Medical admission order sets to improve deep vein thrombosis prophylaxis rates and other outcomes
Bibliographic record
Abstract
BACKGROUND: The value of order sets for clinical decision support has not been established. OBJECTIVE: To determine whether introduction of admission order sets increases the proportion of inpatients receiving deep venous thrombosis (DVT) prophylaxis. DESIGN: Before-after study. SETTING: Community hospital. PATIENTS: General medical patients admitted to hospital. INTERVENTION: Paper-based admission order sets (instead of free-text orders) for voluntary use by internists, without any education or behavior change interventions. MEASUREMENTS: Primary outcome was proportion of medical admissions ordered DVT prophylaxis. Secondary outcomes included overall utilization of DVT prophylaxis in medical inpatients and other admission order care quality measures. RESULTS: Prior to introduction of order sets, DVT prophylaxis was ordered in 10.9% of patients. Patients admitted with order sets were more likely to be ordered DVT prophylaxis than patients admitted with free-text orders (44.0% versus 20.6%, by months 14 and 15, P<0.0001). Hospital-wide DVT prophylaxis in medical inpatients increased from 12.8% to 25.8% of patient-days (P<0.0001). Order set use improved many other secondary outcomes (P<0.05 for all), including allied health consultations (62.8% versus 12.7%), use of standardized diabetic diet (17.0% versus 5.1%), insulin sliding scale (19.1% versus 7.6%), potassium replacement protocol (63.8% versus 0.51%), documentation of allergies (54.3% versus 9.6%) and resuscitation status (57.4% versus 10.2%), and reduced orders for inappropriate laboratory tests such as blood urea nitrogen (39.4% versus 59.0%). CONCLUSIONS: The broad impact of order sets and minimal organizational resources required for their implementation suggests that order sets may have wide applicability as a clinical decision support tool.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".