Financial Implications of the Continuity of Primary Care
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the financial implications of the continuity of care, for patients with high care needs, by examining the cost of government-funded health care services in British Columbia, Canada. METHODS: Using British Columbia Ministry of Health administrative databases for fiscal year 2010-2011 and generalized linear models, we estimated cost ratios for 10 cost-related predictor variables, including patients' attachment to the practice. Patients were selected and divided into groups on the basis of their Resource Utilization Band (RUB) and placement in provincial registries for 8 chronic conditions (1,619,941 patients). The final dataset included all high- and very-high-care-needs patients in British Columbia (ie, RUB categories 4 and 5) in 1 or more of the 8 registries who met the screening criteria (222,779 patients). RESULTS: Of the 10 predictors, across 8 medical conditions and both RUBs, patients' attachment to the practice had the strongest relationship to costs (correlations = -0.168 to -0.322). Higher attachment was associated with lower costs. Extrapolation of the findings indicated that an increase of 5% in the overall attachment level, for the selected high-care-needs patients, could have resulted in an estimated cost avoidance of $142 million Canadian for fiscal year 2010-2011. CONCLUSIONS: Continuity of care, defined as a patient's attachment to his/her primary care practice, can reduce health care costs over time and across chronic conditions. Health care policy makers may wish to consider creating opportunities for primary care physicians to increase the attachment that their high-care-needs patients have to their practices.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".