Enhancing Drug-Related Data Exchange: Advanced Technical Support for Interoperability.

This paper describes the technical support services developed to facilitate the usage of the Canadian Drug Ontology (OCRx). These services include a lookup service named PaperRx, which retrieves OCRx drug entities using description logic queries (DLqueries) conducted within OCRx's classified structure. Additionally, the services feature a SPARQL endpoint, an OWL file downloader, and a RESTful Application Programming Interface. This suite of services supports the application of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles to OCRx.

Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices.

OBJECTIVES: This study aimed to support the implementation of the 11th Revision of the International Classification of Diseases (ICD-11). We used common comorbidity indices as a case study for proactively assessing the impact of transitioning to ICD-11 for mortality and morbidity statistics (ICD-11-MMS) on real-world data analyses. MATERIALS AND METHODS: Using the MIMIC IV database and a table of mappings between the clinical modification of previous versions of ICD and ICD-11-MMS, we assembled a population whose diagnosis can be represented in ICD-11-MMS. We assessed the impact of ICD version on cross-sectional analyses by comparing the populations' distribution of Charlson and Elixhauser comorbidity indices (CCI, ECI) across different ICD versions, along with the adjustment in comorbidity weighting. RESULTS: We found that ICD versioning could lead to (1) alterations in the population distribution and (2) changes in the weight that can be assigned to a comorbidity category in a reweighting initiative. In addition, this study allowed the creation of the corresponding ICD-11-MMS codes list for each component of the CCI and the ECI. DISCUSSION: In common with the implementations of previous versions of ICD, implementation of ICD-11-MMS potentially hinders comparability of comorbidity burden on health outcomes in research and clinical settings. CONCLUSION: Further research is essential to enhance ICD-11-MMS usability, while mitigating, after identification, its adverse effects on comparability of analyses.

Enriching the FIDEO ontology with food-drug interactions from online knowledge sources.

The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed.

Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective.

OBJECTIVES: The objective of this study is to describe how OCRx (Canadian Drug Ontology) has been built to address the dual need for local drug information integration in Canada and alignment with international standards requirements. METHODS: This paper delves into (i) the implementation efforts to meet the Identification of Medicinal Product (IDMP) requirements in OCRx, alongside the ontology update strategy, (ii) the structure of the ontology itself, (iii) the alignment approach with several reference Knowledge Organization Systems, including SNOMED CT, RxNorm, and the list of "Code Identifiant de Spécialité" (CIS-Code), and (iv) the look-up services developed to facilitate its access and utilization. RESULTS: Each OCRx release contains two distinct versions: the full and the up-to-date version. The full version encompasses all drugs with a DIN code sanctioned by Health Canada, while the up-to-date version is limited to drugs currently marketed in Canada. In the last release of OCRx, the full version comprises 162,400 classes; meanwhile, the up-to-date version consists of 36,909 classes. In terms of mappings with OCRx, substances in RxNorm and SNOMED CT fall below 40%, registering at 37% and 22% respectively. Meanwhile, mappings for CIS-Code achieve coverage of 61%. The strength mappings are notably low for RxNorm at 40% and for CIS-code at 28%. This affects the mapping of clinical drugs, which are predominantly alignable through post-coordinated expressions: 56% for RxNorm, 80% for SNOMED CT, and 35% for CIS-Code. The main support service of OCRx is a look-up service known as PaperRx that displays OCRx's entities based on description logic queries (DL-queries) performed through the classified structure of OCRx. The look-up services also contain a SPARQL endpoint, an OCRx OWL file downloader, and a RESTful API. DISCUSSION: The OCRx ontology demonstrates a significant effort towards integrating Canadian drug information with international standards. However, there are areas for improvement. In the future, our focus will be on refining the structure of OCRx for better classification capability and improvement of dosage conversion. Additionally, we aim to harness OCRx in constructing an ontology-based annotator, setting our sights on its deployment in real-world data integration scenarios.

Automated Credibility Assessment of Web-Based Health Information Considering Health on the Net Foundation Code of Conduct (HONcode): Model Development and Validation Study.

