Standard admission order sets promote ordering of unnecessary investigations: a quasi-randomised evaluation in a simulated setting
Bibliographic record
Abstract
Standard admission order sets have become ubiquitous across hospitals to promote adherence to practice guidelines and increase ordering efficiency.1 2 This standardisation arose in part out of a need to minimise waste in healthcare, a phenomenon identified as a major barrier to reducing future healthcare costs.3 However, few studies have systematically evaluated whether these standardised orders can actually promote overordering of investigations. At our academic hospital’s coronary care unit (CCU), a single mandatory generic order set is used regardless of admitting diagnosis and includes optional check boxes for serum thyroid-stimulating hormone (TSH) and brain natriuretic peptide (BNP). We postulated that physicians order investigations differently on admission based on which investigations are included in the admission order set. We quasi-randomised a convenience sample of participants in a double-blind fashion to receive either our standard CCU admission order set or a slightly modified version (see online supplementary file). The participants included internal medicine staff physicians, residents and clinical clerks (medical students, year 3 or 4) at our academic centre who were attending grand rounds. After their respective grand rounds, seated participants were provided with a case of a previously healthy 50-year-old man presenting with uncomplicated ST elevation myocardial infarction, now stable postpercutaneous revascularisation. Based on the clinical information provided, ordering TSH and BNP was not clinically indicated. Unaware of the differing versions, volunteer participants received a paper copy of one of the two versions of the admission order set. Researchers distributing the order sets were also not aware of which version of …
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".