The Association Between Using a Mobile Version of an Electronic Health Record and the Well-Being of Nurses: Cross-sectional Survey Study
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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