Primary care physicians' experiences with electronic medical records: implementation experience in community, urban, hospital, and academic family medicine.
Notice bibliographique
Résumé
OBJECTIVE: To understand how remuneration and care setting affect the implementation of electronic medical records (EMRs). DESIGN: Semistructured interviews were used to illicit descriptions from community-based family physicians (paid on a fee-for-service basis) and from urban, hospital, and academic family physicians (remunerated via alternative payment models or sessional pay for activities pertaining to EMR implementation). SETTING: Small suburban community and large urban-, hospital-, and academic-based family medicine clinics in Alberta. All participants were supported by a jurisdictional EMR certification funding mechanism. PARTICIPANTS: Physicians who practised in 1 or a combination of the above settings and had experience implementing and using EMRs. METHODS: Purposive and maximum variation sampling was used to obtain descriptive data from key informants through individually conducted semistructured interviews. The interview guide, which was developed from key findings of our previous literature review, was used in a previous study of community-based family physicians on this same topic. Field notes were analyzed to generate themes through a comparative immersion approach. MAIN FINDINGS: Physicians in urban, hospital, and academic settings leverage professional working relationships to investigate EMRs, a resource not available to community physicians. Physicians in urban, hospital, and academic settings work in larger interdisciplinary teams with a greater need for interdisciplinary care coordination, EMR training, and technical support. These practices were able to support the cost of project management or technical support resources. These physicians followed a planned system rollout approach compared with community physicians who installed their systems quickly and required users to transition to the new system immediately. Electronic medical records did not increase, or decrease, patient throughput. Physicians developed ways of including patients in the note-taking process. CONCLUSION: We studied physicians' procurement approaches under various payment models. Our findings do not suggest that one remuneration approach supports EMR adoption any more than another. Rather, this study suggests that stronger physician professional networks used in information gathering, more complete training, and in-house technical support might be more influential than remuneration in facilitating the EMR adoption experience.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,000 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,005 |
| 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 ».