Role for PROMs Data to Support Quality Improvement across the Healthcare System: An Informed Exchange with Senior Health System Leaders
Bibliographic record
Abstract
The Institute for Healthcare Improvement's Triple Aim (initiated in 2007) and several high-level Canadian studies have made general calls to improve health system performance. Managers and administrators have been urged to tackle the challenges of quality improvement and cost control. In the lead essay, McGrail et al. point to patient-reported outcome measures (PROMs) as something worth doing, and this has been welcomed as an appropriate response to long-standing calls for action. A recent gathering of senior health system leaders explored the prospect of routinely collecting PROMs data to drive quality improvement. The symposium, Measures of Health Outcomes to Improve Performance, Value and Productivity, was held in Victoria, British Columbia, on December 9, 2010. The symposium delegates considered the challenges and issues involved in moving forward with PROMs, looking closely at the potential for enhancing the quality of data resources available for managing our healthcare system. Senior leaders and administrators from the publicly funded healthcare systems of British Columbia and western and northern Canada participated in a frank discussion of challenges and requirements for moving forward with a PROMs initiative.
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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 | 0.050 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.083 | 0.101 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".