Using the Canadian Institute for Health Information’s Information Quality Framework to Support Integration and Utilization of Complex, Multi-Jurisdictional Data
Bibliographic record
Abstract
The Canadian Institute for Health Information (CIHI) provides essential information on Canada’s health systems and the health of Canadians. This presentation discusses information quality’s role in the integration and utilization of CIHI’s complex, multi-sector and multi-jurisdictional data. IntroductionCIHI’s Data and Information Quality Program is recognized internationally for its comprehensiveness and high standards. As the need for linked data research increases, the requirements on quality continue to grow. CIHI’s multi-sector, multi-jurisdictional healthcare system and the varying health policies, care delivery models, and data collection practices that go with it pose challenges for researchers as they try to pull the data together in a comprehensive way. CIHI’s Information Quality Framework forms the foundation for addressing these challenges and ensuring data are fit for integration and are properly utilized. Objectives and ApproachIn 2019, a connected data quality project was initiated to improve the usability of CIHI’s analytical data. Information quality framework concepts were applied across CIHI data sources to better understand data linkage challenges, measure inconsistencies across data sources, identify opportunities to improve data and standards, and develop resources to support users. ResultsFindings from the project identified key connected data quality activities for the organization to operationalize. These focus on quality assessment and reporting; harmonization of data standards; expanded documentation and analytical resources; data classification and profiling tools to support descriptive analysis; and new source of truth and pre-linked datasets. Quality activities were prioritized based on need and complexity, and “connected data teams” were established to carry out the work. Conclusion / ImplicationsExpansion of CIHI’s quality framework across data sources facilitates its data linkage capabilities and “connected data” use. It enables the evolution of CIHI’s analytical environments and information products from being database specific to integrated-data driven, and facilitates the use of CIHI’s analytical data for research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.226 | 0.331 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.037 | 0.057 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.032 | 0.016 |
| Open science | 0.011 | 0.026 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".