Effect of electronic drug-drug interaction alerts on patient and clinician outcomes: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Drug interaction checking software is ubiquitous in clinical decision support systems (CDSS-DI) but patient relevance and accuracy are variable and the impact on patient outcomes is unproven. We compared the effectiveness of CDSS-DI with similar care without CDSS-DI. MATERIALS AND METHODS: We searched multiple bibliographic databases from 1990 to end-2024 for randomized trials (RCTs) or prospective cohort studies evaluating CDSS-DI at prescription, dispensing, or administration compared to a control group, and assessing clinical, process and burden outcomes. We used Cochrane Risk of Bias v2.0 and ROBINS-I to assess risk of bias and random effects modeling using meta 6.0 package in R for meta-analysis. RESULTS: Eight studies were included-7 RCTs (2 parallel and 5 cluster) and 1 prospective cohort study (total N = 43 413 patients). Mortality rates were similar between intervention (0.14%) and control (0.07%) groups (OR: 1.94 [95% CI: 0.88-4.29], P = .078). One study reported a minor, possibly irrelevant, 3-hour decrease in length of stay (P = .0021) in the intervention group. CDSS-DI alerts modestly influenced prescribing behavior (OR: 2.08 [95% CI: 1.01-4.27], P = .05), but did not significantly reduce the incidence of targeted adverse drug interactions (OR: 0.86 [95% CI: 0.56-1.34], P = .37). DISCUSSION: Surprisingly little high-quality research addresses the effect of CDSS-DI on patient or clinician outcomes. Current evidence continues to suggest no major benefit for patient-important outcomes. Given the potential for harms and important time burdens, CDSS-DI alerting needs improvement. CONCLUSION: CDSS-DI alerts show no significant improvement in patient-important outcomes. Optimizing alert accuracy, clinical relevance, and patient-specific integration is essential to enhance their value in practice.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".