Repurposing the Ordering of Routine Laboratory Tests in Hospitalised Medical Patients (RePORT): results of a cluster randomised stepped-wedge quality improvement study
Bibliographic record
Abstract
BACKGROUND: Low-value use of laboratory tests is a global challenge. Our objective was to evaluate an intervention bundle to reduce repetitive use of routine laboratory testing in hospitalised patients. METHODS: We used a stepped-wedge design to implement an intervention bundle across eight medical units. Our intervention included educational tools and social comparison reports followed by peer-facilitated report discussion sessions. The study spanned October 2020-June 2021, divided into control, feasibility testing, intervention and a follow-up period. The primary outcomes were the number and costs of routine laboratory tests ordered per patient-day. We used generalised linear mixed models, and analyses were by intention to treat. RESULTS: We included a total of 125 854 patient-days. Patient groups were similar in age, sex, Charlson Comorbidity Index and length of stay during the control, intervention and follow-up periods. From the control to the follow-up period, there was a 14% (incidence rate ratio (IRR)=0.86, 95% CI 0.79 to 0.92) overall reduction in ordering of routine tests with the intervention, along with a 14% (β coefficient=-0.14, 95% CI -0.07 to -0.21) reduction in costs of routine testing. This amounted to a total cost savings of $C1.15 per patient-day. There was also a 15% (IRR=0.85, 95% CI 0.79, 0.92) reduction in ordering of all common tests with the intervention and a 20% (IRR=1.20, 95% CI 1.10 to 1.30) increase in routine test-free patient-days. No worsening was noted in patient safety endpoints with the intervention. CONCLUSIONS: A multifaceted intervention bundle using education and facilitated multilevel social comparison was associated with a safe and effective reduction in use of routine daily laboratory testing in hospitals. Further research is needed to understand how system-level interventions may increase this effect and which intervention elements are necessary to sustain results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.123 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".