Opportunities for Medication-Related Support after Discharge from Hospital
Bibliographic record
Abstract
Background: Despite documentation of the need for and benefit of medication-related support for patients who have been discharged from hospital, such support is not consistently available. This gap in care represents an opportunity for community pharmacists. Objective: To detail the types of drug-related issues identified for patients after discharge from hospital. Methods: The study entailed review of medication assessments performed by a Medication Management Program pharmacist for patients discharged from a community hospital over a 1-year period, in order to identify the number and type of drug-related problems, the number of discrepancies and the medications most commonly implicated in these problems. Results: Records were reviewed for 110 patients discharged during the study period. For these patients, the pharmacist identified a total of 259 drug-related problems (median 2 per patient) and 135 medication discrepancies (median 1 per patient) shortly after discharge. The most commonly implicated medications were calcium-vitamin D supplements, acetylsalicylic acid, furosemide and ramipril. Conclusion: With information about the types of drug-related problems and discrepancies commonly identified for patients discharged from hospital, community pharmacists are ideally positioned to respond to this gap in care.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".