The MGH2Home Program, a Seamless Journey from Hospital to Home
Bibliographic record
Abstract
Overview: The MGH2Home program is a novel hospital-to-home initiative. MGH2Home brings together multiple community service agencies to create a single integrated care team empowered to create and adapt care plans directly with patients and their families with the goal of reducing hospital length of stay, preventing avoidable readmission, and improving the care experience for patients and providers. Community Served: An essential aspect of the program is its commitment to improving population health within East Toronto. The program targets adults designated as Alternate Level of Care in hospital who require ongoing health and social supports post-discharge with the added focus on determinants of health care planning. MGH2Home enables transitions of patients who would otherwise remain in the hospital. Program Design: Our program actively involves those who understand the community and patient needs. Caregiver and Community Advisory Group members participated in all program design sessions and helped identify barriers and innovative solutions, and they continue to contribute to our Program Steering Committee. MGH2Home leverages the expertise of the hospital and three home and community care services organizations to create a single integrated care team of diverse providers. Prior to discharge from the hospital, the team meets with patients and caregivers to develop a transition care plan encompassing medical, rehabilitation, and social care needs. This care plan follows them into the community and is responsive to changing patient needs. Regular huddles and connection to primary care providers ensure ongoing collaboration once patients are home. In a departure from the traditional home care fee-for-service model that rewards higher patient volumes, our program adopts a new way of working. Program staff are paid using a salary-based model, incentivizing care coordination activities traditionally not funded in a fee-for-service model. Our Successes thus far: In the first eight months of operation, the program has made a significant impact, serving 146 patients. Patients and their families have referred to the program as a "lifeline"during the challenging transition period from hospital to home. Preliminary program data suggests improved patient outcomes while simultaneously preserving much-needed hospital bed capacity. Our Learnings: Our experience underscores the importance of centralized health information records and communication tools for our multi-sectoral, multi-agency team. To support team cohesion, significant attention was required to align partner organizations with policies and procedures. Insights from our Caregiver and Community Advisory Groups highlighted the necessity for a centralized point of contact for patients and families. Consequently, we co-designed communication tools to enhance clarity for patients. To reduce silos between acute care and the community, a program supervisor is on-site in the hospital to create close connections between hospital and community teams. The program supervisor is equipped with data to support nimble decision-making and ensure a safe and timely transition from the hospital that matches the capacity of the community team of providers. Moving Forward: MGH2Home continues to mature, refining our program based on feedback from patients, families, and providers to ensure that MGH2Home remains a beacon of person-centred, seamless care that can be scaled broadly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".