Opportunities to improve the impact of two national clinical audit programmes: a theory-guided analysis
Bibliographic record
Abstract
BACKGROUND: Audit and feedback is widely used in healthcare improvement, with evidence of modest yet potentially important effects upon professional practice. There are approximately 60 national clinical audit programmes in the UK. These programmes often develop and adapt new ways of delivering feedback to optimise impacts on clinical practice. Two such programmes, the National Diabetes Audit (NDA) and the Trauma Audit Research Network (TARN), recently introduced changes to their delivery of feedback. We assessed the extent to which the design of these audit programmes and their recent changes were consistent with best practice according to the Clinical Performance Feedback Intervention Theory (CP-FIT). This comprehensive framework specifies how variables related to the feedback itself, the recipient, and the context operate via explanatory mechanisms to influence feedback success. METHODS: We interviewed 19 individuals with interests in audit and feedback, including researchers, audit managers, healthcare staff, and patient and public representatives. This range of expert perspectives enabled a detailed exploration of feedback from the audit programmes. We structured interviews around the CP-FIT feedback cycle and its component processes (e.g. Data collection and analysis, Interaction). Our rapid analytic approach explored the extent to which both audits applied features consistent with CP-FIT. RESULTS: Changes introduced by the audit programmes were consistent with CP-FIT. Specifically, the NDA's increased frequency of feedback augmented existing strengths, such as automated processes (CP-FIT component: Data collection and analysis) and being a credible source of feedback (Acceptance). TARN's new analytic tool allowed greater interactivity, enabling recipients to interrogate their data (Verification; Acceptance). We also identified scope for improvement in feedback cycles, such as targeting of feedback recipients (Interaction) and feedback complexity (Perception) for the NDA and specifying recommendations (Intention) and demonstrating impact (Clinical performance improvement) for TARN. CONCLUSIONS: The changes made by the two audit programmes appear consistent with suggested best practice, making clinical improvement more likely. However, observed weaknesses in the feedback cycle may limit the benefits of these changes. Applying CP-FIT via a rapid analysis approach helps identify strengths and remediable weaknesses in the design of audit programmes that can be shared with them in a timely manner.
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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 | 0.112 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".