Implementing Practice Management Strategies to Improve Patient Care: The EPIC Project
Bibliographic record
Abstract
Healthcare gaps, the difference between usual care and best care, are evident in Canada, particularly with respect to our aging, ailing population. Primary care practitioners are challenged to identify, prevent and close care gaps in their practice environment given the competing demands of informed, litigious patients with complex medical needs, ever-evolving scientific evidence with new treatment recommendations across many disciplines and an enhanced emphasis on quality and accountability in healthcare. Patient-centred health and disease management partnerships using measurement, feedback and communication of practice patterns and outcomes have been shown to narrow care gaps. Practice management strategies such as the use of patient registries and recall systems have also been used to help practitioners better understand, follow and proactively manage populations of patients in their practice. The Enhancing Practice to Improve Care project was initiated to determine the impact of a patient-centred health and disease management partnership using practice management strategies to improve patient care and outcomes for patients with chronic kidney disease (CKD). Forty-four general practices from four regions of British Columbia participated and, indeed, demonstrated that care and outcomes for patients with CKD could be improved via the implementation of practice management strategies in a patient-centred partnership measurement model of health and disease management.
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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 | 0.091 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".