Expanding opportunities in health data: Enhancing research by facilitating linkage of Ontario’s population-level health administrative data with data from patient support programs (PSPs)
Bibliographic record
Abstract
ObjectivesPatient Support Programs (PSPs) provide real-world evidence on short-term outcomes and drug adherence for specialty care. These data are collected directly from patients by pharmaceutical companies. Data linkage is facilitated by a publicly-funded health research institute which houses population-based, individual-level health administrative data for 14 million Ontario residents in Canada since 1992. Linkage provides a more comprehensive and transparent view of patients’ pathways and care. ApproachIndustry-funded research conducted through the data & analytic service must align with the institute’s mission, vision and values, and demonstrate a clear public benefit. For transparency, and to support broader public benefit and research access, final deliverables and analytic plans are posted on the institute’s website. Privacy impact assessments are conducted to ensure ethics approvals, consent and data sharing agreements are established, prior to data importation. To safeguard privacy, designated data covenantors encrypt and link the PSP data with in-house data holdings. Analyses are performed by senior analytic staff, providing researchers with summary-level reports. ResultsSince 2016, the institute has worked with organizations to link data for research, including PSP data on thousands of patients, enabling crucial insight into treatment patterns, drug adherence, costs and long-term outcomes. ConclusionLinkage of privately-owned PSP data with administrative health data at the institute, provides opportunities to identify gaps in care and improve quality of research. Data challenges around bias, transparency, completeness, and comprehensiveness are minimized. ImplicationsResults provide decision-makers and healthcare professionals with a trusted and comprehensive understanding of patient care pathways to improve health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".