schemas that break the traditional unit-centric structure of ABCD. Further achievements of the project include creating a Wikibase instance as the editing platform, with related tools for maintenance queries, such as checking for inconsistencies in the ontology and automated export into RDF. This allows for fast iterations of new or updated versions, e.g. when additional mappings to other standards are done. The setup is agnostic to the data standard created, it can therefore also be used to create or model other standards. Mappings to other standards like Darwin Core (https://dwc.tdwg.org/) and Audubon Core (https://tdwg.github.io/ac/) are now machine readable as well. All XPaths (XML Paths) of ABCD 3.0 XML have been mapped to all variants of ABCD 2.06 and 2.1, which will ease transition to the new standard. The ABCD 3 Ontology will also be uploaded to the GFBio Terminology Server (Karam et al. 2016), where individual concepts can be easily searched or queried, allowing for better interactive modelling of ABCD concepts. ABCD documentation now adheres to TDWG’s Standards Documentation Standard (SDS, https://www.tdwg.org/standards/sds/) and is located at https://abcd.tdwg.org/. The new site is hosted on Github: https://github.com/tdwg/abcd/tree/gh-pages.

Integrating terminologies into standard SQL: a new approach for research on routine data

BackgroundMost electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-based database.MethodsWe present an approach that allows databases accessible via SQL (Structured Query Language) to be searched directly through semantic queries without the need for further transformations. Therefore, we developed I) an extension to SQL named Ontology-SQL (O-SQL) that allows to use semantic expressions, II) a framework that uses a standard terminology server to annotate free-text containing database tables and III) a parser that rewrites O-SQL to SQL, so that such queries can be passed to the database server.ResultsI) We compared several semantic queries published to date and were able to reproduce them in a reduced, highly condensed form. II) The quality of the annotation process was measured against manual annotation, and we found a sensitivity of 97.62% and a specificity of 100.00%. III) Different semantic queries were analyzed, and measured with F-scores between 0.91 and 0.98.ConclusionsWe showed that systematic analysis of free-text-containing medical records is possible with standard tools. The seamless connection of ontologies and standard technologies from the database field represents an important constituent of unstructured data analysis. The developed technology can be readily applied to relationally organized data and supports the increasingly important field of translational research.

An eHealth Platform for the Support of a Brazilian Regional Network of Mental Health Care (eHealth-Interop): Development of an Interoperability Platform for Mental Care Integration.

BackgroundThe electronic exchange of health-related data can support different professionals and services to act in a more coordinated and transparent manner and make the management of health service networks more efficient. Although mental health care is one of the areas that can benefit from a secure health information exchange (HIE), as it usually involves long-term and multiprofessional care, there are few published studies on this topic, particularly in low- and middle-income countries.ObjectiveThe aim of this study was to design, implement, and evaluate an electronic health (eHealth) platform that allows the technical and informational support of a Brazilian regional network of mental health care. This solution was to enable HIE, improve data quality, and identify and monitor patients over time and in different services.MethodsThe proposed platform is based on client-server architecture to be deployed on the Web following a Web services communication model. The interoperability information model was based on international and Brazilian health standards. To test platform usage, we have utilized the case of the mental health care network of the XIII Regional Health Department of the São Paulo state, Brazil. Data were extracted from 5 different sources, involving 26 municipalities, and included national demographic data, data from primary health care, data from requests for psychiatric hospitalizations performed by community services, and data obtained from 2 psychiatric hospitals about hospitalizations. Data quality metrics such as accuracy and completeness were evaluated to test the proposed solution.ResultsThe eHealth-Interop integration platform was designed, developed, and tested. It contains a built-in terminology server and a record linkage module to support patients’ identification and deduplication. The proposed interoperability environment was able to integrate information in the mental health care network case with the support of 5 international and national terminologies. In total, 27,353 records containing demographic and clinical data were integrated into eHealth-Interop. Of these records, 34.91% (9548/27,353) were identified as patients who were present in more than 1 data source with different levels of accuracy and completeness. The data quality analysis was performed on 26 demographic attributes for each integrable patient record, totaling 248,248 comparisons. In general, it was possible to achieve an improvement of 18.40% (45,678/248,248) in completeness and 1.10% (2731/248,248) in syntactic accuracy over the test dataset after integration and deduplication.ConclusionsThe proposed platform established an eHealth solution to fill the gap in the availability and quality of information within a network of health services to improve the continuity of care and the health services management. It has been successfully applied in the context of mental health care and is flexible to be tested in other areas of care.

Ontoserver: a syndicated terminology server

BackgroundEven though several high-quality clinical terminologies, such as SNOMED CT and LOINC, are readily available, uptake in clinical systems has been slow and many continue to capture information in plain text or using custom terminologies. This paper discusses some of the challenges behind this slow uptake and describes a clinical terminology server implementation that aims to overcome these obstacles and contribute to the widespread adoption of standardised clinical terminologies.ResultsOntoserver is a clinical terminology server based on the Fast Health Interoperability Resources (FHIR) standard. Some of its key features include: out-of-the-box support for SNOMED CT, LOINC and OWL ontologies, such as the Human Phenotype Ontology (HPO); a fast, prefix-based search algorithm to ensure users can easily find content and are not discouraged from entering coded data; a syndication mechanism to facilitate keeping terminologies up to date; and a full implementation of SNOMED CT’s Expression Constraint Language (ECL), which enables sophisticated data analytics.ConclusionsOntoserver has been designed to overcome some of the challenges that have hindered adoption of standardised clinical terminologies and is used in several organisations throughout Australia. Increasing adoption is an important goal because it will help improve the quality of clinical data, which can lead to better clinical decision support and ultimately to better patient outcomes.

From single-case analysis of neonatal deaths toward a further reduction of the neonatal mortality rate.

Background The infant mortality rate (IMR), a key indicator of the quality of a healthcare system, has remained at approximately 3.5‰ for the past 10 years in Germany. Generic quality indicators (QIs), as used in Germany since 2010, greatly help in ensuring such a good value but do not seem to be able to further reduce the IMR. The neonatal mortality rate (NMR) contributes to 65-70% of the IMR. We therefore propose single-case analysis of neonatal deaths as an additional method and show an efficient way to implement this approach. Methods We used the Nordic-Baltic classification (NBC) to detect avoidable neonatal deaths. We applied this classification to a sample of 1968 neonatal death records, which represent over 90% of all neonatal deaths in East Berlin from 1973 to 1989. All cases were analyzed as to their preventability based on the complete perinatal and clinical data by a special commission of different experts. The NBC was automatically applied through natural language processing and an ontology-based terminology server. Results The NBC was used to select the group of cases that had a high potential of avoidance. The selected group represented 6.0% of all cases, and 60.4% of the cases within that group were judged avoidable or conditionally avoidable. The automatic detection of malformations showed an F1 score of 0.94. Conclusion The results show that our method can be applied automatically and is a powerful and highly specific tool for selecting potentially avoidable neonatal deaths and thus for supporting efficient single-case analysis.