Triple Aim in Canada: developing capacity to lead to better health, care and cost
Bibliographic record
Abstract
QUALITY PROBLEM: Many modern health systems strive for 'Triple Aim' (TA)-better health for populations, improved experience of care for patients and lower costs of the system, but note challenges in implementation. Outcomes of applying TA as a quality improvement framework (QI) have started to be realized with early lessons as to why some systems make progress while others do not. INITIAL ASSESSMENT: Limited evidence is available as to how organizations create the capacity and infrastructure required to design, implement, evaluate and sustain TA systems. CHOICE OF SOLUTION: To support embedding TA across Canada, the Canadian Foundation for Healthcare Improvement supported enrolment of nine Canadian teams to participate in the Institute for Healthcare Improvement's TA Improvement Community. IMPLEMENTATION: Structured support for TA design, implementation, evaluation and sustainability was addressed in a collaborative programme of webinars and action periods. Teams were coached to undertake and test small-scale improvements before attempting to scale. EVALUATION: A summative evaluation of the Canadian cohort was undertaken to assess site progress in building TA infrastructure across various healthcare settings. The evaluation explored the process of change, experiences and challenges and strategies for continuous QI. LESSONS LEARNED: Delivering TA requires a sustained and coordinated effort supported by strong leadership and governance, continuous QI, engaged interdisciplinary teams and partnering within and beyond the healthcare sector.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".