An Evidence-Based Tool for Safe Configuration of Electronic Health Records: The eSafety Checklist
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) are transforming the way health care is delivered. They are central to improving the quality of patient care and have been attributed to making health care more accessible, reliable, and safe. However, in recent years, evidence suggests that specific features and functions of EHRs can introduce new, unanticipated patient safety concerns that can be mitigated by safe configuration practices. OBJECTIVE: This article outlines the development of a detailed and comprehensive evidence-based checklist of safe configuration practices for use by clinical informatics professionals when configuring hospital-based EHRs. METHODS: A literature review was conducted to synthesize evidence on safe configuration practices; data were analyzed to elicit themes of common EHR system capabilities. Two rounds of testing were completed with end users to inform checklist design and usability. This was followed by a four-member expert panel review, where each item was rated for clarity (clear, not clear), and importance (high, medium, low). RESULTS: An expert panel consisting of three clinical informatics professionals and one health information technology expert reviewed the checklist for clarity and importance. Medium and high importance ratings were considered affirmative responses. Of the 870 items contained in the original checklist, 535 (61.4%) received 100% affirmative agreement among all four panelists. Clinical panelists had a higher affirmative agreement rate of 75.5% (656 items). Upon detailed analysis, items with 100% clinician agreement were retained in the checklist with the exception of 47 items and the addition of 33 items, resulting in a total of 642 items in the final checklist. CONCLUSION: Safe implementation of EHRs requires consideration of both technical and sociotechnical factors through close collaboration of health information technology and clinical informatics professionals. The recommended practices described in this checklist provide systems implementation guidance that should be considered when EHRs are being configured, implemented, audited, or updated, to improve system safety and usability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it