The relationship between nurses' input into electronic health record use and nurses' burnout and satisfaction
Bibliographic record
Abstract
Background: The electronic health record (EHR) has become a critical part of an ever-changing healthcare system. However, recent research suggests that EHRs contribute to nurses’ burnout and nurses’ overall dissatisfaction with the EHR. While literature remains inconclusive regarding strategies that can be used to mitigate EHR-related burnout and improved EHR satisfaction, some studies suggest that clinician input into the use of EHRs might improve these outcomes. There are two types of EHRs, fully electronic systems that do not utilize paper charts, and hybrid systems which utilize a combination of paper and electronic documentation. This study aimed to explore the relationship between nurses’ input into the use of EHR systems, nurses’ burnout, and nurses’ satisfaction with their EHR for nurses using hybrid or fully electronic EHRs in a Canadian context. Methods: This descriptive study utilized a secondary analysis of data collected through a survey conducted by Canada Health Infoway in 2020. The target population included 715 clinical nurses working in direct patient care settings, who use either hybrid or fully electronic EHR systems. Results: Overall, nurses who had input into the use of their EHR system had lower levels of burnout and higher levels of satisfaction. Nurses using either hybrid or fully electronic systems, who did not have input into their EHR system had higher levels of burnout and lower levels of system satisfaction. Nurses using either hybrid or fully electronic systems and who had input into their EHR system had significantly lower levels of burnout and higher levels of satisfaction. Implications: These results address the gap in literature where the input of Canadian nurses into the use and design of EHR is largely not discussed. Leaders in health informatics are urged to consider nurse input towards the use of EHRs in Canadian healthcare settings. Further research is needed to control for external factors that may impact these results on a local level. These findings will also assist Canada Health Infoway in the development of future national surveys.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".