Medication management issues identified during home medication reviews for ambulatory community pharmacy patients
Bibliographic record
Abstract
Background: The health risks associated with poor medication practices in the home suggest that patients would benefit from home-based medication reviews that could detect and resolve these issues. However, remuneration for home visits often excludes ambulatory, nonhomebound patients. A subset of these patients have issues that cannot be adequately identified and resolved during the course of a typical pharmacy-based medication review. Purpose: This study aims to characterize the prevalence and nature of “hidden in the home” medication management issues in nonhomebound patients. Methods: Pharmacists facilitated subject enrollment among patients at 6 community pharmacies in Toronto over a 15-month period, from January 2016 to March 2017. Patients taking 5 or more chronic medications who were ambulatory (able to visit the pharmacy) and scored 3 points or higher on a prescreening questionnaire were invited to participate. Visits included a standard medication review, the identification of drug therapy problems and an assessment of the patient’s medication and organization/storage practices, followed by a medication cabinet cleanup. Results: One hundred patients were recruited, with a mean age of 76.9 years and taking on average 10 chronic medications. Pharmacists identified a total of 275 drug therapy problems (2.75 per patient). The most common issues reported additional therapy required (23.6%), nonadherence (23.3%) and adverse drug reactions (17.8%). For those patients 65 years or older (87%), 32% were found to be using at least 1 medication on the Beers Criteria list, while 6% were using 3 or more. Sulfonylureas, non-steroidal anti-inflammatory drugs and short-acting benzodiazepines were the most commonly implicated drugs. Medications were removed from the homes of 67% of the patients, with expiry of medication being the most common reason for removal (54.2%). The mean duration of a home visit was 49.5 minutes. Conclusion: Pharmacist-directed home medication reviews offer an effective mechanism to address the pharmacotherapy issues of patients taking multiple medications. These findings highlight the frequency of medication management issues in this group and suggest that home medication reviews could serve to minimize inappropriate use of medication and maximize health care cost savings in this unique patient population. Can Pharm J (Ott) 2019;152:xx-xx.
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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 | 0.001 | 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 | 0.001 |
| 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".