Advancing Primary Care Through Alternative Payment Models: Lessons from the United States & Canada
Bibliographic record
Abstract
The United States and Canada share high costs, poor health system performance, and challenges to the transformation of primary care, in part due to the limitations of their fee-for-service payment models. Rapidly advancing alternative payment models (APMs) in both countries promise better support for the essential tasks of primary care. These include interdisciplinary teams, care coordination, self-management support, and ongoing communication. This article reviews learnings from a 2017 binational symposium of 150 experts in policy and research that included a discussion of ongoing APM experiments in the United States and Canada. Discussions ranged from APM challenges and successes to their real and potential impact on primary care. The gathering yielded many lessons for policy makers, payors, researchers, and providers. Experts lauded recent APM experimentation on both sides of the border, while cautioning against the risk of "pilotitis," or developing, implementing, and evaluating new payment models without plan or ability scale them into broader practice. Discussants highlighted the power of "learning at scale," highlighting large-scale primary care payment innovations launched by the US Center for Medicare and Medicaid Innovation since 2011, and called for a similar national center to drive innovation across provincial health systems in Canada. There was general consensus that altering payment models alone, absent incentives for innovation and continuous learning as well as increased proportional spending on primary care overall, would not correct health system deficiencies. Participants lamented the absence of more robust evaluation of APM successes and shortcomings, as well as more rapid release of results to accelerate further innovation. They also highlighted the importance of APMs that include flexible and upfront payments for primary care innovations, and which reward measuring and achieving global rather than intermediate outcomes, to achieve utilization goals and patient and provider satisfaction.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| 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".