Altering standard admission order sets to promote clinical laboratory stewardship: a cohort quality improvement study
Bibliographic record
Abstract
BACKGROUND: Careful design of preprinted order sets is needed to prevent medical overuse. Recent work suggests that removing a single checkbox from an order set changes physicians' clinical decision-making. LOCAL PROBLEM: During a 2-month period, our coronary care unit (CCU) ordered almost eight times as many serum thyroid-stimulating hormone (TSH) tests as our neighbouring intensive care unit, many without a reasonable clinical basis. We postulated that we could reduce inappropriate testing and improve clinical laboratory stewardship by removing the TSH checkbox from the CCU admission order set. METHODS: After we retrospectively evaluated CCU TSH ordering before intervention, the checkbox was removed from the CCU admission order set. Twelve weeks later, we commenced a prospective 2-month assessment of TSH testing and clinical sequelae of thyroid disease among all CCU admissions. If clinical indications were absent or testing had occurred within 6 weeks, TSH requests were labelled as 'inappropriate'. RESULTS: Physician ordering and, specifically, inappropriate ordering decreased substantially after the intervention. In 2016 among physician-ordered TSH tests, 60.6% (66/109) were inappropriate; in 2017 this decreased to 20% (2/10, p=0.01). Overall, the net effect of checkbox removal saw the decrease in TSH testing without clinical indication outweigh an increase in missed testing where indications appear to exist. CONCLUSIONS: . Simple removal of a checkbox may dramatically change test ordering patterns and promote clinical laboratory stewardship. Given our reliance on order sets, particularly by trainees, changes to order sets must be cautious to assure guideline-directed care is maintained.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".