The Impact of Measuring Patient-Reported Outcome Measures on Quality of and Access to Palliative Care
Bibliographic record
Abstract
BACKGROUND: Measuring performance for palliative care is complex as care is delivered in many sites, over time and jointly to the patient and family. Measures of structural processes do not necessarily capture aspects that are important to patients and families nor reflect holistic multidisciplinary outcomes of care. This article focuses on the question as to whether measurement of patient-reported outcome measures improves the outcomes of quality and access to palliative care. OBJECTIVES: To review the international evidence that measurement of indicators of desired outcomes improves the quality of and access to palliative care, in order to apply them to the Canadian context. DESIGN: Rapid review. SETTING: Canadian context. FINDINGS: This review identified six systematic reviews and forty-seven studies that describe largely national efforts to arrive at a consensus as to what needs to be measured to assess quality of palliative care. Patient-reported outcome measures (PROMs) are becoming more prevalent, with emerging evidence to suggest that their measurement improves outcomes that are important to patients. Several Canadian initiatives are in place, including the Canadian Partnership Against Cancer's efforts, in conjunction with other partners, to develop common quality measures. Results from Australia's Palliative Care Outcomes Collaborative demonstrate that patient-centered improvements in palliative care can be measured by using patient-reported outcomes derived at the point of care and delivered nationally. CONCLUSIONS: Measurement of quality palliative and end-of-life care is very complex. It requires that both administrative data and PROMs be assessed to reflect outcomes that are important to patients and families. Australia's national initiative is a promising exemplar for continued work in this area.
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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 | 0.326 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".