Ontoserver: a syndicated terminology server

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting

BACKGROUND

Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.

METHODS

This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.

RESULTS

Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.

CONCLUSION

In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset

BACKGROUND

The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository.

METHODS

We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework.

RESULTS

Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%.

CONCLUSION

The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review

BACKGROUND

The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard.

OBJECTIVE

The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies.

METHODS

A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard.

RESULTS

A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language-based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability.

CONCLUSIONS

This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

Future-proofing genomic data and consent management: a comprehensive review of technology innovations

Genomic information is increasingly used to inform medical treatments and manage future disease risks. However, any personal and societal gains must be carefully balanced against the risk to individuals contributing their genomic data. Expanding our understanding of actionable genomic insights requires researchers to access large global datasets to capture the complexity of genomic contribution to diseases. Similarly, clinicians need efficient access to a patient's genome as well as population-representative historical records for evidence-based decisions. Both researchers and clinicians hence rely on participants to consent to the use of their genomic data, which in turn requires trust in the professional and ethical handling of this information. Here, we review existing and emerging solutions for secure and effective genomic information management, including storage, encryption, consent, and authorization that are needed to build participant trust. We discuss recent innovations in cloud computing, quantum-computing-proof encryption, and self-sovereign identity. These innovations can augment key developments from within the genomics community, notably GA4GH Passports and the Crypt4GH file container standard. We also explore how decentralized storage as well as the digital consenting process can offer culturally acceptable processes to encourage data contributions from ethnic minorities. We conclude that the individual and their right for self-determination needs to be put at the center of any genomics framework, because only on an individual level can the received benefits be accurately balanced against the risk of exposing private information.

The standard problem

OBJECTIVE

This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.

METHODS

Beginning with the notion of common models, the framework describes the general standard problem-the seeming impossibility of creating a singular, persistent, and definitive standard which is not subject to change over time in an open system.

RESULTS

The standard problem arises from uncertainty driven by variations in operating context, standard quality, differences in implementation, and drift over time. As a result, fitting work using conformance services is needed to repair these gaps between a standard and what is required for real-world use. To guide standards design and repair, a framework for measuring performance in context is suggested, based on signal detection theory and technomarkers. Based on the type of common model in operation, different conformance strategies are identified: (1) Universal conformance (all agents access the same standard); (2) Mediated conformance (an interoperability layer supports heterogeneous agents); and (3) Localized conformance (autonomous adaptive agents manage their own needs). Conformance methods include incremental design, modular design, adaptors, and creating interactive and adaptive agents.

DISCUSSION

Machine learning should have a major role in adaptive fitting. Research to guide the choice and design of conformance services may focus on the stability and homogeneity of shared tasks, and whether common models are shared ahead of time or adjusted at task time.

CONCLUSION

This analysis conceptually decouples interoperability and standardization. While standards facilitate interoperability, interoperability is achievable without standardization.

Towards interoperability in infection control: a standard data model for microbiology

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

Machine Learning-Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Background

Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable.

Objective

This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs.

Methods

Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system's functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems.

Results

A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy.

Conclusions

Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.

Aligning Semantic Interoperability Frameworks with the FOXS Stack for FAIR Health Data

BACKGROUND

 FAIR Guiding Principles present a synergy with the use cases for digital health records, in that clinical data need to be found, accessible within a range of environments, and data must interoperate between systems and subsequently reused. The use of HL7 FHIR, openEHR, IHE XDS, and SNOMED CT (FOXS) together represents a specification to create an open digital health platform for modern health care applications.

OBJECTIVES

 To describe where logical FOXS components align to the European Open Science Cloud Interoperability Framework (EOSC-IF) reference architecture for semantic interoperability. This should provide a means of defining if FOXS aligns to FAIR principles and to establish the data models and structures that support longitudinal care records as being fit to underpin scientific research.

METHODS

 The EOSC-IF Semantic View is a representation of semantic interoperability where meaning is preserved between systems and users. This was analyzed and cross-referenced with FOXS architectural components, mapping concepts, and objects that describe content such as catalogues and semantic artifacts.

RESULTS

 Majority of conceptual Semantic View components were featured within the FOXS architecture. Semantic Business Objects are composed of a range of elements such as openEHR archetypes and templates, FHIR resources and profiles, SNOMED CT concepts, and XDS document identifiers. Semantic Functional Content comprises catalogues of metadata that were also supported by openEHR and FHIR tools.

CONCLUSIONS

 Despite some elements of EOSC-IF being vague (e.g., FAIR Digital Object), there was a broad conformance to the framework concepts and the components of a FOXS platform. This work supports a health-domain-specific view of semantic interoperability and how this may be achieved to support FAIR data for health research via a standardized framework.

Strategies and techniques for quality control and semantic enrichment with multimodal data: a case study in colorectal cancer with eHDPrep

BACKGROUND

Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses.

FINDINGS

We developed an R package for electronic health data preparation, "eHDPrep," demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative "meta-variables" according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset.

CONCLUSIONS

eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package = eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

Pathling: analytics on FHIR

Background

Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in the task of analyzing Fast Healthcare Interoperability Resources (FHIR^®) data and associated clinical terminology.

Results

A server implementation was developed, featuring a FHIR API with new operations designed to support exploratory data analysis (EDA), advanced patient cohort selection and data preparation tasks. Integration with a FHIR Terminology Service is also supported, allowing users to incorporate knowledge from rich terminologies such as SNOMED CT within their queries. A prototype user interface for EDA was developed, along with visualizations in support of a health data analysis project.

Conclusions

Experience with applying this technology within research projects and towards the development of analytics-enabled applications provides a preliminary indication that the FHIR Analytics API pattern implemented by Pathling is a valuable abstraction for data scientists and software developers within the health care domain. Pathling contributes towards the value proposition for the use of FHIR within health data analytics, and assists with the use of complex clinical terminologies in that context.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13326-022-00277-1.

