Understanding the Association Between Electronic Health Record Satisfaction and the Well-Being of Nurses: Survey Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background Intensive care unit (ICU) nurses experience high levels of burnout related to the high-stress environment. Management of electronic health records (EHR) is a contributing factor to physician burnout. However, limited research has established the relationship between the nurse’s well-being and EHR use. Objective The objective of this study was to examine the association between EHR use and the well-being of nurses. Methods We surveyed registered nurses employed at a major Southeastern medical center in the United States about their demographics, experience with EHRs, satisfaction with EHRs, and elements of well-being. The correlation between subgroup demographics and survey questions was examined using Kendall and Fisher tests. Results A total of 113 ICU registered nurses responded to the survey, of which 93 (82.3%) were females. The population had a mean age of 35.18 years (SD 10.65). A significant association was found between satisfaction and well-being scores, where higher EHR satisfaction was associated with higher self-reported well-being (correlation 0.35, P<.001). Nurses who were unhappy with the time spent in EHR use compared with direct patient care reported higher levels of stress (P<.001) and isolation (P=.009). Older nurses reported higher dissatisfaction with the amount of time spent on EHR tasks related to direct patient care compared to younger nurses (P<.001). Conclusions Although nurses reported acceptable satisfaction scores with EHR use, deeper analysis suggests that EHR indirectly affects the well-being of nurses. These findings strongly indicate that lower EHR satisfaction can impact the well-being of nurses. More research is needed to optimize the nurse-EHR experience through more user-centered design approaches.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it