Exploring the Collection and Use of Health Data for Smart Cities Initiatives
Bibliographic record
Abstract
IntroductionICES is an entity in Ontario, Canada that collects and uses the personal health information (PHI) of individuals for evaluation, planning and monitoring of the provincial health system. It currently does not have legal authority to collect PHI from, or disclose PHI to, municipalities for the purpose of supporting evidence-based policymaking and enabling “Smarter Cities”. Objectives and ApproachTo assess how ICES could allow municipalities to access PHI, while maintaining strong privacy and security data protection, we first: (i) explored the legal data trust model as a vehicle for broader collection and use of municipal data, and (ii) analyzed the regulatory changes and type of framework that would enable broader access and use of PHI by municipalities. Following this and to demonstrate the value of access to ICES data for municipal planning, we identified a case project involving a municipal health stakeholder. Leveraging ICES’ remote access model, two local public health analysts performed analytics on de-sensitized, individual-level data in a secure analytic environment. ResultsWe determined that a legal data trust is not the appropriate model for the type of data sharing envisioned, but rather, a data governance and ethical use framework complimentary to a new legal regime for Smart Cities would be optimal. In Phase II the local municipal partner was able to identify several use cases for the ICES data that would support local policy making; access to these data was considered a critical enabler to improved evidence-based decision making. Conclusion / ImplicationsAllowing municipal policy makers to use data under a complimentary framework to a new legal regime, may improve policy and produce direct economic impact for municipalities where evidence needed for decision-making is lacking; representing a practical step forward towards Smart Cities.
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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 | 0.048 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".