Major reductions in unnecessary aspartate aminotransferase and blood urea nitrogen tests with a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND/CONTEXT: Unnecessary laboratory testing leads to considerable healthcare costs. Aspartate aminotransferase (AST), commonly ordered with alanine aminotransferase (ALT) and blood urea nitrogen (BUN), commonly ordered with creatinine (Cr), often add little value to patient management at significant cost. We undertook a choosing wisely based quality improvement initiative to reduce the frequency of testing. OBJECTIVES: To reduce the ratio of AST/ALT and BUN/Cr to less than 5% for all inpatient and outpatient test orders. MEASURES: Absolute number and ratio of AST/ALT and BUN/Cr; AST, ALT, BUN and Cr tests per 100 hospital days; projected annualised cost savings and monthly acute inpatient bed days. IMPROVEMENTS: We created guidelines for appropriate indications of AST and BUN testing, provided education with audit and feedback and removed AST and BUN from institutional order sets. IMPACT/RESULTS: The ratios of AST/ALT and BUN/Cr decreased significantly over the study period (0.37 to 0.14, 0.57 to 0.14, respectively), although the goal of 0.05 was not achieved due to a delay in adopting the choosing wisely strategies during the study time period by some inpatient units. The number of tests per 100 hospital days decreased from 20 to 7 AST (95% CI 19 to 20.5, 5.6 to 8.7, p<0.001) and from 72 to 17 BUN (95% CI 70 to 73.4, 16.6 to 22.9, p<0.001). The initiative resulted in a projected annualised cost savings of C$221 749. DISCUSSION: A significant decrease in the AST/ALT and BUN/Cr ratios can be achieved with a multimodal approach and will result in substantial healthcare savings.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".