The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research

Dahl, Lindsey Todd; Katz, Alan; McGrail, Kimberlyn; Diverty, Brent; Éthier, Jean-François; Gavin, Frank; McDonald, James Ted; Paprica, P. Alison; Schull, Michael J.; Walker, Jennifer; Wu, Juliana

doi:10.23889/ijpds.v5i1.1374

Cited by 7 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the resulted relational data model was used in different use cases of the Quebec SPOR Support Unit and the SPOR Canadian data platform. 16 The prototype and detailed results, including the RDB scripts, can be found on GitHub: https://github.com/OpenLHS/OntoRela.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background. A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with its full semantic remains a challenging task. Objectives. This paper addresses the question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and its semantic to support various applications? Method. This paper describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. Results. This paper (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents , a prototype developed to demonstrate the conversion rules. Conclusion. This work is a first step towards automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.

show abstract

Section: Resultsmentioning

confidence: 99%

“…SPOR Canadian data platform 16 . The prototype and detailed results, including the RDB scripts, can be found on GitHub: https://github.com/OpenLHS/OntoRela.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…An environmental scan of institutes and initiatives that follow their strategic funding to understand research impacts, such as the Canadian Institute for Health Research’s Community-Based Primary Health Care reports and the SPOR Evidence Alliance [ 37 ] will also be conducted. We also aim to examine metrics used by funding agencies within jurisdictions.…”

Section: Methodsmentioning

confidence: 99%

The Conceptualization and Measurement of Research Impact in Primary Health Care: Protocol for a Rapid Scoping Review

Aggarwal,

Hutchison,

Kokorelias

et al. 2024

JMIR Res Protoc

View full text Add to dashboard Cite

Background The generation of research evidence and knowledge in primary health care (PHC) is crucial for informing the development and implementation of interventions and innovations and driving health policy, health service improvements, and potential societal changes. PHC research has broad effects on patients, practices, services, population health, community, and policy formulation. The in-depth exploration of the definition and measures of research impact within PHC is essential for broadening our understanding of research impact in the discipline and how it compares to other health services research. Objective The objectives of the study are (1) to understand the conceptualizations and measures of research impact within the realm of PHC and (2) to identify methodological frameworks for evaluation and research impact and the benefits and challenges of using these approaches. The forthcoming review seeks to guide future research endeavors and enhance methodologies used in assessing research impact within PHC. Methods The protocol outlines the rapid review and environmental scan approach that will be used to explore research impact in PHC and will be guided by established frameworks such as the Canadian Academy of Health Sciences Impact Framework and the Canadian Health Services and Policy Research Alliance. The rapid review follows scoping review guidelines (PRISMA-ScR; Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews). The environmental scan will be done by consulting with professional organizations, academic institutions, information science, and PHC experts. The search strategy will involve multiple databases, citation and forward citation searching, and manual searches of gray literature databases, think tank websites, and relevant catalogs. We will include gray and scientific literature focusing explicitly on research impact in PHC from high-income countries using the World Bank classification. Publications published in English from 1978 will be considered. The collected papers will undergo a 2-stage independent review process based on predetermined inclusion criteria. The research team will extract data from selected studies based on the research questions and the CRISP (Consensus Reporting Items for Studies in Primary Care) protocol statement. The team will discuss the extracted data, enabling the identification and categorization of key themes regarding research impact conceptualization and measurement in PHC. The narrative synthesis will evolve iteratively based on the identified literature. Results The results of this study are expected at the end of 2024. Conclusions The forthcoming review will explore the conceptualization and measurement of research impact in PHC. The synthesis will offer crucial insights that will guide subsequent research, emphasizing the need for a standardized approach that incorporates diverse perspectives to comprehensively gauge the true impact of PHC research. Furthermore, trends and gaps in current methodologies will set the stage for future studies aimed at enhancing our understanding and measurement of research impact in PHC. International Registered Report Identifier (IRRID) PRR1-10.2196/55860

show abstract

“…These features are described in the following sections. The PARS3 [43] data access system, in deployment in Canada [44], is currently using SDAM to structure its communications. Candidate protocols from the project, and other standards like FHIR, are mentioned in the different layers (a full presentation and discussion of these protocols is out of scope for this article).…”

Section: Sdam Layer Descriptionsmentioning

confidence: 99%

A Sensitive Data Access Model in Support of Learning Health Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

Given the ever-growing body of knowledge, healthcare improvement hinges more than ever on efficient knowledge transfer to clinicians and patients. Promoted initially by the Institute of Medicine, the Learning Health System (LHS) framework emerged in the early 2000s. It places focus on learning cycles where care delivery is tightly coupled with research activities, which in turn is closely tied to knowledge transfer, ultimately injecting solid improvements into medical practice. Sensitive health data access across multiple organisations is therefore paramount to support LHSs. While the LHS vision is well established, security requirements to support them are not. Health data exchange approaches have been implemented (e.g., HL7 FHIR) or proposed (e.g., blockchain-based methods), but none cover the entire LHS requirement spectrum. To address this, the Sensitive Data Access Model (SDAM) is proposed. Using a representation of agents and processes of data access systems, specific security requirements are presented and the SDAM layer architecture is described, with an emphasis on its mix-network dynamic topology approach. A clinical application benefiting from the model is subsequently presented and an analysis evaluates the security properties and vulnerability mitigation strategies offered by a protocol suite following SDAM and in parallel, by FHIR.

show abstract

The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research

Cited by 7 publications

References 34 publications

Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

The Conceptualization and Measurement of Research Impact in Primary Health Care: Protocol for a Rapid Scoping Review

A Sensitive Data Access Model in Support of Learning Health Systems

Contact Info

Product

Resources

About