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review

OBJECTIVES

The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research.

MATERIALS AND METHODS

We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities.

RESULTS

Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself.

DISCUSSION

Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities.

CONCLUSION

The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

LUMA: A Mapping Assistant for Standardizing the Units of LOINC-Coded Laboratory Tests

The coding system Unified Code for Units of Measure (UCUM) serves the unambiguous electronic communication of physical quantities and their measurements and has faced a slow uptake. Despite being closely related to popular healthcare standards such as LOINC, laboratories still majorly report results using proprietary unit terms. Currently available methods helping users create mappings between their units and UCUM are not flexible and automated enough to be of great use in trying to remedy this. We propose the “LOINC to UCUM Mapping Assistant” (LUMA) as a tool able to overcome the drawbacks of existing approaches while being more accessible even to inexperienced users. By mapping LOINC’s Property axis to representations within UCUM reflecting its semantics, we were able to formalize the association between the two. An HL7 FHIR back-end provides LUMA with UCUM unit recommendations sourced from existing lookup tables simply by providing it with a LOINC code. Additionally, the mappings users created may be used to perform unit conversions from proprietary units to UCUM. The tool was evaluated with five participants from the LADR laboratory network in Germany, who valued the streamlined approach to creating the mappings and particularly emphasized the utility of being able to perform unit conversions within the tool.

Semantic Search for Large Scale Clinical Ontologies

Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies. A search algorithm that overcomes this problem is useful in applications such as concept normalisation and ontology matching, where concepts can be referred to in different ways, using different synonyms. In this paper, we present a deep learning based approach to build a semantic search system for large clinical ontologies. We propose a Triplet-BERT model and a method that generates training data directly from the ontologies. The model is evaluated using five real benchmark data sets and the results show that our approach achieves high results on both free text to concept and concept to concept searching tasks, and outperforms all baseline methods.

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for federated Feasibility Queries in the context of COVID-19: An automated approach (Preprint)

Background

The 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.

Objective

This 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.

Methods

We 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.

Results

In 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.

Conclusions

We 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.

Transformation of microbiology data into a standardised data representation using OpenEHR

The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.

Interoperable medical data: The missing link for understanding COVID-19

Being able to link clinical outcomes to SARS‐CoV‐2 virus strains is a critical component of understanding COVID‐19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the ‘Fast Healthcare Interoperable Resource’ (FHIR) implementation guide, we introduce an ontology‐based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data‐driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID (‘Global Initiative on Sharing All Influenza Data’), immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

An Interoperable UMLS Terminology Service Using FHIR

The Unified Medical Language System (UMLS) is an internationally recognized medical vocabulary that enables semantic interoperability across various biomedical terminologies. To use its knowledge, the users must understand its complex knowledge structure, a structure that is not interoperable or is not compliant with any known biomedical and healthcare standard. Further, the users also need to have good technical skills to understand its inner working and interact with UMLS in general. These barriers might cause UMLS usage concerns among inter-disciplinary users in biomedical and healthcare informatics. Currently, there exists no terminology service that normalizes UMLS’s complex knowledge structure to a widely accepted interoperable healthcare standard and allows easy access to its knowledge, thus hiding its workings. The objective of this research is to design and implement a light-weight terminology service that allows easy access to UMLS knowledge structured using the fast health interoperability resources (FHIR) standard, a widely accepted interoperability healthcare standard. The developed terminology service, named UMLS FHIR, leverages FHIR resources and features, and can easily be integrated into any application to consume UMLS knowledge in the FHIR format without the need to understand UMLS’s native knowledge structure and its internal working.

A Neuro-ontology for the neurological examination

BACKGROUND

The use of clinical data in electronic health records for machine-learning or data analytics depends on the conversion of free text into machine-readable codes. We have examined the feasibility of capturing the neurological examination as machine-readable codes based on UMLS Metathesaurus concepts.

METHODS

We created a target ontology for capturing the neurological examination using 1100 concepts from the UMLS Metathesaurus. We created a dataset of 2386 test-phrases based on 419 published neurological cases. We then mapped the test-phrases to the target ontology.

RESULTS

We were able to map all of the 2386 test-phrases to 601 unique UMLS concepts. A neurological examination ontology with 1100 concepts has sufficient breadth and depth of coverage to encode all of the neurologic concepts derived from the 419 test cases. Using only pre-coordinated concepts, component ontologies of the UMLS, such as HPO, SNOMED CT, and OMIM, do not have adequate depth and breadth of coverage to encode the complexity of the neurological examination.

CONCLUSION

An ontology based on a subset of UMLS has sufficient breadth and depth of coverage to convert deficits from the neurological examination into machine-readable codes using pre-coordinated concepts. The use of a small subset of UMLS concepts for a neurological examination ontology offers the advantage of improved manageability as well as the opportunity to curate the hierarchy and subsumption relationships.

FHIR OWL: Transforming OWL ontologies into FHIR terminology resources

The FHIR specification provides a mechanism to access clinical terminologies using a standard API, and many existing terminologies, such as SNOMED CT, are well supported. However, in areas such as genomics, terminologies from other domains are starting to be used in clinical settings. Many of these are authored or distributed in Web Ontology Language (OWL) format. In this paper we describe a transformation between OWL ontologies and FHIR terminology resources. The results show that there are several challenges in implementing the transformation, with the major one being the lack of a modularisation mechanism in the FHIR code system resource that resembles the import mecha nism available in OWL. A workaround with minimal drawbacks was successfully implemented in this solution. The availability of this transformation is significant because it enables a broad range of terminologies that are currently available in OWL to be available using the FHIR API.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.