Medication reconciliation: Barriers and facilitators from the perspectives of resident physicians and pharmacists
Bibliographic record
Abstract
BACKGROUND: Medication reconciliation can prevent medication errors and harm when patients transition between hospital and other care settings. Though a Joint Commission hospital Patient Safety Goal since 2006, organizations continue to have difficulty implementing the process. OBJECTIVE: To determine factors that influence performance of medication reconciliation in a hospital setting with a computerized medication reconciliation tool. DESIGN: Cognitive task analysis (CTA) and focus group interviews. SETTING: Urban, academic, tertiary-care Veterans Affairs medical center. PARTICIPANTS: Internal medicine house staff physicians (n = 23) and inpatient staff pharmacists (n = 12). MEASUREMENTS: CTA participants verbalized their thoughts while they completed medication reconciliation with the computerized tool. Focus group participants described medication reconciliation's purpose and effectiveness, how they completed the task, and its barriers and facilitators. Interviews were recorded and analyzed using social science methods for analyzing qualitative data. RESULTS: Participants agreed that a central goal of medication reconciliation is to prevent prescribing errors, but disagreed about whether it achieves this goal. Computerization facilitated the task, but participants said that computers and patients can be unreliable sources of information. Participants varied in how they sequenced components of the task. When time was limited, physicians considered other responsibilities higher priority. Both physicians and pharmacists expressed low self-efficacy, ie, low perceived capability to achieve the objectives of the process. CONCLUSION: Key barriers to medication reconciliation are unreliable sources of medication information and tasks that compete for providers' time and attention that they consider higher priority. Addressing these barriers while increasing providers' self-efficacy might improve medication reconciliation and its outcomes.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".