Taking Charge of High-Risk and High-Cost Patients in the Public Healthcare System
Bibliographic record
Abstract
Many healthcare systems are struggling with the issue of providing high-quality care to high-needs patients at lower costs. Our comments in this paper draw on insights that we have gained from the development and implementation of integrated models of care in Québec. This experience highlights the importance of developing a clear clinical approach to the delivery and coordination of care and to support providers in new roles. Our second insight is that system-level policy guidelines may help to focus the attention of organizations and providers on key priorities, but they need to take into account differing needs in various contexts. Third, a crucial factor for success over the longer term is the ability of local networks to reshape the allocation and use of resources to bring about change in day-to-day operations. We conclude by highlighting key characteristics of high-performing health systems and with the final observation that politicians and policymakers need to allow enough time to harness the full benefit of change initiatives.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.035 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".