Perioperative Medication Management (POMM) Pilot: Integrating a Community-Based Medication History (MedsCheck) into Medication Reconciliation for Elective Orthopedic Surgery Inpatients
Bibliographic record
Abstract
Background: Medication safety along the continuum of care is dependent on the quality of medication information at each point of transfer. The purpose of our study was to assess the impact of integrating a community-based medication history (MedsCheck) into perioperative medication reconciliation for elective orthopedic surgery patients by assessing postoperative unintentional medication discrepancies. Secondary objectives were to evaluate community pharmacist participation and patient satisfaction. Methods: Patients scheduled for elective hip or knee surgery between April and September 2008 were identified as the study population. Patients and community pharmacies were contacted to coordinate the MedsCheck prior to the pre-admission clinic visit. At the visit, the Meds Check document was used to prepare a best possible medication history, which was documented in the patient chart. Medications were reconciled postoperatively. Participants were surveyed for feedback on the process. Results: Eighty-two patients were included in the study. A MedsCheck was completed for 73.8% (31/42) of eligible patients who were contacted prior to their pre-admission clinic visit. The average number of medications per patient was 8.4. The percentage of patients with at least 1 unintentional medication discrepancy decreased from 68.4% (13/19) to 47.6% (39/82) post-intervention. Total unintentional medication discrepancies decreased from 25.6% to 10.6%. Discussion: Integrating MedsCheck into the perioperative medication reconciliation process resulted in positive outcomes. The main challenge was coordination of the MedsCheck service prior to the patient's pre-admission clinic visit. Conclusion: Integrating MedsCheck into routine perioperative care for orthopedic patients is a feasible way to facilitate pharmacist medication reconciliation and increase patient satisfaction.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".