A systematic review of medication safety outcomes related to drug interaction software.
Bibliographic record
Abstract
BACKGROUND: Adverse drug events (ADEs) represent an important problem for hospital and primary care. Software that detects potential adverse drug interactions has been widely implemented in an effort to reduce the rate of ADEs. However, the impact of drug interaction detection software (DIS) on patient safety outcomes remains unknown. OBJECTIVES: To systematically review the literature on DIS in preventing adverse drug events and determine the effectiveness and cost-effectiveness of DIS. METHODS: A literature search of MEDLINE, EMBASE, CINAHL, IPA and Healthstar, using terms "Computer, Software or Decision Support" combined with "Drug Interactions, Drug Errors or Drug Monitoring" sought English language, post-1990 prospective studies that examined drug interaction (drug-drug) software as an intervention and adverse drug interactions as an outcome. Relevant studies were analyzed using a Bayesian meta-analysis approach. RESULTS: Of 5848 citations, only four studies met our inclusion criteria. Most of the excluded studies were not prospective or measured only prescriber attitudes, implementation success or changes in workflow. No study examined the impact of drug interaction software exclusively, rather as a component of decision support software. A Bayesian meta-analysis of these studies showed no significant difference in event rate between intervention and control groups (relative risk 0.66, 95% CI 0.33 to 1.18). The posterior median I-squared was 52%. CONCLUSION: No good quality studies address the specific benefits and harms or cost-effectiveness of drug interaction software on medication safety or clinical outcomes. The evidence at present does not support a benefit for these systems or support any policy to widely disseminate their use.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 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".