Understanding nurses' perceptions of electronic health record use in an acute care hospital setting
Bibliographic record
Abstract
As Canadian healthcare organizations implement electronic health records (EHRs), nurses are expected to use the technology in their practice. Findings of a literature review suggest that usability (ease of use, functionality, navigation and impact on workload), the organizational context (support from leadership, level of training, level of on-going support, physical environment and implementation process) and individual nurse characteristics (sex, age, nursing unit, years of experience as a registered nurse, country of nursing education, years of experience using an EHR, previous EHR use and formal informatics training) influence nurses’ use of these systems. Thus, the purpose of this doctoral research was to better understand the relationships between the variables that make up usability, organizational context, individual nurse characteristics, and nurses’ perceptions of EHR use. This study was conducted using a sequential mixed methods design with two phases. Phase One consisted of a cross sectional survey that was piloted and then administered to nurses in an acute care teaching hospital in Toronto, Canada. The aim of the survey was to obtain information about nurses’ perceptions of the usability of the EHR, the organizational context, their individual nurse characteristics and their use of the system. Phase two involved focus groups to better understand the findings identified in the survey. Multivariable and hierarchical linear regression was conducted. A multivariable model made up of the variables ease of use, navigation and impact on workload, explained 13% of the variance in nurses’ perceptions of EHR use, however navigation was the only significant predictor in the model. In the data from the focus groups, nurses described how they navigated through the EHR, and which functionalities supported or hindered their use of it. Results of this study provide insights into factors that may influence nurses’ use of EHRs in an acute care hospital setting that have implications for research, nurse leaders, vendors, healthcare settings and nursing practice.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".