Quality Improvement Project to Reduce Drug-Related Problems (DRPs) and Potentially Inappropriate Medications (PIMs) in Geriatrics Cardiac Clinic in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Elderly people have a high risk of potentially inappropriate medications (PIMs) and drug-related problems (DRPs) due to polypharmacy, physical and mental limitations, pharmacokinetic, and pharmacodynamics changes. PURPOSE: To determine the role of geriatric pharmacists in reducing drug-related problems and potentially inappropriate medication. METHODS: The observational study was conducted from October 2014 to October 2017 to show the prevalence of DRPs, and PIMs. A total of 375 geriatric cardiology patients (aged ≥ 65) were recruited from Geriatrics Cardiac Clinic in Saudi Arabia. AGS Beers Criteria 2012 and STOPP/START Criteria were used to view the impact of services directed by clinical pharmacists in decreasing DRPs and PIMs including medication review, intervention, and education to junior physicians during multi-disciplinary rounds (MDRs) and by sending e-mail alerts. RESULTS: DRPs were found in 29.6% of patients and PIMs were found in 19% of patients. After medication review, 25% required interventions and the majority (89%) of interventions were accepted by the managing team. DRPs were found in 14.9% of patients and PIMs were found in 9.6% of the patients. DRPs and PIMs were reduced almost by 50% by reviewing the files and educating the involved physicians. CONCLUSION: This prospective study confirms a high prevalence of DRPs and PIMs in Saudi elderly cardiac patients.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".