Audit and feedback to reduce inappropriate Full Blood Count pathology testing
Bibliographic record
Abstract
Objective: This study was designed to reduce inappropriate Full Blood Count (FBC) pathology testing on specific hospital wards. It was hoped that by auditing requested Full Blood Counts, the researcher would be able to determine and benchmark appropriateness before conducting feedback interventions to promote appropriate pathology test ordering.Methods: To reduce inappropriate Full Blood Count pathology test ordering, the researcher audited patient notes and pathology test request forms in June 2015 before conducting audit and feedback interventions in July, August, and September 2015 on the hospital ward areas. The feedback intervention consisted of auditing patient notes, pathology request forms, and the local pathology clinical integration systems to determine Full Blood Count appropriateness. This data was then communicated to the attending doctor and requesting doctor during feedback sessions. To conceptualize appropriate pathology test ordering, the researchers highlighted the “Framework for analysis of test ordering” during scheduled feedback sessions. It was hypothesized that audit and feedback would decrease the amount of inappropriate Full Blood Counts ordered.Results: After receiving the audit and feedback intervention, clinicians were more likely not to order inappropriate Full Blood Counts (64.60% vs. 23.40%), specifically providing adequate clinical reasoning for the test, t(4.6706) = 0.0429, p = .05.Conclusions: This study found that audit and feedback sessions significantly improved appropriate pathology test ordering and the clinical reasoning associated with Full Blood Counts.
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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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".