British Columbia’s Health Data Platform: Unleashing the Power of a Data Environment Commons for Health and Health System Improvement
Bibliographic record
Abstract
IntroductionThere is no power for change greater than a community discovering what it cares about.1
 The Health Data Platform (HDP) will democratize British Columbia’s (population of approximately 4.6 million) health sector data by creating common enabling infrastructure that supports cross-organization analytics and research used by both decision makers and cademics. HDP will provide streamlined, proportionate processes that provide timelier access to data with increased transparency for the data consumer and provide shared data related services that elevate best practices by enabling consistency across data contributors, while maintaining continued stewardship of their data. HDP will be built in collaboration with Swansea University following an agile pragmatic approach starting with a minimum viable product.
 Objectives and ApproachBuild a data sharing environment that harnesses the data and the understanding and expertise about health data across academe, decision makers, and clinicians in the province by:
 
 Enabling a common harmonized approach across the sector on:
 
 Data stewardship
 Data access
 Data security and privacy
 Data management
 Data standards
 
 
 To:
 
 Enhance data consumer data access experience
 Increase process consistency and transparency
 Reduce burden of liberating data from a data source
 Build trust in the data and what it is telling us and therefore the decisions made
 Increase data accessibility safely and responsibly
 
 
 
 Working within the jurisdiction’s existing legislation, the Five Safes Privacy and Security Framework will be implemented, tailored to address the requirements of data contributors.
 ResultsThe minimum viable product will provide the necessary enabling infrastructure including governance to enable timelier access, safely to administrative data to a limited set of data consumers. The MVP will be expanded with another release planned for early 2021.
 Conclusion / ImplicationsCollaboration with Swansea University has enabled BC to accelerate its journey to increasing timelier access to data, safely and increasing the maturity of analytics by creating the enabling infrastructure that promotes collaboration and sharing of data and data approaches.
 1 Margaret Wheatley
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".