Impact of an education and multilevel social comparison–based intervention bundle on use of routine blood tests in hospitalised patients at an academic tertiary care hospital: a controlled pre-intervention post-intervention study
Bibliographic record
Abstract
BACKGROUND: Repetitive inpatient laboratory testing contributes to waste in healthcare. We evaluated an intervention bundle combining education and multilevel social comparison feedback to safely reduce repetitive use of inpatient routine laboratory tests. METHODS: This non-randomised controlled pre-intervention post-intervention study was conducted in four adult hospitals from October 2016 to March 2018. In the medical teaching unit (MTU) of the intervention site, learners received education and aggregate social comparison feedback and attending internists received individual comparison feedback on routine laboratory test utilisation. MTUs of the remaining three sites served as control units. Number and cost of routine laboratory tests ordered per patient-day before and after the intervention was compared with the control units, adjusting for patient factors. Safety endpoints included number of critically abnormal laboratory test results, number of stat laboratory test orders, patient length of stay, transfer rate to the ICU, and 30-day readmission and mortality. RESULTS: A total of 14 000 patients were included. Pre-intervention and post-intervention groups were similar in age, sex, Charlson Comorbidity Index and length of stay. From the pre-intervention period to the post-intervention period, significantly fewer routine laboratory tests were ordered at the intervention MTU (incidence rate ratio=0.89; 95% CI 0.79 to 1.00; p=0.048) with associated costs savings of $C68 877 (p=0.020) as compared with the control sites. The variability in the ordering pattern of internists at the intervention site also decreased post-intervention. No worsening was noted in the safety endpoints between the pre-intervention and post-intervention period at the intervention unit compared with the controls. CONCLUSIONS: led to cost savings through reduced use of routine laboratory tests in hospitalised patients.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".