Pharmacist-led medication reconciliation at patient discharge: a tool to reduce healthcare utilization? an observational study in patients 65 years or older
Bibliographic record
Abstract
BACKGROUND: Older patients often experience adverse drug events (ADEs) after discharge that may lead to unplanned readmission. Medication Reconciliation (MR) reduces medication errors that lead to ADEs, but results on healthcare utilization are still controversial. This study aimed to assess the effect of MR at discharge (MRd) provided to patients aged over 65 on their unplanned rehospitalization within 30 days and on both patients' experience of discharge and their knowledge of their medication. METHODS: An observational multicenter prospective study was conducted in 5 hospitals in Brittany, France. RESULTS: Patients who received both MR on admission (MRa) and MRd did not have significantly fewer deaths, unplanned rehospitalizations and/or emergency visits related to ADEs (OR = 1.6 [0.7 to 3.6]) or whatever the cause (p = 0.960) 30 days after discharge than patients receiving MRa alone. However, patients receiving both MRa and MRd were more likely to feel that their discharge from the hospital was well organized (p = 0.003) and reported more frequently that their community pharmacist received information about their hospital stay (p = 0.036). CONCLUSIONS: This study found no effect of MRd on healthcare utilization 30 days after discharge in patients over 65, but the process improved patients' experiences of care continuity. Further studies are needed to better understand this positive impact on their drug care pathway in order to improve patients' ownership of their drugs, which is still insufficient. Improving both the interview step between pharmacist and patient before discharge and the transmission of information from the hospital to primary care professionals is needed to enhance MR effectiveness. TRIAL REGISTRATION: NCT04018781 July 15, 2019.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".