Impact of doing Medication Reconciliation at the start of Inpatient Admission for Acute Care
Bibliographic record
Abstract
Medication reconciliation, conducted at the onset of inpatient admission for acute care, is a pivotal process aimed at averting medication errors and ensuring patient safety. This review scrutinizes the impact of medication reconciliation on patient outcomes, healthcare quality, and cost-effectiveness within acute care settings. While medication reconciliation entails creating an accurate list of a patient's current medications, comparing it with prescribed medications, and making clinical judgments, its efficacy has garnered increased attention from healthcare accrediting bodies such as the Accreditation Canada Program and the Joint Commission of the USA. Additionally, the World Health Organization's High 5s Project underscores its global significance in enhancing patient safety. Despite its benefits, medication reconciliation poses challenges such as inadequate patient medication documentation and resource limitations. Overcoming these challenges is pivotal to integrating medication reconciliation seamlessly into routine clinical practice. Nevertheless, the benefits of medication reconciliation in reducing medication errors, optimizing patient outcomes, and mitigating healthcare costs are substantial, highlighting the necessity of prioritizing its implementation in acute care settings.
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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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".