Bibliographic record
Abstract
BACKGROUND: Medication reconciliation (MR) is associated with reduced discrepancies and adverse events within institutions. In ambulatory care, MR is often considered more challenging due to periodic, brief patient encounters and the involvement of multiple prescribers who lack shared records. MedsCheck, a community pharmacy program in Ontario for patients with diabetes or those taking 3 or more medications, generates a best possible medication history (BPMH) that can serve as a starting point for MR. Our objectives were to develop and evaluate a program to integrate MedsCheck into the workflow of an ambulatory clinic. METHODS: An initiative was implemented within the Complex Care Clinic (CCC), an academic internal medicine clinic at Women's College Hospital (WCH). During booking of their first appointment, patients were encouraged to receive a MedsCheck. A letter was faxed to the patient's preferred community pharmacy with a request to conduct a MedsCheck and send documentation to the clinic. Evaluation included patient and health care provider questionnaires and chart review. RESULTS: Fifty-five of 86 new patients referred to the CCC were eligible for a MedsCheck. Fifty-four patients consented to having their community pharmacy contacted, and documentation was received for 21 (39%) of these reviews. Chart review was conducted for patients who completed the patient feedback questionnaire (n = 32). Community pharmacists reported at least 1 drug therapy problem for 12 (57%) patients with a mean of 2.6 (SD 1.5) per patient. Medical residents reported an estimated mean appointment time savings of 7.9 minutes (SD 2.4). CONCLUSION: The program was feasibly integrated into clinic workflow and shortened the time spent creating BPMHs. This approach could be adopted by other ambulatory care clinics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.347 | 0.154 |
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".