Methods for studying medication safety following electronic health record implementation in acute care: a scoping review
Bibliographic record
Abstract
OBJECTIVES: The objective of this scoping review is to map methods used to study medication safety following electronic health record (EHR) implementation. Patterns and methodological gaps can provide insight for future research design. MATERIALS AND METHODS: We used the Joanna Briggs Institute scoping review methodology and a custom data extraction table to summarize the following data: (1) study demographics (year, country, setting); (2) study design, study period, data sources, and measures; (3) analysis strategy; (4) identified limitations or recommendations; (5) quality appraisal; and (6) if a Safety-I or Safety-II perspective was employed. RESULTS: We screened 5879 articles. One hundred and fifteen articles met our inclusion criteria and were assessed for eligibility by full-text review. Twenty-seven articles were eligible for extraction. DISCUSSION AND CONCLUSION: We found little consistency in how medication safety following EHR implementation was studied. Three study designs, 7 study settings, and 10 data sources were used across 27 articles. None of the articles shared the same combination of design, data sources, study periods, and research settings. Outcome measures were neither defined nor measured consistently. It may be difficult for researchers to aggregate and synthesize medication safety findings following EHR implementation research. All studies but one used a Safety-I perspective to study medication safety. We offer a conceptual model to support a more consistent approach to studying medication safety following EHR implementation.
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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 | 0.140 | 0.332 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.061 | 0.050 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".