Medication Reconciliation in the Hospital: What, Why, Where, When, Who and How?
Bibliographic record
Abstract
Medication reconciliation arose as the solution to the well-documented patient safety problem of unintentionally introducing changes in patients' medication regimens due to incomplete or inaccurate medication information at transitions in care. Unfortunately, medication reconciliation has often been misperceived as a superficial administrative accounting task with a "pre-occupation with completing forms," resulting in the implementation of ineffective processes. In this article, the authors briefly review the evidence supporting medication reconciliation but focus more on key practical questions regarding the elements of an effective medication reconciliation process: what it should consist of, where and when it should occur, who should carry it out and how hospitals should implement it. The authors take the why of medication reconciliation to consist not just of the professional obligation to avoid causing harm, but also of a rational self-interest on the part of healthcare leaders. The authors argue that, rather than wasting time implementing a nominal reconciliation process, we should invest time and energy in a more robust and effective strategy, and they address specific practical questions that arise in such an effort.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".