Achieving the Quadruple Aim. Evaluating the Impact of an Integrated Senior’s Care Model in Burlington Ontario.
Bibliographic record
Abstract
Background: In the quest to deliver comprehensive care that is both efficient and person-centric, integrated care models have emerged as a vital solution. These models amalgamate health and social care services ensuring that care is accessible, tailored, and sustainable. However, evaluating their effectiveness across the four domains of the quadruple aim framework remains a complex endeavor. In 209, the Ontario Ministry of Health and Long-Term Care unveiled the Ontario Health Teams (OHTs). This groundbreaking integrated care network model was designed to provide seamless, person-centered care throughout an individual's life span. Among the pioneering OHTs was the Burlington OHT, which prioritized integrated senior care models critical consideration given that seniors constitute 25% of Burlington's demographic. Approach: At the heart of these models is the Community Wellness Hub, a collaborative of health and social service entities dedicated to the well-being of seniors. Strategically situated within affordable housing buildings, the Hub extends its services to both residents and the local community. Its mission is to promote a healthy, active lifestyle among its members by preemptively addressing wellness needs and mitigating the risk of health emergencies that necessitate acute care. The Hub represents a confluence of healthcare, housing, and social care, involving 5 distinct organizations. The implementation of the Hub was complimented with a comprehensive evaluation plan that assesses the implementation, performance and impact of the hub. The impact of the Hub across the quadruple aim framework domains (value, person experience, health workforce experience and health outcomes) was assessed using a toolbox of acute and community care data, member and provider experience surveys and health and wellness self-reported measures. Results: The impact can be described as a 4% reduction in non-urgent Emergency Department visits among its members compared to a similar demographic in Ontario. This indicates that Hub members are effectively using the Emergency Department for only those conditions that cannot be managed within the community. Furthermore, the Hub's members experience fewer hospitalizations for conditions that are typically managed outside of hospitals, such as chronic diseases, and when they are hospitalized, their stays are shorter. Projecting these results to a larger scale, if the Hub were to serve 00,000 seniors in Ontario, it could potentially save the healthcare system approximately $89.72 million annually, based on current hospitalization costs for these conditions. The hub members reported positive experiences in the domains of person-centeredness, enhanced access, safety and connectedness. The hub providers described positive experiences with areas of collaboration and teamwork, enhanced access and navigation, and ease of decision-making. The hub members described improved self-reported health and wellness outcomes and longer tenancy length at the affordable housing units than non-members. Implications: Given the complexity of assessing the impact of complex integrated care interventions. The evaluation approach (methodology and tools) utilized to showcase the Hub impact can be used in similar interventions.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".