The implementation of a value-based learning health system for preventative care in Ontario, Canada.
Bibliographic record
Abstract
OBJECTIVE: While value-based learning health systems may address challenges associated with the integrative delivery of therapeutic lifestyle management in usual care, the extent to which they have been evaluated in real-world settings have remained limited. METHODS: To explore the feasibility and user-experiences, associated with the first-year implementation of a preventative Learning Health System (LHS), consecutive patients were evaluated following referral from primary and/or specialty care providers from the Halton and Greater Toronto Area in Ontario, Canada, between December 2020 and December 2021. The integration of a LHS into medical care was facilitated using a digital e-learning platform, and consisted of exercise, lifestyle, and disease-management counselling. The dynamic monitoring of user-data allowed patients and providers to modify goals, treatment plans, and care-delivery in real-time in accordance with patient engagement, weekly exercise, and risk-factor targets. All program costs were covered by the public-payer health care system using a physician fee-for-service payment model. Descriptive statistics evaluated attendance to prescheduled visits, drop-out rates, changes in self-reported weekly Metabolic Expenditure Task-Minutes (MET-MINUTES), perceived changes in health knowledge, lifestyle behaviours, health status, satisfaction with care, and programmatic costs. RESULTS: 378 of 437 patients (86.5%) enrolled in the 6-month program; The average age of patients was 61.2 ± 12.2, 156 (41.3%) of which were female and 140 (37.0%) with established coronary disease. After 1 year, 15.6% dropped out of the program. On average, weekly MET-MINUTES rose by 191.1 throughout the program (95% CI [331.82, 57.96], P=0.007), with increases most prominent among sedentary populations. Participants reported significant improvements in perceived health status and health knowledge, at a total health-care delivery cost of $517.70 per patient for a completed program. CONCLUSION: The implementation of an integrative preventative learning health system was feasible, with high patient engagement and favourable user-experiences. Further research is required to compare health outcomes against usual care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".