Lipstick on a pig: Qualitative understanding of the efforts to redesign electronic audit and feedback reports for primary care. (Preprint)
Bibliographic record
Abstract
BACKGROUND In Ontario, Canada, a government agency known as Ontario Health is responsible for making audit and feedback reports available to all family physicians. The confidential report provides summary data on three key areas of practice: safe prescribing, cancer screening, and diabetes management. OBJECTIVE This report was redesigned to improve its usability and the objective of this study was to explore how the redesign was perceived. METHODS We conducted qualitative semi-structured interviews with family physicians who had experience with both versions of the report, recruited through purposeful and snowball sampling. We analysed the transcripts following an emergent and iterative approach. RESULTS Saturation was reached after 17 family physicians participated. Two key themes emerged as factors that impacted the perceived usability of the report: (1) alignment between report and recipients’ expectations and (2) capacity to engage in quality improvement. Family physicians expected the report and its quality indicators to reflect best practice, to be valid and accurate. They also expected the report to offer feedback on clinical activities they perceived were within their control to change. Further, family physicians expected the goal of the report to be aligned with their perspective on feasible quality improvement activities. Most of these expectations were not met, limiting the perceived usability of the report. The capacity to engage with audit and feedback was hindered by several organizational and physician-level barriers including the lack of fit with existing workflow, competing priorities, time constraints, and insufficient skills for bridging the gaps between their data and the corresponding desired actions. CONCLUSIONS Overall, the redesigned feedback report was not perceived as highly usable, given the misalignement between report and family physicians’ expectations as well as limited capacity to engage with the report. Consequently, the potential impact on clinical practice may be limited. Co-interventions to address the barriers of using audit and feedback report, as well as creating space for bridging together audit and feedback designers and recipients are avenues to consider for improving usability and effectiveness such quality improvement initiatives.
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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 | 0.054 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".