Advancing Primary Care Coalitions and Cultivating a People-Centered Healthcare System with SCOPE
Notice bibliographique
Résumé
Primary Care is widely acknowledged as the cornerstone for authentic system integration in healthcare (Ontario Ministry of Health, 2022). Historically, however, collaboration with Primary Care has been hampered by fragmentation due to the absence of a unified Primary Care organization (Misra et al., 2020). Enter the Seamless Care Optimizing Patient Experience (SCOPE), a pioneering virtual interdisciplinary team designed to foster relationships and trust within the broader healthcare system. SCOPE functions as a barometer, alerting stakeholders to the specific needs of Primary Care. This article explores SCOPE's innovative role in connecting solo and small group practice primary care providers (PCPs), hospitals, and communities in Ontario. The effectiveness of team-based primary care models like SCOPE in enhancing patient outcomes and reducing Emergency Department visits and avoidable hospitalizations is substantiated by robust evidence (Pariser et al., 2020). The grassroots development of SCOPE involves a strategic partnership with Sunnybrook hospital, leveraging its support to address the unique needs and barriers faced by local PCPs. This includes enhancing access to specialists, home care, and system navigation. Detailed tracking of engagement with Sunnybrook SCOPE informs continuous improvement and expansion of SCOPE pathways. Communication channels, such as phone, email, social media, and office visits, facilitate comprehensive insight into PCPs' workflows and administrative challenges. Continuous feedback mechanisms, including surveys, interviews, and regular meetings with the Physician Advisory Group (PAG), contribute significantly to shaping SCOPE's evolution. As SCOPE expands across Ontario, gaps in standardized core offerings become apparent, necessitating a nuanced understanding of local contexts and Primary Care needs. This program evaluation employs Quality Improvement (QI) surveys and interviews to glean insights from both Sunnybrook SCOPE users and their patients. These highlight SCOPE's efficacy in addressing care needs while pinpointing areas for further investigation. QI interviews with SCOPE PCPs delve into reasons behind pathway usage, identify care barriers, and propose enhancements for future utilization. Findings emphasize the importance of bi-directional communication and co-design with stakeholders. Recognizing the absence of a universal approach to health system integration with Primary Care, SCOPE's commitment to adapting to local contexts makes it an adaptable model for international audiences. SCOPE's commitment to building trust through a community of practice, service-oriented approaches, and regular communication positions it as a progressive force in advancing primary care (Pariser et al., 2020). The next phase involves standardizing core elements across SCOPE sites and establishing a platform for PCPs to openly share challenges, fostering an environment of honest collaboration. In conclusion, SCOPE stands as a transformative initiative, navigating the intricate landscape of Primary Care, fostering collaboration, and contributing to the evolution of a people-centered healthcare system.
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 | 0,026 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,021 | 0,026 |
| Communication savante | 0,016 | 0,010 |
| Science ouverte | 0,003 | 0,047 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».