Creating the conditions to advance integrated care: University Health Network’s journey to change how care is experienced in Toronto. 
Bibliographic record
Abstract
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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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".