Transforming Healthcare: Calgary Foothills' Journey Towards Patient-Centric Integrated Community Care
Bibliographic record
Abstract
Integrated care thrives when built around the patient, not on them navigating the system. This principle guided Calgary Foothills Primary Care Network's case collaborative program, initiated over five years ago. Approximately 20% of Canadians face mental health or addiction issues annually, and 57% seek initial professional help from family doctors. The intricacies of modern healthcare demand provider collaboration, but a structured framework is lacking. Feedback from patients and families illuminated challenges in navigating resources and coordinating care. In response, the case collaborative initiative emerged, uniting doctors, healthcare professionals, schools, communities, and government services. This collaborative model addresses the complex health, psychological, and social needs of patients. Co-designed by the Primary Care Network and community partners, with input from patient advisors, it aligns with best practices like patient-centered care, team-based care, population-focused care, evidence-based, and quality improvement. The successful implementation of four case collaboratives has addressed 223 cases, focusing on concerns such as a lack of family resources, coordination of care, housing, financial issues, and social isolation. Evaluation results show that 85% of providers believe the model facilitated timely connections to resources, 91% believe it enhanced patients' quality of care, and 100% felt empowered to provide care. Patients reported feeling better supported, emphasizing the collaborative team's significant impact. The strengthened relationships among providers have not only benefited directly involved patients but have created a ripple effect, enhancing overall service delivery in the community. Our presentation emphasizes integrating primary health care with patients and families as active partners. We'll outline the transition from traditional primary health practices to an integrated community care model, focusing on the co-design of the case collaborative model, key principles, challenges, learnings, and enablers supporting its success. Our goal is to contribute to a primary care collaborative that leads to better health, care, and value. Our future steps include enhancing patient engagement, transitioning to an inclusive model where families serve as primary referrers, and scaling the model provincially, nationally, and internationally.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 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".