Exploring the Contextual and Human Factors of Electronic Medication Reconciliation Research: A Scoping Review
Bibliographic record
Abstract
UNLABELLED: Medication reconciliation (MedRec) is an important task that occurs in a variety of different contexts. Similar to other healthcare practices, MedRec is transitioning from being a paper-based process to one that is performed electronically. This paper will provide a scoping review of the prevalent research topics from both contextual and human factors perspectives. METHODS: PubMed and CINAHL were searched for all articles including the term "medication reconciliation". The 139 articles that met inclusion criteria were reviewed for themes and findings. RESULTS: Three primary themes surfaced through this analysis: a) The contextual factors of MedRec, b) information technology (IT) in MedRec, and c) obstacles and opportunities for improving MedRec. DISCUSSION: MedRec is performed in a variety of settings. The transition to electronic MedRec (eMedRec) has the potential to mitigate errors associated with a paper-based system but also creates opportunities for new technology-induced errors to occur. Interoperability with other health information systems is ideal. Additionally, Process standardization and workflow are important considerations when transitioning to eMedRec. CONCLUSION: As the process of medication reconciliation transitions from a paper-based to an electronic task, it is imperative to minimize the opportunity for human error and maximize the effectiveness of the system as a whole. Further, it is important for research to continue to explore original strategies for IT to enhance medication reconciliation.
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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 | 0.043 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".