Qualitative study of international key informants’ perspectives on the current and future state of healthcare quality measurement and feedback
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to explore the current and future state of quality measurement and feedback and identify factors influencing measurement feedback systems, including the barriers and enablers to their effective design, implementation, use and translation into quality improvement. DESIGN: This qualitative study used semistructured interviews with key informants. A deductive framework analysis was conducted to code transcripts to the Theoretical Domains Framework (TDF). An inductive analysis was used to produce subthemes and belief statements within each TDF domain. SETTING: All interviews were conducted by videoconference and audio-recorded. PARTICIPANTS: Key informants were purposively sampled experts in quality measurement and feedback, including clinical (n=5), government (n=5), research (n=4) and health service leaders (n=3) from Australia (n=7), the USA (n=4), the UK (n=2), Canada (n=2) and Sweden (n=2). RESULTS: . The most populous subthemes included 'quality improvement culture', 'financial and human resource support' and 'patient-centred measurement'. There were minimal conflicting beliefs outside of 'data quality and completeness'. Conflicting beliefs in these subthemes were predominantly between government and clinical leaders. CONCLUSIONS: Multiple factors were found to influence measurement feedback systems and future considerations are presented within this manuscript. The barriers and enablers that impact these systems are complex. While there are some clear modifiable factors in the design of measurement and feedback processes, influential factors described by key informants were largely socioenvironmental. Evidence-based design and implementation, coupled with a deeper understanding of the implementation context, may lead to enhanced quality measurement feedback systems and ultimately improved care delivery and patient outcomes.
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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 | 0.041 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".