BACKGROUND: An increasing number of users are turning to web-based sources as an important source of health care guidance information. Thus, trustworthy sources of information should be automatically identifiable using objective criteria. OBJECTIVE: The purpose of this study was to automate the assessment of the Health on the Net Foundation Code of Conduct (HONcode) criteria, enhancing our ability to pinpoint trustworthy health information sources. METHODS: A data set of 538 web pages displaying health content was collected from 43 health-related websites. HONcode criteria have been considered as web page and website levels. For the website-level criteria (confidentiality, transparency, financial disclosure, and advertising policy), a bag of keywords has been identified to assess the criteria using a rule-based model. For the web page-level criteria (authority, complementarity, justifiability, and attribution) several machine learning (ML) approaches were used. In total, 200 web pages were manually annotated until achieving a balanced representation in terms of frequency. In total, 3 ML models-random forest, support vector machines (SVM), and Bidirectional Encoder Representations from Transformers (BERT)-were trained on the initial annotated data. A second step of training was implemented for the complementarity criterion using the BERT model for multiclass classification of the complementarity sentences obtained by annotation and data augmentation (positive, negative, and noncommittal sentences). Finally, the remaining web pages were classified using the selected model and 100 sentences were randomly selected for manual review. RESULTS: For web page-level criteria, the random forest model showed a good performance for the attribution criterion while displaying subpar performance in the others. BERT and SVM had a stable performance across all the criteria. BERT had a better area under the curve (AUC) of 0.96, 0.98, and 1.00 for neutral sentences, justifiability, and attribution, respectively. SVM had the overall better performance for the classification of complementarity with the AUC equal to 0.98. Finally, SVM and BERT had an equal AUC of 0.98 for the authority criterion. For the website level criteria, the rule-based model was able to retrieve web pages with an accuracy of 0.97 for confidentiality, 0.82 for transparency, and 0.51 for both financial disclosure and advertising policy. The final evaluation of the sentences determined 0.88 of precision and the agreement level of reviewers was computed at 0.82. CONCLUSIONS: Our results showed the potential power of automating the HONcode criteria assessment using ML approaches. This approach could be used with different types of pretrained models to accelerate the text annotation, and classification and to improve the performance in low-resource cases. Further work needs to be conducted to determine how to assign different weights to the criteria, as well as to identify additional characteristics that should be considered for consolidating these criteria into a comprehensive reliability score.

Identify and classify interprofessional primary care performance indicators: a scoping review protocol.

INTRODUCTION: Measuring the performance of interprofessional primary care is needed to examine whether this model of care is achieving its desired outcomes on patient care and health system effectiveness as well as to guide quality improvement initiatives. The aim of this scoping review is to map the literature on primary care performance measurement indicators to determine the extent to which current indicators capture or could be adapted to capture processes, outputs and outcomes that reflect interprofessional primary care. METHODS AND ANALYSIS: The review will be guided by the six-stage framework by Arksey and O'Malley (2005). MEDLINE, Embase, CINAHL, grey literature and the reference list of key studies will be searched to identify any study, published in English or French between 2000 and 2022, related to the concepts of performance indicators, frameworks, interprofessional teams and primary care. Two reviewers will independently screen all abstracts and full-text studies for inclusion. Eligible indicators will be classified according to process, output and outcome domains proposed by two validated frameworks. This study started in November 2022 and is expected to be completed by July 2023. ETHICS AND DISSEMINATION: This review does not require ethical approval. The results will be disseminated through a peer-reviewed publication, conference presentations and presentations to stakeholders.

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®).

OBJECTIVE: Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems. MATERIALS AND METHODS: We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied. RESULTS: We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59. CONCLUSION: We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only.

OCRx: Canadian Drug Ontology.

This paper describes the development and evaluation of a Canadian drug ontology (OCRx), built to provide a normalized and standardized description of drugs that are authorized to be marketed in Canada. OCRx aims to improve the usability and interoperability of drugs terminologies for a non-ambiguous access to drugs information that is available in electronic health record systems. We present the first release of OCRx that is described in Web Ontology Language and aligned to the Identification of Medicinal Product (IDMP) standards. For comparison purposes, OCRx is mapped to RxNorm, its US variant.

Building a Graph Representation of LOINC® to Facilitate its Alignment to French Terminologies.

The aim of our study was to create a graph model for the description of LOINC® concepts. The main objective of the constructed structure is to facilitate the alignment of French local terminologies to LOINC. The process consisted of automatically incorporating the naming rules of LOINC labels, based on punctuation. We implemented these rules and applied them to the French variants of LOINC and then created attributes and concepts described with synonymous labels. When comparing the created attributes to the stated ones, the multiple mappings led to the identification of errors that must be corrected for improving the translation quality. These mappings are consecutive to semantic errors generated during the translation process. They mainly corresponded to misinterpretations of LOINC concepts and/or LOINC attributes.

Integrating cancer diagnosis terminologies based on logical definitions of SNOMED CT concepts.

In oncology, the reuse of data is confronted with the heterogeneity of terminologies. It is necessary to semantically integrate these distinct terminologies. The semantic integration by using a third terminology as a support is a conventional approach for the integration of two terminologies that are not very structured. The aim of our study was to use SNOMED CT for integrating ICD-10 and ICD-O3. We used two complementary resources, mapping tables provided by SNOMED CT and the NCI Metathesaurus, in order to find mappings between ICD-10 or ICD-O3 concepts and SNOMED CT concepts. We used the SNOMED CT structure to filter inconsistent mappings, as well as to disambiguate multiple mappings. Based on the remaining mappings, we used semantic relations from SNOMED CT to establish links between ICD-10 and ICD-O3. Overall, the coverage of ICD-O3 and ICD10 codes was over 88%. Finally, we obtained an integration of 24% (203/852) of ICD-10 concepts with 86% (888/1032) of ICD-O3 morphology concepts combined to 39% (127/330) of ICD-O3 topography concepts. Comparing our results with the 23,684 ICD-O3 pairs mapped to ICD-10 concepts in the SEER conversion file, we found 17,447 pairs of ICD-O3 concepts in common among which 11,932 pairs were integrated with the same ICD-10 concept as the SEER conversion file. The automated process leverages logical definitions of SNOMED CT concepts. While the low quality of some of these definitions impacted negatively the integration process, the identification of such situations made it possible to indirectly audit the structure of SNOMED CT.