Community-based specialist palliative care teams and health system costs at end of life: a retrospective matched cohort study
Bibliographic record
Abstract
BACKGROUND: Access to community-based specialist palliative care teams has been shown to improve patients' quality of life; however, the impact on health system expenditures is unclear. This study aimed to determine whether exposure to these teams reduces health system costs compared with usual care. METHODS: We conducted a retrospective matched cohort study in Ontario, Canada, using linked administrative data. Decedents treated by 1 of 11 community-based specialist palliative care teams in 2009/10 and 2010/11 (the exposed group) were propensity score matched (comorbidity, extent of home care, etc.) 1 to 1 to similar decedents in usual care (the unexposed group). The teams are comprised of a core group of specialized physicians, nurses and other providers; their role is to manage symptoms around the clock, provide education and coordinate care. Our primary outcome was the overall difference in health system costs (among 5 health care sectors) between all matched pairs of exposed versus unexposed patients in the last 30 days of life. RESULTS: The total cohort of decedents included 3109 matched pairs. Among matched pairs, the mean health system cost difference was $512 (95% confidence interval [CI] -$641 to -$383) lower in the last 30 days among exposed than among unexposed patients. In the last 30 days, the mean home care costs of the exposed group were $189 higher (95% CI -$151 to $227) than those of the unexposed group, but their mean hospital costs were $733 lower (95% CI -$950 to -$516). INTERPRETATION: Our study suggests that health system costs are lower for patients who have access to community-based specialist teams than for those who receive usual care alone, largely because of decreased hospital costs. Ensuring access to in-home palliative care support, as provided by these teams, is an efficacious strategy for reducing health care expenditures at the end of life.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".