The Association Between Using a Mobile Version of an Electronic Health Record and the Well-Being of Nurses: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Mobile devices such as tablets and smartphones are increasingly being used in health care in many developed countries. Nurses form the largest group in health care that uses electronic health records (EHRs) and their mobile versions. Mobile devices are suggested to promote nurses' workflow, constant updating of patient information, and improve the communication within the health care team. However, little is known about their effect on nurses' well-being. OBJECTIVE: This study aimed to examine the association between using a mobile version of the EHR and nurses' perceived time pressure, stress related to information systems, and self-rated stress. Moreover, we examined whether mobile device use modifies the associations of EHR usability (ease of use and technical quality), experience in using EHRs, and number of systems in daily use with these well-being indicators. METHODS: This was a cross-sectional population-based survey study among 3610 Finnish registered nurses gathered in 2020. The aforesaid associations were examined using analyses of covariance and logistic regression adjusted for age, gender, and employment sector (hospital, primary care, social service, and other). RESULTS: =6.45, P=.01) were significant for stress related to information systems. Inexperience in using EHRs, low levels of ease of use, and technical quality were associated with higher stress related to information systems and this association was more pronounced among those who used mobile devices. That is, the highest levels of stress related to information systems were perceived among those who used mobile devices as well as among inexperienced EHR users or those who perceived usability problems in their EHRs. CONCLUSIONS: According to our results, it seems that at present mobile device use is not beneficial for the nurses' well-being. In addition, mobile device use seems to intensify the negative effects of usability issues related to EHRs. In particular, inexperienced users of EHRs seem to be at a disadvantage when using mobile devices. Thus, we suggest that EHRs and their mobile versions should be improved such that they would be easier to use and would better support the nurses' workflow (eg, improvements to problems related to small display, user interface, and data entry). Moreover, additional training on EHRs, their mobile versions, and workflow related to these should be provided to nurses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".