Connected Care: A co-designed community-led pathway for early detection and intervention for chronic obstructive pulmonary disease
Bibliographic record
Abstract
Background: We developed a community pathway for chronic obstructive pulmonary disease (COPD), one of the most prevalent chronic respiratory conditions in Canada. We had the unique opportunity to co-develop a pathway that truly reflects health status and care continuum perspectives and strengths in COPD. Approach:COPD is one of the most common reasons for hospital admission in Canada, and in Ontario accounts for up to one-third of all health care utilization. COPD is also one of the most common comorbidities in the community.During this presentation, the audience will learn how Connected Care in partnership with the University of Toronto Division of Respirology and Toronto Paramedic Services Community Paramedicine developed a comprehensive COPD pathway for residents of the Greater Toronto Area (GTA). We will also demonstrate how these groups worked together to co-design a pathway, and will share interesting findings from this complex patient population.Planning involved bringing key partners and stakeholders together in the engagement phase to better understand the target patient population we proposed to screen for COPD. Leveraging existing resources was key given the abundance of information about the COPD journey and the testing required for this patient population. The clinical team, including nurse practitioners, respiratory therapists and Respirologists were instrumental in the design of the pathway, providing input and clinical guidance. Throughout the pathway pilot phase, we will continue to revise the process based on both clinical and patient feedback.This pathway will provide earlier access to treatment for community residents with diagnosed and un-diagnosed COPD that are often missed, preventing unnecessary 9 calls, Emergency Department visits, and hospital admissions. Results:The COPD Pathway aims to keep residents in their community and manage symptoms to prevent unnecessary 9 calls, visits to the Emergency Department, and hospitalization. The pathway has multiple services and streams of follow-up based on the patients needs: ) Proactive outreach and screening in high priority settings such as Naturally Occurring Retirement Communities (NORCs) with an emphasis on prevention and health promotion in addition to intervention;a. NORCs present an opportunity to keep people healthy; this was an opportunity to not re-invent the wheel but bring together evidence-based models that are working to be even greater than the sum of its parts2) Leveraging the experience and scope of various roles to create an effective pathway3) Patients are treated based on the pathway that suits their needsa. We have an escalation of care option where a nurse practitioner is able to refer patients living with moderate to severe COPD to a Respirologist4) Patients receive ongoing monitoring and care5) Patients are reintegrated into community and able to self-manage their condition.The pilot was initiated in April 2024 and residents were identified through Paramedic Led Wellness Clinics. So far, we have conducted four Wellness Clinics, where 95 residents have been screened, and 0 referrals to the pathway have been generated. We will provide initial results and impacts at the conference. Implications: Through this pathway, our highly engaged team is: Detecting residents who may be living with undiagnosed COPD earlier than otherwise possible Monitoring existing COPD patients to reduce exacerbation/unnecessary 9 calls/Emergency Department visits Providing seamless care from community to hospital In addition, we have discovered that we are screening not only patients who may screen positive for COPD through spirometry testing, but also patients who fall outside this pathway but equally require a more comprehensive assessment for another respiratory condition, and we therefore are providing our findings back to their primary care provider for further investigations and treatment.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".