Management of potentially inappropriate medication use among older adult’s patients in primary care settings: description of an interventional prospective non-randomized study
Bibliographic record
Abstract
BACKGROUND: The management of inappropriate medication use in older patients suffering from multimorbidity and polymedication is a major healthcare challenge. In a primary care setting, a medication review is an effective tool through which a pharmacist can collaborate with a practitioner to detect inappropriate drug use. AIM: This project described the implementation of a systematic process for the management of potentially inappropriate medication use among Lebanese older adults. Its aim was to involve pharmacists in geriatric care and to suggest treatment optimization through the analysis of prescriptions using explicit and implicit criteria. METHOD: This study evaluated the medications of patients over 65 years taking a minimum of five chronic medications a day in different regions of Lebanon. Descriptive statistics for all the included variables using mean and standard deviation (Mean (SD)) for continuous variables and frequency and percentage (n, (%)) for multinomial variables were then performed. RESULTS: A total of 850 patients (50.7% women, 28.6% frail, 75.7 (8.01) mean age (SD)) were included in this study. The mean number of drugs per prescription was 7.10 (2.45). Roughly 88% of patients (n = 748) had at least one potentially inappropriate drug prescription: 66.4% and 64.4% of the patients had at least 1 drug with an unfavorable benefit-to-risk ratio according to Beers and EU(7)-PIM respectively. Nearly 50.4% of patients took at least one medication with no indication. The pharmacists recommended discontinuing medication for 76.5% of the cases of drug related problems. 26.6% of the overall proposed interventions were implemented. DISCUSSION: The rate of potentially inappropriate drug prescribing (PIDP) (88%) was higher than the rates previously reported in Europe, US, and Canada. It was also higher than studies conducted in Lebanon where it varied from 22.4 to 80% depending on the explicit criteria used, the settings, and the medical conditions of the patients. We used both implicit and explicit criteria with five different lists to improve the detection of all types of inappropriate medication use since Lebanon obtains drugs from many different sources. Another potential source for variation is the lack of a standardized process for the assessment of outpatient medication use in the elderly. CONCLUSION: The prevalence PIDP detected in the sample was higher than the percentages reported in previous literature. Systematic review of prescriptions has the capacity to identify and resolve pharmaceutical care issues thus improving geriatric care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".