Implementation of Admission Medication Reconciliation at Two Academic Health Sciences Centres: Challenges and Success Factors
Bibliographic record
Abstract
Admission Medication Reconciliation (Med Rec) is an organizational practice designed to ensure patients' pre-admission medications are ordered correctly upon hospital admission. We describe the implementation of admission Med Rec at two academic health sciences centres, each having designed distinctly different processes. Common challenges encountered included the multi-step, inter-professional nature of Med Rec, staffing resource and workload concerns and frequent medical staff turnover in a teaching environment. Both teams found that participation in a national safety collaborative enabled the pilot initially; however, they later found the outcome measures suggested by the collaborative less useful and switched to internal compliance measures for establishing maintenance and spread. Common themes were identified among the critical success factors, with unique variations at each centre. Both teams acknowledged accreditation standards to be a major accelerator of implementation and spread. Using different measures of implementation success at each centre, the majority of patient admissions on the pilot units are complying with admission Med Rec. However, very high levels of compliance remain elusive. At Sunnybrook Health Sciences Centre's pilot unit, 62-77% of patients are being screened by a pharmacist and 65-75% of high-risk patients identified are undergoing Med Rec by a pharmacist. At The Hospital for Sick Children's pilot unit, 72-88% of patients have a physician's primary medication history documented on a Med Rec form and 57-73% of patients are also undergoing Med Rec by a nurse or pharmacist.
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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 | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".