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 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".