Quality indicators of end-of-life cancer care: What rate is right?
Bibliographic record
Abstract
57 Background: Many publications use administrative health care data to describe quality of care indicators at the end of life (EOL). However, very little is available to help decide on optimal rates for these indicators. The purpose of this abstract is to develop data-driven and achievable benchmark rates for EOL quality indicators using administrative data from 4 provinces in Canada. Methods: Five quality indicators of EOL care were defined and measured using linked administrative data for each of the 33 regions across British Columbia, Alberta, Ontario and Nova Scotia. These were: emergency department (ED) use, intensive care unit (ICU) admission, physician house calls (MD) and nursing visits at home (RN) prior to death, and death in hospital (DH). First, an empiric benchmark was defined by determining indicator rates among the top ranked regions to include the top decile of patients overall. Second, funnel plots were used to graph the age and sex adjusted indicator rates for each region along with the overall average value and 95% confidence limits (CL) that accounted for region size. Results: There was significant variation in rates for each indicator among the regions. Minimum and maximum rates for ED, ICU, RN, MD and DH varied approximately 2 to 4 fold across the regions with MD showing the greatest variation. Benchmark rates based on the top decile performers were: ED 34%, ICU 2%, MD 34%, RN 63%, DH 38%. With the exception of ICU, funnel plots demonstrated that mean indicator rates and their 95% CL were uniformly worse than these benchmarks even after adjusting for age and sex. Additionally, few regions met the benchmark rates. Conclusions: There is significant variation in EOL quality indicators across regions in 4 provinces in Canada. The combination of these two methods allows each region to determine its performance relative to both a benchmark and the overall average. As a result, each region is then able to gauge their performance with greater context which facilitates priority setting and resource deployment. These two methods demonstrate how decreasing variation and striving for a target can drive quality improvement. Deriving benchmark values from ‘real world’ data offers the advantage of realistically achievable targets.
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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 | 0.125 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".