A Realist Evaluation of a 72-Hour Readmission Audit and Feedback (A&F) Intervention in Emergency Medicine
Bibliographic record
Abstract
Introduction Audit and feedback (A&F) interventions are intended to increase accountability and improve the quality of care; however, their impact can vary significantly. As performance feedback is implemented in healthcare, there is a growing need to determine how users interact with the data and how systems can achieve more consistent performance outcomes. This study aimed to understand the contexts, mechanisms, and outcomes of an emergency department 72-hour readmission A&F intervention. Methods Semi-structured interviews with key stakeholders were conducted and analyzed using thematic and template analysis techniques specifically aimed at identifying context, mechanism, and outcome configurations. Results Seventeen (17) physician interviews were conducted. We identified five outcomes of the intervention and the contexts and mechanisms contributing to them. Importantly, we identified that this A&F strategy could potentially have positive (improved follow-up of cases, improved discharge communication) and negative impacts (increased physician anxiety, potentially increased resource use) on physicians and departmental efficiency. Conclusion The 72-hour readmission alert A&F intervention generates a number of distinct outcome patterns that result from a variety of mechanisms acting in different contexts. Knowledge of these context-mechanism-outcome relationships may help implementers design and tailor performance feedback strategies.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".