Measuring indicators of health system performance for palliative and end-of-life care using health administrative data: a scoping review
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> A plethora of performance measurement indicators for palliative and end-of-life care currently exist in the literature. This often leads to confusion, inconsistency and redundancy in efforts by health systems to understand what should be measured and how. The objective of this study was to conduct a scoping review to provide an inventory of performance measurement indicators that can be measured using population-level health administrative data, and to summarize key concepts for measurement proposed in the literature. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> A scoping review using MEDLINE and EMBASE, as well as grey literature was conducted. Articles were included if they described performance or quality indicators of palliative and end-of-life care at the population-level using routinely-collected administrative data. Details on the indicator such as name, description, numerator, and denominator were charted. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> A total of 339 indicators were extracted. These indicators were classified into nine health care sectors and one cross-sector category. Extracted indicators emphasized key measurement themes such as health utilization and cost and excessive, unnecessary, and aggressive care particularly close to the end-of-life. Many indicators were often measured using the same constructs, but with different specifications, such as varying time periods used to ascribe for <ns4:italic>end-of-life</ns4:italic> care, and varying patient populations. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Future work is needed to achieve consensus ‘best’ definitions of these indicators as well as a universal performance measurement framework, similar to other ongoing efforts in population health. Efforts to monitor palliative and end-of-life care can use this inventory of indicators to select appropriate indicators to measure health system performance. </ns4:p>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".