Strategies and Tools for Electronic Health Records and Physician Workflow Alignment: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Electronic health records (EHRs) have been widely adopted in health care systems globally, offering potential benefits in data accessibility, quality improvement, and enhanced patient outcomes. However, the alignment between EHRs and physician workflows remains a significant challenge, leading to negative impacts on physician well-being and patient care. While health care organizations have attempted various strategies to improve this alignment, critical gaps still persist, highlighting the need for a comprehensive understanding of existing approaches and their effectiveness as a means to chart effective strategies to align physician workflows with EHRs. OBJECTIVE: This scoping review aims to identify and synthesize the strategies and tools health care organizations have used to align physician workflows with EHRs. This review will provide a toolkit for health care organizations and researchers, offering insights into effective alignment practices and identifying knowledge gaps for future research. METHODS: This scoping review will follow the Joanna Briggs Institute framework for scoping reviews while incorporating the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for transparent reporting. We will search multiple databases, including MEDLINE, PubMed, Cochrane, CINAHL, Scopus, Embase, and Web of Science, for relevant literature on tools, strategies, and interventions used to align physician workflows with EHRs. The review will focus on studies involving physicians in direct patient care across primary, secondary, tertiary, and quaternary care settings. Two independent reviewers will screen titles, abstracts, and full texts for inclusion. Data extraction will be performed using a standardized form, and findings will be narratively synthesized and presented in tables and charts. RESULTS: The study is expected to provide a comprehensive toolkit of strategies, tools, and interventions for EHR-physician workflow alignment. This synthesis will offer health care organizations practical guidance for improving alignment and provide researchers with a foundation for identifying research gaps and future directions. The final report is planned for submission to an indexed journal in August 2025. CONCLUSIONS: This scoping review will offer valuable insights into the strategies and tools implemented by health care organizations to align EHRs with physician workflows. By assessing the effectiveness and limitations of these approaches, the review aims to contribute to improved EHR usability, reduced physician burnout, and enhanced patient care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60464.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.142 | 0.164 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".