ORDER SETS IN HEALTH CARE: A SYSTEMATIC REVIEW OF THEIR EFFECTS
Bibliographic record
Abstract
OBJECTIVES: Order sets are widely used in hospitals to enter diagnosis and treatment orders. To determine the effectiveness of order sets in improving guideline adherence, treatment outcomes, processes of care, efficiency, and cost, we conducted a systematic review of the literature. METHODS: A comprehensive literature search was performed in various databases for studies published between January 1, 1990, and April 18, 2009. A total of eighteen studies met inclusion criteria. No randomized controlled trials were found. RESULTS: Outcomes of the included studies were summarized qualitatively due to variations in study population, intervention type, and outcome measures. There were no important inconsistencies between the results reported by studies involving different types of order sets. While the studies generally suggested positive outcomes, they were typically of low quality, with simple before-after designs and other methodological limitations. CONCLUSIONS: The benefits of order sets remain eminently plausible, but given the paucity of high quality evidence, further investigations to formally evaluate the effectiveness of order sets would be highly valuable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| 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; a candidate call from one teacher head, 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".