Medication Reconciliation During Internal Hospital Transfer and Impact of Computerized Prescriber Order Entry
Bibliographic record
Abstract
BACKGROUND: Internal hospital transfer is a vulnerable time during which patients are at high risk of medication discrepancies that can result in clinically significant harm, medication errors, and adverse drug events. OBJECTIVE: To identify, characterize, and assess the clinical impact of unintentional medication discrepancies during internal hospital transfer and to investigate the influence of computerized prescriber order entry (CPOE) on medication discrepancies. METHODS: All patients transferred between 10 inpatient units at 2 tertiary care hospitals were prospectively assessed to identify discrepancies. Interfaces included transfers between (1) units that both used paper-based medication ordering systems; (2) units that both used CPOE-based systems; and (3) units that used both paper-based and CPOE-based systems (hybrid transfer). The primary endpoint was the number of patients with at least 1 unintentional medication discrepancy during internal hospital transfer. Discrepancies were identified through assessment and comparison of a best possible medication transfer list with the actual transfer orders. A multidisciplinary team of clinicians assessed the potential clinical impact and severity of unintentional discrepancies. RESULTS: Overall, 190 patients were screened and 129 patients were included. Eighty patients (62.0%) had at least 1 unintentional medication discrepancy at the time of transfer, and the most common discrepancy was medication omission (55.6%). Factors that independently increased the risk of a patient experiencing at least 1 unintentional discrepancy included lack of best possible medication history, increasing number of home medications, and increasing number of transfer medications. Forty-seven patients (36.4%) had at least 1 unintentional discrepancy with the potential to cause discomfort and/or clinical deterioration. The risk of discrepancies was present regardless of the medication-ordering system (paper, CPOE, or hybrid). CONCLUSIONS: Clinically significant medication discrepancies occur commonly during internal hospital transfer. A structured, collaborative, and clearly defined medication reconciliation process is needed to prevent internal transfer discrepancies and patient harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".