Innovative technology and change management: E-health applications in Canada.
Notice bibliographique
Résumé
Background \nFocusing on the Canadian healthcare system, this study explores factors influencing the adoption of recent specialized technology in e-health applications due to concerns about the allocation of economic resources and governmental policy formulation. This study focuses on the specific technologies of the Electronic Medical Record (EMR)-based-Personal Health Record (PHR) and their use by physicians and residents of Northern Ontario. \nObjectives \nThe primary objective of this study is to understand the interdisciplinary factors that predict Northern residents’ attitude toward EMR-based-PHR innovative technology. Conducting this study also serves to increase awareness of patient-driven e-health in Northern Ontario and provides decision makers with useful quantitative data and strategies to support future initiatives. \nMethods/Materials \nUsing customized data obtained from the National Physician Survey (NPS) in Canada and primary data collected through an adaptation of this survey, a comparative analysis was conducted to understand the electronic patient-physician relationship and explore interdisciplinary factors regarding perception and use of EMR-based-PHR. The data was analyzed using Descriptive Statistics, Z Test for two Population Proportions, ANOVA and Regression Analysis. \nResults \nThe results indicate significant differences between Northern physicians and patients in usage and preference regarding several technological applications. More Northern patients use websites, social media and mobile applications than Northern physicians. In capturing health information, fewer physicians exclusively prefer to use electronic records than use a combination of paper charts and electronic records, and most Northern patients prefer either a combination of both methods or exclusively paper charts in their healthcare. Interdisciplinary factors related to EMR-based-PHR were significant predictors and explained 69.6% of the variance in the behavioral attitude and 74.5% of the variance in the behavioral intention to adopt this innovative technology. \nConclusions. \nEstablishing an electronic patient-physician relationship in the Canadian healthcare system requires coordinated and concerted efforts from all stakeholders involved in this process. Significant cost without benefits is evidence of a misallocation of Canadian resources and requires increased attention. New strategies must address current gaps in educational, technical, managerial, and financial supports. Physician support, however, is ultimately the key to increasing the adoption rate of EMR and fostering positive attitudes toward PHR among the Canadian people.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».