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.

Contextualizing protein representations using deep learning on protein networks and single-cell data.

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce Pinnacle, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, Pinnacle provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. Pinnacle's contextualized representations of proteins reflect cellular and tissue organization and Pinnacle's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained Pinnacle's protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations for important protein interactions in immuno-oncology (PD-1/PD-L1 and B7-1/CTLA-4) and to study the effects of drugs across cell type contexts. Pinnacle outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that predict therapeutic targets better than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). Pinnacle is a graph-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

Building a Logic Model to Foster Engagement and Learning Using the Case of a Province-Wide Multispecies Antimicrobial Use Monitoring System.

Successfully designing and implementing a program is complex; it requires a reflexive balance between the available resources and the priorities of various stakeholders, both of which change over time. Logic models are theory-based evaluation approaches used to identify and address key challenges of a program. This article describes the process of building a logic model on advanced theories in complexity studies. The models aim to support a province-wide multispecies monitoring system of antimicrobial use (AMU), designed in collaboration with the animal health sector in Quebec (Canada). Based on a rigorous theoretical foundation, the logic model is built in three steps: (1) mapping, a narrative review of literature on similar programs in other jurisdictions; (2) framing, iterative consultations with project members to elaborate the logic model; (3) shaping, hypotheses based on the logic model. The model emerges from the reflexive balancing of current scientific knowledge and empirical insights to gather relevant information about stakeholders from interdisciplinary experts that led a 3-year consensus-building process within the community. Recognizing the challenge of unpacking theories for practical use, we illustrate how the process of an "open" logic model building could enable governance coordination in complex processes. Logic models are useful for evaluating public, private, and academic partnerships in One Health programs that characterize an adaptive governance process.

Improving medication safety in a paediatric hospital: a mixed-methods evaluation of a newly implemented computerised provider order entry system.

OBJECTIVES: Computerised provider order entry (CPOE) systems have been implemented around the world as a solution to reduce ordering and transcription errors. However, previous literature documented many challenges to attain this goal, especially in paediatric settings. The objectives of this study were to (1) analyse the impact of a paediatric CPOE system on medication safety and (2) suggest potential error prevention strategies. METHODS: A pre-post observational study was conducted at the pilot ward (n=60 beds) of a paediatric academic health centre through mixed methods. The implementation project and medication management workflows were described through active participation to the project management team, observation, discussions and analysis of related documents. Furthermore, using incident reports, the nature of each error and error rate was compared between the preperiod and postperiod. RESULTS: The global error rate was lower, but non-statistically significant, in the post implementation phase, which was mostly driven by a significant reduction in errors during order acknowledgement, transmission and transcription. Few errors occurred at the prescription step, and most errors occurred during medication administration. Furthermore, some errors could have been prevented using a CPOE in the pre-implementation period, and the CPOE led to few technology-related errors. DISCUSSION AND CONCLUSION: This study identified both intended and unintended effects of CPOE adoption through the entire medication management workflow. This study revealed the importance of simplifying the acknowledgement, transmission and transcribing steps through the implementation of a CPOE to reduce medication errors. Improving the usability of the electronic medication administration record could help further improve medication safety.

Evaluating the implementation of a referral system for virtual pharmacy counselling in a province-wide nurse phone line.

Digital technology offers several opportunities to improve access to professional expertise in primary care, and the offer of various "virtual" services has exploded in the past few years. The aim of this study was to evaluate the implementation of a direct-to-consumer on-line pharmacy consultation service (Ask Your Pharmacist - AYP) to a universal phone consultation service led by the universal public health system in Quebec (811 Info-Santé), through a direct bridge. Semi-structured interviews were conducted with clinician users of the service, and stakeholders involved in this pilot project funded by the Ministry of Economy (n = 22); documents were also analyzed, and content of the question was asked through the AYP service. Adoption of the service was low, and it suggests a poor alignment between the need and the service as implemented. Further research should investigate the mechanisms for an appropriate integration of digital services for primary care universal consultation services.

Implementation of a Computerized Provider Order Entry System in a Pediatric Hospital in Canada.

The Centre Hospitalier Universitaire Sainte-Justine (Montreal, Canada) is a pediatric academic tertiary hospital that has begun the implementation of a commercial computerized provider order entry system (CPOE) in October 2019. The objectives of this paper are 1) to estimate the impact of the CPOE system on medication errors, and 2) to identify vulnerability issues related to the configuration of the CPOE system's design. Using a pre-post implementation methodology measuring medication errors captured by clinical pharmacists revealed that the implementation of a CPOE has eliminated all prescription conformity (e.g., missing fields) and legibility errors. Pharmacists have continued to detect medication errors, especially inappropriate dosing instructions, and to intervene in similar clinical situations (medication reconciliation, deprescribing, adjusting orders). Additionally, the vulnerability analysis, based on typical clinical order test cases in an inpatient pediatric setting, highlighted the need to configure a clinical decision support system that can identify inappropriate dosing instructions for pediatric patients.

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.

Development of a Method for Extracting Structured Dose Information from Free-Text Electronic Prescriptions.

In this study, we sought to develop an automatic parser tool for unstructured free-text electronic prescriptions, focusing specifically on defining the daily dose. We manually coded a set of electronic discharge prescriptions and established the most reliable rules to structure the medication data. A named-entity recognition (NER) parser tool was implemented, which was capable of identifying 90% of the doses and 86% of the frequencies from 255 dosage instructions.