Nurses’ Perceptions on the Usability of Electronic Health Records: A Scoping Review
Bibliographic record
Abstract
Introduction: Electronic health records (EHRs) are designed to enhance the efficiency and quality of nursing workflows and documentation. EHR usability refers to how effectively the system supports users to accomplish their work tasks. However, the understanding of nurses’ perceptions of EHRs usability in inpatient settings is limited. Objective: Examine the available literature on nurses’ perceptions of EHRs usability in these settings. Methods: This scoping review was guided by the Arksey and O’Malley (2005) methodology and methodological steps of the Joana Brigs Institute (Peters et al., 2015). Search terms included combinations of synonyms for nurses, perceptions, and EHRs usability. Eligible sources of data were primary research studies published in English between January 1, 2013, and July 1, 2024, and extracted from PubMed, CINAHL, and Scopus databases. Inclusion criteria targeted Registered Nurses and Registered Practical Nurses in inpatient hospital settings in North America, Europe and Australia. Findings were presented descriptively and thematically using a narrative analysis. Results: Twenty studies met the inclusion criteria. The narrative synthesis generated five themes: 1) ease of information accessibility; 2) nursing workflow challenges; 3) EHR design, technical issues, interoperability; 4) impact of the EHR on the nurse-patient relationship, and 5) user training. Nurses identified factors that positively impacted EHRs usability, such as real-time access to patient information in one location, the ability to view patterns and trends in patient status, and improved interprofessional collaboration, but they shared disproportionally negative perceptions of EHRs usability. Discussion and Conclusion: Future research should focus on addressing these challenges to optimize EHR design, enhance training strategies, and improve system interoperability, ultimately supporting nursing workflows and enhancing patient care quality.
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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 | 0.047 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".