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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".