Refocusing quality measurement to best support quality improvement: local ownership of quality measurement by clinicians: Table 1
Bibliographic record
Abstract
Recent years have seen unprecedented efforts to measure healthcare quality and to link such measurement to improved care delivery. The methodological and pragmatic complexities of these efforts have led to major debates: which ‘dimensions’ of quality to measure; whether to focus on processes or outcomes; which outcomes to prioritise—traditional clinical outcomes or more patient-centred ones; and, perhaps most important, how to link measurement to action through policy, professional and management levers.1 A variety of quality measurement schemes exist in many countries, including confidential reporting directed at healthcare organisations, public reporting of performance, policies tying performance to funding, such as ‘pay for performance’.2 3 Further, in some countries, professional training and/or licensing and revalidation processes for doctors include skills to measure and improve quality as core competencies.4–6 Moreover, public and governmental expectations for quality measurement have not just continued to rise but have expanded to include interest in long-term conditions, rather than the historical focus on short-term outcomes after surgery or hospitalisation for acute medical conditions. The question thus arises: what approach to measuring and reporting quality will best equip health systems to address these needs, especially given ubiquitous fiscal constraints. In this commentary, we first outline five general categories of problems that have beset quality measurement efforts to date. ### Prioritising one type of measure Much debate has focused on whether processes or outcomes constitute the ‘best’ quality measures. This debate has a long history7–9 but represents a false choice. Each element of Donabedian's triad of structure, process and outcome has both advantages and disadvantages,10 with no single category providing the best performance measurement across all settings and circumstances (see table 1). In healthcare, as in other industries, the players who achieve the best outcome pay the greatest attention to implementing reliable, effective and efficient processes of care and putting in …
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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 | 0.048 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".