Re-Purposing the Ordering of Routine Laboratory Tests in Hospitalized Medical Patients (RePORT): protocol for a multicenter stepped-wedge cluster randomised trial to evaluate the impact of a multicomponent intervention bundle to reduce laboratory test over-utilization
Bibliographic record
Abstract
Abstract Background Laboratory test overuse in hospitals is a form of healthcare waste that also harms patients. Developing and evaluating interventions to reduce this form of healthcare waste is critical. We detail the protocol for our study which aims to implement and evaluate the impact of an evidence-based, multicomponent intervention bundle on repetitive use of routine laboratory testing in hospitalized medical patients across adult hospitals in the province of British Columbia, Canada. Methods We have designed a stepped-wedge cluster randomized trial to assess the impact of a multicomponent intervention bundle across 16 hospitals in the province of British Columbia in Canada. We will use the Knowledge to Action cycle to guide implementation and the RE-AIM framework to guide evaluation of the intervention bundle. The primary outcome will be the number of routine laboratory tests ordered per patient-day in the intervention versus control periods. Secondary outcome measures will assess implementation fidelity, number of all common laboratory tests used, impact on healthcare costs, and safety outcomes. The study will include patients admitted to adult medical wards (internal medicine or family medicine) and healthcare providers working in these wards within the participating hospitals. After a baseline period of 24 weeks, we will conduct a 16-week pilot at one hospital site. A new cluster (containing approximately 2–3 hospitals) will receive the intervention every 12 weeks. We will evaluate the sustainability of implementation at 24 weeks post implementation of the final cluster. Using intention to treat, we will use generalized linear mixed models for analysis to evaluate the impact of the intervention on outcomes. Discussion The study builds upon a multicomponent intervention bundle that has previously demonstrated effectiveness. The elements of the intervention bundle are easily adaptable to other settings, facilitating future adoption in wider contexts. The study outputs are expected to have a positive impact as they will reduce usage of repetitive laboratory tests and provide empirically supported measures and tools for accomplishing this work. Trial Registration This study was prospectively registered on April 8, 2024, via ClinicalTrials.gov Protocols Registration and Results System (NCT06359587). https://classic.clinicaltrials.gov/ct2/show/NCT06359587?term=NCT06359587&recrs=ab&draw=2&rank=1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".