Computer order entry systems in the emergency department significantly reduce the time to medication delivery for high acuity patients
Bibliographic record
Abstract
BACKGROUND: Computerized physician order entry (CPOE) systems are designed to increase safety and improve quality of care; however, their impact on efficiency in the ED has not yet been validated. This study examined the impact of CPOE on process times for medication delivery, laboratory utilization and diagnostic imaging in the early, late and control phases of a regional ED-CPOE implementation. SETTING: Three tertiary care hospitals serving a population in excess of 1 million inhabitants that initiated the same CPOE system during the same 3-week time window. Patients were stratified into three groupings: Control, Early CPOE and Late CPOE (n = 200 patients per group/hospital site). Eligible patients consisted of a stratified (40% CTAS 2 and 60% CTAS 3) random sample of all patients seen 30 days preceding CPOE implementation (Control), 30 days immediately after CPOE implementation (Early CPOE) and 5-6 months after CPOE implementation (Late CPOE). Primary outcomes were time to (TT) from physician assignment (MD-sign) up to MD-order completion. An ANOVA and t-test were employed for statistical analysis. RESULTS: In comparison with control, TT 1st MD-Ordered Medication decreased in both the Early and Late CPOE groups (102.6 min control, 62.8 Early and 65.7 late, p < 0.001). TT 1st MD-ordered laboratory results increased in both the Early and Late CPOE groups compared to Control (76.4, 85.3 and 73.8 min, respectively, p < 0.001). TT 1st X-Ray also significantly increased in both the Early and Late CPOE groups (80.4, 84.8 min, respectively, compared to 68.1, p < 0.001). Given that CT and ultrasound imaging inherently takes increased time, these imaging studies were not included, and only X-ray was examined. There was no statistical difference found between TT discharge and consult request. CONCLUSIONS: Regional implementation of CPOE afforded important efficiencies in time to medication delivery for high acuity ED patients. Increased times observed for laboratory and radiology results may reflect system issues outside of the emergency department and as a result of potential confounding may not be a reflection of CPOE impact.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".