East Toronto Health Partners: Collaborating on Quality Improvement
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".