The Effect of Computerized Physician Order Entry on Medication Errors and Adverse Drug Events in Pediatric Inpatients
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Bibliographic record
Abstract
OBJECTIVE: Computerized physician order entry (CPOE) has the potential to reduce patient injury resulting from medication errors. We assessed the impact of a CPOE system on medication errors and adverse drug events (ADEs) in pediatric inpatients. DESIGN: A retrospective cohort study. SETTING: Tertiary care pediatric hospital. PARTICIPANTS: Pediatric inpatients on 3 medical and 2 surgical wards. INTERVENTION: CPOE system implemented on 2 medical wards and compared with 1 medical and 2 surgical wards that continued to use hand written orders. OUTCOME MEASURES: Rate of medication error and ADEs before and after CPOE implementation. RESULTS: In 6 years, a total of 804 medication errors were identified with 18 ADEs, resulting in patient injury among 36 103 discharges and 179 183 patient days. The overall medication error rate (MER) was 4.49 per 1000 patient days. Before the introduction of CPOE, the MERs of the intervention versus control wards were indistinguishable (ratio = 0.93; 95% confidence interval [CI] = 0.76, 1.13). After the introduction of CPOE, the MER was 40% lower on the intervention than on the control wards (ratio = 0.60; 95% CI = 0.48, 0.74). On average, 490 patient days are required to see the benefit of one less medication error using CPOE. We did not demonstrate a similar effect of CPOE for ADEs (ratio of rate ratios = 1.30; 95% CI 0.47, 3.52). CONCLUSIONS: The introduction of a commercially available physician computer order entry system was associated with a significant decrease in the rate of medication errors but not ADEs in an inpatient pediatric population.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it