Connected Care Hub: Filling a gap with virtual transitional care and decreasing Emergency Department visits and inpatient readmissions.
Bibliographic record
Abstract
Background: In April 2020, COVID-19 was filling Emergency Departments (EDs). The University Health Network (UHN) identified a need for coordinated care of COVID-19 patients outside the ED to reduce overcapacity burden, safely care for infected individuals, and reduce spread within UHN hospitals. The Connected Care Hub was launched with the goal of reducing this burden by providing integrated, holistic, timely and equitable access to quality patient care. A virtual clinic led by Nurse Practitioners, provided comprehensive assessment, diagnosis, and treatment of COVID-19 patients, along with close ongoing follow-up until symptoms improved. It has since expanded beyond COVID-19 to provide timely, effective care for respiratory and transitional care needs. The Hub works with home care providers, community pharmacy, specialists, and primary care providers to better support patients and address gaps in transitional care. Population/Engagement: Co-developed by partners from public health, government, acute, primary, home and community care, and involved partnership with local public health units as well as regional and provincial levels of governments. The Hub works closely with inter-professional teams including transplant, oncology, infectious disease, internal medicine, ED, primary care providers, and community care in order to maintain and spread current knowledge, and ensure seamless care and effective transitions for these populations. The Hub serves a variety of high risk patients across Ontario, including patients with COVID-19, RSV, influenza, and pneumonia. Most respiratory patients come from EDs, Transplant and Oncology units and community clinics. Additionally, the Hub serves patients transitioning home from hospital (including CHF, COPD, diabetes). Continual monitoring and feedback from Hub NPs, patients, referring clinicians, community and primary care providers informs Hub improvements and innovations. Having a diversity of opinions, expertise, and lived experience around the table led to greater creativity, innovation, critical analysis, and strength of solutions where they are most needed. Intervention: The Hub virtual clinic provides comprehensive care for ~14 days, with support including rapid initial assessment and treatment (e.g. therapeutics), ongoing monitoring and timely access to specialists, including links to primary and home and community care, rehabilitation and psychosocial supports. Results/Impact: In the last year 2,000+ patients have benefited in being cared for at home while maintaining timely access to acute care when needed. Assuming each patient would have gone to ED, we averted ~2,000 unnecessary ED visits and prevented a minimum of 830 inpatient bed days. The Hub provides: Better patient outcomes by support of one coordinated team with a central point of contact Comprehensive care by NPs, including collaboration with community supports Lessen patient anxiety and improve self-care through timely access, continuity of care and education for self-management Equitable and accessible care, complimenting public health efforts Learnings/Next Steps: Many neighbourhoods in Toronto do not have access to this comprehensive care and this is critical to equitable access and outcomes for patients. As we expand to new populations, continued collaboration with community and patients, along with additional NP education will be critical. During this presentation, we will discuss how this model can be leveraged across multiple pathways using our principles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".