Cross‐sectional evaluation of a clinical decision support tool to identify medication‐related problems at discharge from the acute care setting
Bibliographic record
Abstract
Abstract Background There are many reported pharmacist‐led transitions of care (TOC) programs to address medication‐related problems (MRP) at discharge from the acute care setting. Most have identified time and labor resources as significant limitations. This study aims to assess the effectiveness of a medication risk score (MRS)‐driven clinical decision support system (CDSS) in identifying actionable MRPs and improving medication safety in the acute care discharge TOC setting. Methods A cross‐sectional analysis was conducted in a cohort of 481 subjects discharged from the acute care setting. The MRS‐CDSS was utilized to identify MRPs and provide recommendations for risk reduction. The distribution of MRPs, recommendations, and their associations with MRS severity were analyzed. Additionally, the potential reduction in MRS per subject and its correlation with MRS severity were examined. Results The median MRS reduction per subject was 2 points, while high/severe‐risk patients showed a median potential reduction of 7 points. Among the identified MRPs ( n = 691), drug interaction, drug use without indication, and adverse drug reaction accounted for 89.7% of all MRPs. The top three recommendations, discontinue medication, change the time of administration, and start alternative therapy, represented 94.1% of all recommendations. Stratified analysis by MRS category revealed a significant increase in adverse drug reaction MRPs and recommendations to discontinue medications with higher MRS severity. The results were consistent with previous outpatient studies, supporting the MRS‐CDSS's ability to enhance medication safety. Conclusion This study demonstrates that the MRS‐CDSS effectively identifies actionable MRPs and has the potential to substantially reduce overall pharmacotherapy regimen risk when applied during acute care discharge TOC. The findings support implementable recommendations directed at patient safety and the allocation of health care resources to high‐risk patients for maximum benefit.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".