Quality Indicators of End-of-Life Care in Patients With Cancer: What Rate Is Right?
Bibliographic record
Abstract
PURPOSE: To develop data-driven and achievable benchmark rates for end-of-life quality indicators using administrative data from four provinces in Canada. METHODS: Indicators of end-of-life care were defined and measured using linked administrative data for 33 health regions across British Columbia, Alberta, Ontario, and Nova Scotia. These were emergency department use, intensive care unit admission, physician house calls and home care visits before death, and death in hospital. An empiric benchmark was defined using indicator rates from the top-ranked regions to include the top decile of patients overall. Funnel plots were used to graph each region's age- and sex-adjusted indicator rates along with the overall rate and 95% confidence limits. RESULTS: Rates varied approximately two- to four-fold across the regions, with physician house calls showing the greatest variation. Benchmark rates based on the top decile performers were emergency department use, 34%; intensive care unit admission, 2%; physician house calls, 34%; home care visits, 63%; and death in hospital, 38%. With the exception of intensive care unit admission, funnel plots demonstrated that overall indicator rates and their confidence limits were uniformly worse than benchmarks even after adjusting for age and sex. Few regions met the benchmark rates. CONCLUSION: There is significant variation in end-of-life quality indicators across regions in four provinces in Canada. Using this study's methods-deriving empiric benchmarks and funnel plots-regions can determine their relative performance with greater context that facilitates priority setting and resource deployment. Applying this study's methods can support quality improvement by decreasing variation and striving for a target.
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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 | 0.065 | 0.245 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".