1033 Evaluation of the edmonton zone triple aim initiative: building and implementing a measurement system for improvement with complex, vulnerable clients
Bibliographic record
Abstract
Background The Alberta Health Services, Edmonton Zone Triple Aim Initiative launched in January, 2013. The seven participating clinical teams target complex, vulnerable patients in inner-city Edmonton, Alberta, Canada. The Initiative follows the Institute for Healthcare Improvement’s Better Health Lower Cost Road Map and pursues the Quadruple Aim. Grant funding was provided by MERCK Canada and Alberta Innovates. Objectives The objectives of the evaluation were: 1) Determine the extent to which the Aims of the Initiative have been met; and 2) Build a measurement system to monitor the performance of the team quality improvement efforts. Methods Over 40 providers and 445 patients participated. Data collection include: administrative data; patient surveys; and patient and staff interviews. Analyses of system level data (e.g., emergency department visits; inpatient stays; and physician continuity) included both descriptive and statistical modelling approaches from a pre/post comparison perspective. Results Across all teams there were strong improvements in self-reported experience for both patients and providers. Some teams demonstrated reduced acute care utilisation and cost, and higher continuity with a family physician. Better outcomes were linked with teams delivering on more elements of the Managing Complex Change model: having a vision, skills, incentives, adequate resources and an action plan. Conclusions The evaluation demonstrates that the teams have improved care for their patients. Lessons learned from this evaluation will be critical for the Initiative moving forward, and also others working with similar populations. Recommendations from the evaluation for implementing system-level improvement initiatives will be discussed, as well as recommendation for implementing measurement systems with complex patients.
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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 | 0.130 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".