Factors Affecting General Practitioners’ Readiness to Accept and Use an Electronic Health Record System in the Republic of North Macedonia: A National Survey of General Practitioners
Notice bibliographique
Résumé
Background Electronic health records (EHRs) represent an important aspect of digital health care, and to promote their use further, we need to better understand the drivers of their acceptance among health care professionals. EHRs are not simple computer applications; they should be considered as a highly integrated set of systems. Technology acceptance theories can be used to better understand users’ intentions to use EHRs. It is recommended to assess factors that determine the future acceptance of a system before it is implemented. Objective This study uses a modified version of the Unified Theory of Acceptance and Use of Technology with the aim of examining the factors associated with intentions to use an EHR application among general practitioners (GPs) in the Republic of North Macedonia, a country that has been underrepresented in extant literature. More specifically, this study aims to assess the role of technology acceptance predictors such as performance expectancy, effort expectancy, social influence, facilitating conditions, job relevance, descriptive norms, and satisfaction with existing eHealth systems already implemented in the country. Methods A web-based invitation was sent to 1174 GPs, of whom 458 completed the questionnaire (response rate=40.2%). The research instrument assessed performance expectancy, effort expectancy, facilitating conditions, and social influence in relation to the GPs’ intentions to use future EHR systems. Job relevance, descriptive norms, satisfaction with currently used eHealth systems in the country, and computer/internet use were also measured. Results Hierarchical linear regression analysis showed that effort expectancy, descriptive norms, social influence, facilitating conditions, and job relevance were significantly associated with intentions to use the future EHR system, but performance expectance was not. Multiple mediation modeling analyses further showed that social influence (z=2.64; P<.001), facilitating conditions (z=4.54; P<.001), descriptive norms (z=4.91; P<.001), and effort expectancy (z=5.81; P=.008) mediated the association between job relevance and intentions. Finally, moderated regression analysis showed that the association between social influence and usage intention was significantly moderated (P=.02) by experience (Bexperience×social influence=.005; 95% CI 0.001 to 0.010; β=.080). In addition, the association between social influence and intentions was significantly moderated (P=.02) by age (Bage×social influence=.005; 95% CI 0.001 to 0.010; β=.077). Conclusions Expectations of less effort in using EHRs and perceptions on supportive infrastructures for enabling EHR use were significantly associated with the greater acceptance of EHRs among GPs. Social norms were also associated with intentions, even more so among older GPs and those with less work experience. The theoretical and practical implications of these findings are also discussed.
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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 | 0,009 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».