Collectively committing to improved care and outcomes: Fostering an environment of trust, collaboration and accountability
Notice bibliographique
Résumé
Background: Ontario, like other provinces, continues to face challenges caring for patients within the current system; hospitals in the region seeing increasing occupancy rates that are regularly above 00%. With significant bed pressures due to increasing acuity and complexity of patients these system pressures compels us to accelerate integrated models of care. However, implementing rapid system wide changes to deliver care is complicated and challenging in a siloed system. University Health Network (UHN), Canada's largest research and education health system, launched an Integrated Care Program in 209. This model of care streamlines and breaks down barriers to provide better care experiences for patients, essential care partners and providers with improved outcomes while also creating much needed in-patient bed capacity resulting from lowering hospital lengths of stay at hospital as well as preventing avoidable Emergency Department revisits and readmissions. Methods/Results: Critical to the success of this system-wide change was the shared vision and collective commitment of patients, essential care partners and practitioners from acute care, home care and community paramedicine. Investing time in creating a true one team is the foundation for sustainable change towards integrated care but continues to remain elusive to many teams. This presentation speaks to the key elements that fostered the environment for trust, collaboration and accountability for a program that over five years has delivered 7 pathways across surgery, medicine and transplant benefiting ~4,000 patients annually.Strategies for success include the following: Guiding Principles to support ongoing decision-making Co-creation of pathways with a view to advance care at home Collective commitment to standards Clear accountabilities with aligned incentives; Use of quantitative and qualitative feedback to learn from success and failureThis approach has led to the following benefits: ) Delivering the right care at the right time - Improved connectedness and communication amongst care providers fosters and supports a one care team approach with a focus on where care is best delivered. This has resulted in a significant impact to reduce ED (Emergency Department) visits and hospital admissions. 2) Faster Access to Care - ability to create acute care capacity and accelerate recovery by decreasing total lengths of stay and hospital readmissions.3) Ability to hire/retain more health care workers - Build teams to work to their full scope of practice, create new models of education and training and move away from historical pay per visit care.By removing siloes care providers have gained a better understanding of care experiences across the continuum and a better appreciate for challenges across environments. By enabling collaboration teams have been able to ongoing identify opportunities to advance care at home and increasingly support complex patients. Conclusion and Next Steps: Rapid and enterprise wide change is possible, and with a shared vision and commitment, can deliver both meaningful and sustainable change to for patients, essential care partners, and care providers.
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 | 0,055 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,022 | 0,035 |
| Communication savante | 0,024 | 0,012 |
| Science ouverte | 0,004 | 0,037 |
| Intégrité de la recherche | 0,005 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».