Medication reconciliation by community pharmacists: an essential contribution to ensure continuity of care with regard to medications at hospital discharge.
Bibliographic record
Abstract
Background: Collaboration and communication between hospital and ambulatory care is crucial to ensure continuity of care with regard to medications. However, electronic platforms to facilitate seamless care are often deficient and/or insufficiently integrated. Different Belgian chronic care projects (i.e. regional projects with support of the National Institute for Health and Disability Insurance to enhance integrated care) have therefore decided to implement the ‘green envelope’ as a way to transfer the therapy plan of the patient at discharge to the community pharmacy. The green envelope also contains the prescriptions for the patient, as well as an information letter for the pharmacist, including a QR-code to an online platform where the characteristics of the medication reconciliation, as performed by the community pharmacist, can be registered. The aim of this study was to analyze the findings registered by the pharmacists, as to have a clear idea on their contribution to continuity of care with regard to medications. Methods: A descriptive analysis was performed on the data registered by the community pharmacist during / after medication reconciliation. Results: In total, 1807 online surveys were completed by community pharmacists. The mean duration of the patient encounter in the pharmacy, which in more than three quarter of the cases took place on the day of discharge or the day thereafter, was 12,8 minutes (SD 6,933). Over 63% of patients discharged from hospital were aged +75; 52.4% was female. In about 68% of cases, another person than the patient presented him/herself at the pharmacy. About 74% of the patients had not visited the GP before presenting at the pharmacy; only 24% of patients had a planned appointment with the GP. In about 19% of the cases, the pharmacist discovered at least one medication discrepancy. Most frequently it concerned omitted medication, i.e. medication that was taken by the patient before admission, and for which it was unclear for the patient and pharmacist whether it had been stopped intentionally or not. The pharmacist noted in about 11% of cases at least one drug-related problem, mostly related to the choice or use of the drug. With regard to the choice of drug, it mostly concerned a lack of re-substitution to the molecule used by the patient at home; problems related to the use of the drug mostly referred to incorrect dosing or frequency, or incorrect way of administration. In about 15% of cases, the pharmacist contacted another HCP, mostly the GP. On the level of the patient, the intervention mostly consisted of extensive counselling. The final therapy plan was in less than 60% shared with other HCPs. Problems with the platforms used, and lack of collaboration, were mentioned as the most important reasons for not sharing. Conclusion: Our results show that pharmacists discover a relatively high number of medication discrepancies and drug related problems while performing medication reconciliation at discharge. Their alertness, engagement to contact other HCPs and talk to the patients and their relatives undoubtedly contribute to a safe transfer of patients from the hospital to home.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".