Achieving higher performing primary care through patient registration: A review of twelve high-income countries
Bibliographic record
Abstract
BACKGROUND: Patient registration with a primary care providers supports continuity in the patient-provider relationship. This paper develops a framework for analysing the characteristics of patient registration across countries; applies this framework to a selection of countries; and identifies challenges and ongoing reform efforts. METHODS: 12 jurisdictions (Denmark, France, Germany, Ireland, Israel, Italy, Netherlands, Norway, Ontario [Canada], Sweden, Switzerland, United Kingdom) were selected for analysis. Information was collected by national researchers who reviewed relevant literature and policy documents to report on the establishment and evolution of patient registration, the requirements and benefits for patients, providers and payers, and its connection to primary care reforms. RESULTS: Patient registration emerged as part of major macro-level health reforms linked to the introduction of universal health coverage. Recent reforms introduced registration with the aim of improving quality through better coordination and efficiency through reductions in unnecessary referrals. Patient registration is mandatory only in three countries. Several countries achieve high levels of registration by using strong incentives for patients and physicians (capitation payments). CONCLUSION: Patient registration means different things in different countries and policy-makers and researchers need to take into consideration: the history and characteristics of the registration system; the use of incentives for patients and providers; and the potential for more explicit use of patient-provider agreements as a policy to achieve more timely, appropriate, continuous and integrated care.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".