Impact of specialist palliative care on utilization of healthcare and social services at the end-of-life: a nationwide register-based cohort study
Bibliographic record
Abstract
Non-malignant diseases cause 60% of non-communicable diseases requiring palliative care, yet specialist palliative care services primarily focus on cancer. We investigated end-of-life healthcare and social services utilization among cancer and non-malignant patients, and, secondarily, access to specialist palliative care and its effect on services utilization. This retrospective, nationwide register-based study included all adults (n = 38 540) who died from non-communicable life-limiting diseases in Finland in 2019, categorized into neurodegenerative (31%), other non-malignant (36%), and cancer (33%) groups. Hospital was the most common place of death (61%). Healthcare utilization substantially increased during the final weeks of life in all groups but remained highest in cancer patients. Social services utilization was highest in neurodegenerative diseases. Specialist palliative care contact was significantly (P < .001) higher in cancer (30.1%) compared to neurodegenerative (10.9%) and other non-malignant (7%) diseases. Early (>30 days before death) compared to late/no specialist palliative care contact significantly reduced emergency care contacts (47.8% vs. 52.2%) and hospitalizations in secondary hospitals (24.7% vs. 33.7%), and increased specialist palliative care ward (15.5% vs. 1.5%) and hospital-at-home (36.8% vs. 3.4%) utilization during the final month (P < .001). Healthcare utilization was high in all disease groups, highest among cancer patients. Hospital was the most common place of death. Specialist palliative care contact was rare in non-malignant diseases. Early contact with specialist palliative care associated with lower emergency care utilization and secondary hospital inpatient care during the last month of life. These results highlight the necessity for timely equitable specialist palliative care services for all.
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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 | 0.003 | 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".