Collectively committing to improved care and outcomes: Fostering an environment of trust, collaboration and accountability
Bibliographic record
Abstract
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.
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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 | 0.055 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.035 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".