East Toronto Health Partners: Collaborating on Quality Improvement
Notice bibliographique
Résumé
Developing a shared quality agenda has led to a new focus on population health within our community in East Toronto, Ontario, Canada. Amidst pandemic recovery, community members often struggle to access care. Health care organizations are challenged to support patient flow and mental health needs, and to catch up on preventative care and chronic disease management that was delayed due to reduced in-person care. One might think that these pressures would sideline quality improvement (QI) work, however we’ve seen new energy generated by QI capacity-building and application of rapid learning cycles to address our shared challenges. Ontario Health Team context An Ontario Health Team is “a group of collaborative partners (organizations and individuals who offer health services, social services, and other health-relevant activities) brought together under a collaborative governance framework… to work with, and be accountable for, the health and wellbeing of a well-defined Attributed Population.” This context generates motivation for organizations and stakeholders to work closely on quality initiatives. Quality improvement activities form part of a larger rapid-learning health system strategy that includes program evaluation, evidence-based best practices in frontline care, and business intelligence efforts. Approaches within our QI Collaboration -Collecting and sharing patient experience measures as part of Best Practice Guideline implementation for Person & Family-Centred Care; -Strengthening involvement of community members; co-creating our 2022 collaborative Quality Improvement Plan with diverse partners; -Accessing and reflecting on baseline data for our defined population, and building capacity for local analytics. Together with other Ontario Health Teams, we have outlined requirements for data and tools to support population health management; -Focusing on priority neighbourhoods to improve health equity; and building QI capacity with teams of Community Health Ambassadors within those neighbourhoods. Key Learnings Patient Experience: We grounded our work in person- and family-centred care; respecting the voices of patients and caregivers in reporting their level of involvement in care plans and treatment. Collaboration among partners uncovered examples of best practice in patient experience data collection and moved us toward data collection as an integrated system of care (beyond surveys conducted within individual organizations). Patient Flow: Our collaboration led us to focus “upstream,” and to strengthen our ability to identify unmet needs for clients in community settings. A strengths-based assessment across collaborating organizations helped to increase awareness of diverse assets within our community and examples of leadership in Seniors-Friendly Care. We are launching transdisciplinary teams dedicated to responsive support for transitions in care. Preventative Care: An equity approach was key. In addition to primary care practice-based improvement initiatives (e.g. cancer screening) we prioritized efforts to reach vulnerable groups, such as individuals without a family doctor, and those with barriers to navigating health services in English. Rapid-cycle improvement and evaluation plans (co-designed with community members) are helping to ensure that service offerings are addressing the needs of the community. RESULT: The East Toronto Health Partners’ collaborative Quality Improvement work has helped our Ontario Health Team to align and integrate work across multiple organizations – building our capabilities for proactive population health management.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,061 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,012 | 0,006 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,005 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,048 | 0,010 |
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
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Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».