Bibliographic record
Abstract
BACKGROUND: Pharmacists are now receiving reimbursement by the Ontario government to do medication reviews for patients on 3 or more medications. However, they are often too busy in the community setting to thoroughly review medications with patients. Having a designated pharmacist to provide medication reviews could increase the number of reviews performed. METHODS: Step 1 involved developing a business plan to determine the number of medication reviews that needed to be done to pay a pharmacist a full-time salary. Step 2 involved establishing the core elements of medication therapy management that included medication review, a medication-related action plan, documentation and follow-up. In step 3, eligible patients were called and invited to attend an appointment to review their medications with the pharmacist. Upon completion of the medication reviews, a random group of patients were requested to complete a satisfaction survey after the medication review. RESULTS: Three hundred thirty-six patients received billable medication reviews from April 4 to July 27, 2012. Twenty-seven additional visits were performed as follow-up visits. Eighty pharmaceutical opinions met the eligibility criteria for billing. Fifteen patients received counselling for smoking cessation. Medication reviews were completed for 19 patients from 8 other pharmacies. Extra revenue was generated through the sales of replacements of expired products. An average of 2.08 drug-related problems per patients was identified. One hundred percent of the patients were very satisfied with the service. CONCLUSION: A full-time pharmacist position providing enhanced medication management services generated enough income to pay for a full-time pharmacist's salary. The benefits to the patients were an increase in identification and resolution of drug-related problems, as well as an opportunity to receive disease state education and experience an improvement in disease states. Patients were extremely satisfied with the medication review process and the service provided to them. Can Pharm J 2013;146:162-168.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.001 |
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".