Using administrative data to measure the extent to which practitioners work together: "interconnected" care is common in a large cohort of family physicians.
Bibliographic record
Abstract
BACKGROUND: Health care practitioners in jurisdictions around the world are encouraged to work in groups. The extent to which they actually do so, however, is not often measured. The purpose of this paper is to demonstrate the potential for administrative data to measure how practitioners are interconnected through their care of patients. Our example examined the interconnected care provided by family physicians. METHODS: We defined a physician as being "interconnected" with another physician if these 2 physicians provided at least 1% of their clinic visits over a 2-year period to the same patients. We examined a cohort of 2945 primary care physicians in 309 Family Health Networks and Family Health Groups in Ontario, Canada, in 2005/06. In total, 9.3 million physician visits for 2.1 million patients were studied. For each group practice we calculated the number of interconnected physicians. RESULTS: Physicians had, on average, 2.2 interconnected physician partners (median=1; 25th and 75th percentile: 0, 3). Physicians saw mainly their own listed patients, and 7.9% (median=5.9%; 25th and 75th percentile: 2.4%, 11.6%) of their visits were provided to patients of their interconnected partners. The number of interconnected physicians was higher in group practices that had more physicians, but levelled to 2.5 interconnected physicians in practices with 8 or 9 physicians. INTERPRETATION: Routinely collected administrative data can be used to examine how health care is organized and delivered in groups or networks of practitioners. This study's concept of interconnected care provided by primary care physicians within groups could be expanded to include other practitioners and, indeed, entire health care systems using more complex network analysis methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".