Creating the conditions to advance integrated care: University Health Network’s journey to change how care is experienced in Toronto. 
Notice bibliographique
Résumé
Background - With a population of 6.3 million residents, Toronto stands as a multicultural centre that like many other health “systems,” face challenges with care that is fragmented with the burden of care navigation and coordination falling to individuals and caregivers. UHN one of Canada’s largest healthcare organizations tasked its Connected Care team with a mandate to address system-wide issues related to communication, continuity of care and lack of personalized care and coordination. Objective - This paper delves into the leadership insights gained by UHN Connected Care over the last five years, highlighting the collaborative efforts involving patients, care partners, and healthcare providers. Taking advantage of pressing health system needs learnings come from addressing poor patient experiences, provider burnout, capacity issues, and the strategic response to the COVID-19 pandemic. Methods - In 2019, the Connected Care team initiated an integrated care approach, implementing a collective impact strategy that brought together patients, care partners, local and regional providers, and funders. The methodology was built on key pillars: Backbone support – centralized supports and dedicated team Opportunistic interventions – work together on pressing shared concerns Partnership and accountability – leverage and recognize areas of expertise Co-create – incremental solutions developed together Recognition of all voices – provide opportunities for all leaders Patient partners led all aspects of planning, delivery and evaluation and by offering varying levels of commitment help support participation and representation. Patients could be involved in interviews to support specific care pathways, to leading the development of a minimum patient experience data set, to longer-term commitments on working groups and committees. Results - Over the course of five years, the initiative expanded its reach from addressing issues within a surgical division to growing city-wide pathways, positively impacting the lives of 35,000 individuals. The outcomes included a reduction in emergency department visits, hospital stays, and surgical backlogs. This not only improved patient satisfaction but also bolstered the overall capacity of the healthcare system. The collaborative efforts extended across the care continuum to encompass primary, acute, and homecare teams, as well as community paramedicine, pharmacy services, and various social support organizations. The relationships and trust built across the city have created an integrated health and social network that continuously leads and learns together. Conclusion and Next Steps - Future efforts to scale and spread integrated care through community partnerships will continue to increasingly support population health with a more concerted effort to provide much need integration with new partners to address social determinant of health supports. The team is also now embarking on an ambitious multi-year strategy to develop an integrated care digital platform. One critical area of growth has been initiated to explore the potential of aging in place program with a focus on community-led interventions that will support hyper-local needs and expand service provider partnerships.
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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 | 0,008 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,029 | 0,012 |
| Communication savante | 0,013 | 0,007 |
| Science ouverte | 0,003 | 0,020 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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 ».