Seamless Support: Improving Health Outcomes in Long-Term Care Through System Integration and Partnerships
Bibliographic record
Abstract
Background: Increased acuity and complexity of long-term care (LTC) residents along with chronic underfunding has led to gaps in the delivery of health care services within LTC homes. In response, the integrated care system in North York, Ontario, has facilitated a partnership between the local hospital and LTC homes in the region to improve access and integration under a single program called North York Congregate Access and Support Team (NY CAST). This oral presentation will highlight our experiences creating and leading the NY CAST program, along with data on numerous quality indicators. Approach: In June 2023, a group of 88 representatives including health system planners, hospital and LTC representatives, and patient partners came together for a workshop to collaboratively design an initiative that would leveraging existing and new resources to better support local LTC homes. The North York Congregate Access and Support Team (NY CAST) model that we developed provides a variety of supports for LTC homes, including:. Nurse-Led Outreach Team. A specialized team of Advanced Practice Nurses provides remote and on-site support to LTC homes through direct provision of care, advice on care management, and capacity building activities to train LTC staff.2. Palliative Care Consults work together with the LTC care team to provide suggestions for timely symptom management, advance care planning, psychosocial, social, and spiritual support, with a focus on early integration of palliative care rather than just end of life care.3. Digital Health. All program services can be accessed via the SCOPE (Seamless Care Optimizing the Patient Experience) digital line, allowing for a one stop shop for practitioners in LTC. SCOPE connects the LTC care team to a nurse navigator, who can assist by connecting to acute care or community support services.The NY CAST program also brings together patient partners, community and hospital staff leaders, and LTC staff leaders for monthly Collaborative meetings to discuss issues and implement shared quality improvement initiatives.Experience to DateAs part of its first phase launched in July 2023, NYCAST has engaged with eight long-term care homes in North York. The Nurse-Led Outreach team has educated 25% of all staff across the 8 participating LTC homes and had over 500 encounters with LTC residents, leading to a measurable decrease in ED visits. In-person Palliative Care Consultations have been provided to over 50 LTC residents, who have subsequently experienced fewer hospital transfers or in-hospital deaths. In total, the SCOPE digital line has received over 00 calls for services.Through the first 9 months of the NY CAST program, relationships have been developed between the hospital, LTC, and patient partners, allowing for us to work collaboratively on difficult issues and develop shared priorities for the year to come. Implications: The NYCAST model is an alliance-based approach to building a model of sustainable integrated care. Most of the service changes within the program have been enabled through relationship building between sectors and a collaborative quality improvement approach. Although there are always challenges to new alliances and partnerships, dedicated champions can help overcome these concerns and build trusting relationships that allow for collaborative, solution-focused improvement across healthcare sectors.
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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 | 0.014 | 0.020 |
| 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 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| 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".