Cost‐effectiveness of an electronic medication ordering and administration system in reducing adverse drug events
Bibliographic record
Abstract
OBJECTIVES: Adverse drug events (ADEs) are common and cause significant morbidity and mortality. Patient safety groups advocate the implementation of electronic medication order entry systems to reduce ADEs. However, these systems are costly, and there are limited data on their effectiveness. We conducted a study to examine the costs of introducing an electronic medication ordering and administration system and its potential impact on reducing ADEs. METHODS: An incremental cost-effectiveness analysis was performed comparing an electronic medication ordering and administration system to the standard system used at a large health care institution over a 10-year time horizon. Estimates of effect were obtained from the literature. Cost data were obtained from a health care institution in Toronto, Canada. RESULTS: The incremental cost-effectiveness of the new system was $12,700 (USD) per ADE prevented. The cost-effectiveness was found to be sensitive to the ADE rate, to the effectiveness of the new system, the cost of the system, and costs due to possible increase in doctor workload. CONCLUSIONS: An electronic medication order entry and administration system could improve care by reducing adverse events. Unfortunately there are limited data on effectiveness of these systems at reducing ADEs. Further research is required to determine more precisely the potential economic benefit of this technology.
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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 | 0.106 | 0.037 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".