New Evidence on End-of-Life Hospital Utilization for Enhanced Health Policy and Services Planning
Bibliographic record
Abstract
BACKGROUND: Long-standing concern exists over hospital use by people near or at the end of life (EOL) related to the appropriateness, quality, and cost of care in hospital. It is widely believed that most people die in hospital after an escalation in hospital use over the last year of life. As most deaths in high-income countries are not sudden or unexpected, opportunities exist for planning compassionate, effective, and evidence-based EOL care. OBJECTIVE: Gain current population-based evidence for EOL health policy and services planning. DESIGN: Retrospective study of population-based hospital utilization data. SETTING/SUBJECTS: All hospital patients in every Canadian province and territory except Quebec. All decedents with hospital separations in 2014-2015. MEASURES: Descriptive-comparative and logical regression analysis tests. RESULTS: In 2014-2015, 3.5% of hospital episodes ended in death and 43.7% of all deaths in Canada (excluding Quebec) took place in hospital. 95.2% of those dying in hospital were only admitted once or twice during their last 365 days of life. 3.6% of those dying in hospital had been living in the community and receiving publicly funded home care before the hospital admission that ended in death, while 67.0% had been living at home without home care. 79.0% of hospital deaths followed an unplanned admission through the emergency room, with 70.5% arriving by ambulance. The hospital care provided in the last stay was largely noninterventionist. CONCLUSIONS: These findings reveal the need for a major reconceptualization of death, dying, and EOL care to ensure sufficient capacity of palliative home care and other services to support dying people and prevent the health and family caregiver crises that lead to hospital-based EOL care and 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 | 0.053 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.001 |
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".