Impact of physician-based palliative care delivery models on health care utilization outcomes: A population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Increasing involvement of palliative care generalists may improve access to palliative care. It is unknown, however, if their involvement with and without palliative care specialists are associated with different outcomes. AIM: To describe physician-based models of palliative care and their association with healthcare utilization outcomes including: emergency department visits, acute hospitalizations and intensive care unit (ICU) admissions in last 30 days of life; and, place of death. DESIGN: Population-based retrospective cohort study using linked health administrative data. We used descriptive statistics to compare outcomes across three models (generalist-only palliative care; consultation palliative care, comprising of both generalist and specialist care; and specialist-only palliative care) and conducted a logistic regression for community death. SETTING/PARTICIPANTS: All adults aged 18-105 who died in Ontario, Canada between April 1, 2012 and March 31, 2017. RESULTS: Of the 231,047 decedents who received palliative services, 40.3% received generalist, 32.3% consultation and 27.4% specialist palliative care. Across models, we noted minimal to modest variation for decedents with at least one emergency department visit (50%-59%), acute hospitalization (64%-69%) or ICU admission (7%-17%), as well as community death (36%-40%). In our adjusted analysis, receipt of a physician home visit was a stronger predictor for increased likelihood of community death (odds ratio 9.6, 95% confidence interval 9.4-9.8) than palliative care model (generalist vs consultation palliative care 2.0, 1.9-2.0). CONCLUSION: The generalist palliative care model achieved similar healthcare utilization outcomes as consultation and specialist models. Including a physician home visit component in each model may promote community death.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".