The impact of patient enrolment in primary care on continuity and quality of care around the world, 2014–2024, and lessons for Australia: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To identify publications examining the enablers of and barriers to patient enrolment in primary care and its impact on continuity and quality of care; to assess the likely effectiveness of voluntary patient enrolment (MyMedicare) in Australia with regard to improving continuity of care and supporting other health care reforms. STUDY DESIGN: Scoping review of peer-reviewed journal article published in English during 1 January 2014 - 12 July 2024 that evaluated primary care enrolment models, including patient enrolment enablers and barriers. DATA SOURCES: PubMed, Cochrane Database of Systematic Reviews, Embase, CINAHL (Cumulated Index in Nursing and Allied Health Literature), PsycINFO, PAIS (Public Affairs Information Service), Web of Science, Scopus. The bibliographies of included articles were checked for further relevant publications. DATA SYNTHESIS: The database searches and bibliography checks identified 508 potentially relevant articles; we reviewed the full text of 66 articles after title and abstract screening, of which 24 publications met our inclusion criteria. Twenty-two of the included studies were undertaken in fifteen countries, including eleven in Canada, four in Australia, and two each in the United Kingdom and New Zealand; one publication compared schemes in twelve countries, one was a rapid review. The characteristics of patient enrolment models differ greatly between countries in both form and implementation, including the mandatory and voluntary components. We found little evidence that enrolment improved continuity of care. However, existing patient engagement with usual general practitioners was high among participants in many studies, and some studies involved patients who may already have had high levels of continuity of care. There is evidence that enrolment can support primary care reforms, including preventive care and the management of chronic conditions, and that other reforms, such as incentives and increased access to services can affect the enrolment of patients and practices. People in marginalised groups or with complex care needs are less likely to enrol with practices or practitioners. CONCLUSIONS: The Australian voluntary patient enrolment scheme should be continuously evaluated to assess levels of engagement by patients and general practices, drawing on the experiences of other countries in which similar schemes operate. Further assessment of overseas enrolment systems could identify reasons for the different experiences reported, as well as enablers of and barriers to successful implementation and better health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".