A Multifaceted Quality Improvement Initiative to Reduce Unnecessary Laboratory Testing on Internal Medicine Inpatient Wards
Bibliographic record
Abstract
Background The American and Canadian Choosing Wisely campaigns recommend against routine complete blood count (CBC) and chemistry testing in the face of clinical stability in the inpatient internal medicine setting. Problem Patients on internal medicine units commonly have daily lab tests ordered at admission and lab testing is often bundled. Four ‘core’ lab tests (CBC, electrolytes, creatinine, and urea) account for more than half of all lab tests performed. Methods The Model for Improvement and the Donabedian framework was used to define the problem, evaluate the baseline state, and generate targeted improvements. A quality improvement (QI) initiative consisting of education and process change was implemented on one general internal medicine unit and multiple plan-do-study-act cycles were carried out. The outcome measure was the total number of core labs performed, and the process measure was the proportion of patients with tests ordered on a repeating daily basis. Results The initiative led to an 18.9% decrease in the total number of core labs ordered and an 18.2% absolute decrease in repeating daily lab orders. Conclusions A multifaceted QI initiative aimed at reducing unnecessary lab testing was successful at reducing the number of lab tests ordered and changing lab ordering process.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".