Regional Integration: Physician Perceptions on Electronic Medical Record Use and Impact in South West Ontario
Notice bibliographique
Résumé
Regional initiatives in the health care context in Canada are typically organized and administered along geographic boundaries or operational units. Regional integration of Electronic Medical Records (EMR) has been continuing across Canadian provinces in recent years, yet the use and impact of regionally integrated EMRs are not routinely assessed and questions remain about their impact on and use in physicians’ practices. Are stated goals of simplifying connections and sharing of electronic health information collected and managed by many health services providers being met? What are physicians’ perspectives on the use and impact of regionally integrated EMR? In this thesis, I examined how primary health care and family physicians use electronic medical records and associated electronic health information resources in South West Ontario, the challenges they face in doing so, as well as the impact of an integrated EMR. A mixed methods-grounded theory research approach was employed to explore physician EMR use, and data acquired using participant consultation, observership and shadowing, semi-structured interviews, and a self-administered questionnaire. The study revealed that there are clear and present challenges to regional integration of EMR. Although regional integration initiatives such as implementation of ClinicalConnect, a regional EMR clinical viewer, continue to expand, physicians face challenges related to implementation, support and advanced use of electronic records. Not every patient has data access, patient portals are often not fully integrated, and the impact of EMR transitioning can reshape a primary care physician practice. A comprehensive model of physician integrated EMR use and a six-stage maturity model were developed from this study: The comprehensive model conceptualizes how the experience of EMR transitioning, managing patient expectation, meeting information needs, engaging regional entities, support and practice context, influence physician perception of EMR integration, and often resulted in practice changing moments. It further describes influences on physician perception of EMR use by EMR offering, EMR content, integration tools, information attributes, practice type, and patient and physician characteristics. The six-stage maturity model provides a framework that describes key elements of operative EMR use within the context of regional integration of electronic health information resources. It enhances understanding of EMR maturity by shifting orientation from theoretical evolutionary improvement path, which characterized prior maturity models, to assessment of EMR maturity based on how practicing physicians actually use EMR in primary health care. Insights from this study will advance understanding of regional integration of electronic medical records and serve as additional resource for individuals interested in assessment of the use and impact of electronic health information resources in primary health care.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».