Mobile Technologies: Expectancy, Usage, and Acceptance of Clinical Staff and Patients at a University Medical Center
Notice bibliographique
Résumé
BACKGROUND: Despite their increasing popularity, little is known about how users perceive mobile devices such as smartphones and tablet PCs in medical contexts. Available studies are often restricted to evaluating the success of specific interventions and do not adequately cover the users' basic attitudes, for example, their expectations or concerns toward using mobile devices in medical settings. OBJECTIVE: The objective of the study was to obtain a comprehensive picture, both from the perspective of the patients, as well as the doctors, regarding the use and acceptance of mobile devices within medical contexts in general well as the perceived challenges when introducing the technology. METHODS: Doctors working at Hannover Medical School (206/1151, response 17.90%), as well as patients being admitted to this facility (213/279, utilization 76.3%) were surveyed about their acceptance and use of mobile devices in medical settings. Regarding demographics, both samples were representative of the respective study population. GNU R (version 3.1.1) was used for statistical testing. Fisher's exact test, two-sided, alpha=.05 with Monte Carlo approximation, 2000 replicates, was applied to determine dependencies between two variables. RESULTS: The majority of participants already own mobile devices (doctors, 168/206, 81.6%; patients, 110/213, 51.6%). For doctors, use in a professional context does not depend on age (P=.66), professional experience (P=.80), or function (P=.34); gender was a factor (P=.009), and use was more common among male (61/135, 45.2%) than female doctors (17/67, 25%). A correlation between use of mobile devices and age (P=.001) as well as education (P=.002) was seen for patients. Minor differences regarding how mobile devices are perceived in sensitive medical contexts mostly relate to data security, patients are more critical of the devices being used for storing and processing patient data; every fifth patient opposed this, but nevertheless, 4.8% of doctors (10/206) use their devices for this purpose. Both groups voiced only minor concerns about the credibility of the provided content or the technical reliability of the devices. While 8.3% of the doctors (17/206) avoided use during patient contact because they thought patients might be unfamiliar with the devices, (25/213) 11.7% of patients expressed concerns about the technology being too complicated to be used in a health context. CONCLUSIONS: Differences in how patients and doctors perceive the use of mobile devices can be attributed to age and level of education; these factors are often mentioned as contributors of the problems with (mobile) technologies. To fully realize the potential of mobile technologies in a health care context, the needs of both the elderly as well as those who are educationally disadvantaged need to be carefully addressed in all strategies relating to mobile technology in a health context.
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Comment cette classification a été obtenuedéplier
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».