Strategies and Tools for electronic health records and physician workflow alignment: A scoping review protocol
Bibliographic record
Abstract
Abstract Introduction The rapid adoption of electronic health records (EHR) across the globe by the healthcare industry is an indication of the rising digitalization of healthcare functions. Despite the potential benefits of EHR, achieving a fit between physician workflow and EHR has posed a major challenge, with negative effects on physician wellbeing and patient outcomes. To this effect, organizations have attempted to align the EHR with physician workflow in various ways. It is important to understand the strategies and tools that have been employed and, where possible, the outcome of these engagements as a fundamental insight to resolving this issue. Methodology The study will employ a methodological framework developed by Arksey and O’Malley to strategically identify articles that use any type of concept for the alignment of physician workflow and EHR from Embase (OVID), MEDLINE (OVID), PubMed, CINAHL, and Scopus databases. It will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) checklist to report findings. The articles will be extracted into the Covidence software, screened, and relevant data will be extracted from the selected articles. A qualitative thematic approach will be used to analyze the data. No ethical approval was sought because the data were collected from sources in the public domain. Result The scoping review is scheduled to be completed by April 2024. The results will be presented in tabular and narrative form and published in a reputable journal and through conference presentations. Conclusion The outcome of the review aims to provide a systematic toolkit of activities and strategies and, where possible, the corresponding effectiveness that organizations can use to optimize EHR-to-physician workflow alignment. The outcome would also help make appropriate recommendations for alignment. Subsequently, it can improve patient outcomes such as the reduction of medication errors and improvement of patient-centered, improve physician well-being and reduce burnout.
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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 | 0.167 | 0.133 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".