Appropriateness of Medications in Older Adults Living With Frailty: Impact of a Pharmacist-Led Structured Medication Review Process in Primary Care
Bibliographic record
Abstract
Background: Older persons with frailty take multiple medications and are vulnerable to inappropriate prescribing. Objective: This study assesses the impact of a team-based, pharmacist-led structured medication review process in primary care on the appropriateness of medications taken by older adults living with frailty. Methods: This was a quasi-experimental pretest-posttest design in 6 primary care practices within an academic clinic in Edmonton, Alberta, Canada. We enrolled community dwelling older adults 65 years and older with frailty who have polypharmacy and/or 2 or more chronic conditions (ie, high-risk group for drug-related issues). The intervention was a structured pharmacist-led medication review using evidence-based explicit criteria (ie, Beers and STOPP/START criteria) and implicit criteria (ie, pharmacist expertise) for potentially inappropriate prescribing, done in the context of a primary care team-based seniors’ program. We measured the changes in the number of medications pre- and postmedication review, number of medications satisfying explicit criteria of START and STOPP/Beers and determined the association with frailty level. Data were analyzed using descriptive and inferential statistics (a priori significance level of P < .05). Results: A total of 54 participants (61.1% females, mean age 81.7 years [SD = 6.74]) enrolled April 2017 to May 2018 and 52 participants completed the medication review process (2 lost to hospitalization). Drug-related problems noted on medication review were untreated conditions (61.1%), inappropriate medications (57.4%), and unnecessary therapy (40.7%). No significant changes in total number of medications taken by patients before and after, but the intervention significantly decreased number of inappropriate medications (1.15 meds pre to 0.9 meds post; P = .006). Conclusion: A pharmacist-led medication review is a strategy that can be implemented in primary care to address inappropriate medications.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".