Physician home visits in Ontario: a cross-sectional analysis of patient characteristics and postvisit use of health care services
Bibliographic record
Abstract
BACKGROUND: It is unknown how much of current physician home visit volume is driven by low-complexity or low-continuity visits. Our objectives were to measure physician home visit volumes and costs in Ontario from 2005/06 to 2018/19, and to compare patient characteristics and postvisit use of health care services across home visit types. METHODS: This was a retrospective cross-sectional study using health administrative data. We examined annual physician home visit volumes and costs from 2005/06 to 2018/19 in Ontario, and characteristics and postvisit use of health care services of residents who received at least 1 home visit from any physician in 2014/15 to 2018/19. We categorized home visits as palliative, provided to a patient who also received home care services or "other," and compared characteristics and outcomes between groups. RESULTS: A total of 4 418 334 physician home visits were performed between 2005/06 and 2018/19. More than half (2 256 667 [51.1%]) were classified as "other" and accounted for 39.1% ($22 million) of total annual physician billing costs. From 2014/15 to 2018/19, of the 413 057 home visit patients, 240 933 (58.3%) were adults aged 65 or more, and 323 283 (78.3%) lived in large urban areas. Compared to the palliative care and home care groups, the "other" group was younger, had fewer comorbidities, and had lower rates of emergency department visits and hospital admissions in the 30 days after the visit. INTERPRETATION: About half of physician home visits in 2014/15 to 2018/19 were to patients who were receiving neither palliative care nor home care, a group that was younger and healthier, and had low use of health care services after the visit. There is an opportunity to refine policy tools to target patients most likely to benefit from physician home visits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".