Results of a Quality Improvement Project Aimed at Eliminating Healthcare Waste by Changing Medical Resident Test Ordering Behavior
Bibliographic record
Abstract
BACKGROUND: In light of rising healthcare costs and evidence of inefficient use of medical resources, there is growing interest in reducing healthcare waste by clinicians. Unwarranted lab tests may lead to further tests, prolonged hospital stays, unnecessary referrals and procedures, patient discomfort, and iatrogenic anemia, resulting in significant economic and clinical effects. Blood tests are essential in guiding medical decisions, but they are also associated with significant financial and clinical costs. We designed a quality improvement study that attempted to decrease inappropriate ordering of laboratory tests while maintaining quality of care in a large residency program. METHODS: An algorithm outlining indications for complete blood count (CBC), coagulation profile (PT/INR) and basic metabolic profile (BMP) was created by the study team. Data from 1,312 patients over a 3-month period in the pre-intervention phase and 1,255 patients during the selected intervention phase were analyzed. The primary endpoint was mortality rate and secondary endpoints were length of stay and laboratory costs. RESULTS: There were significant decreases in the number of PT/INR orders (20.6%), followed by BMP orders (12.4%), and CBC orders (9.3%). The mortality rate was 5.3% for the pre-intervention phase and 5.8% for the selected intervention phase, with a difference of 0.5% (P = 0.44). CONCLUSION: Our approach leads to a decrease in costs, preventing unnecessary downstream testing, and improving patient experience. It also brought a mental discipline while ordering blood tests amongst residents.
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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 | 0.106 | 0.417 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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".