"No place like home: impact of the House Calls model on ED visits, hospitalizations and time in the community"
Bibliographic record
Abstract
Home-based primary care in Canada is currently greatly under-powered, despite an aging population, an increasing proportion of homebound older adults,1 and 100% of Canadians hoping to age at home.2 Homebound older adults in Canada have increased comorbidities, medications and mental diagnoses compared to their non-homebound peers.3,4 They have difficulty accessing office-based care due to a combination of physical, mental and social frailty, resulting in higher emergency department (ED) visits and hospitalization rates.4 Our team, House Calls, reaches homebound adults with further identities that may impact equitable care, including racialized older adults, newcomers, those with English as a second language, adults with disabilities and the precariously housed. House Calls is a unique not-for-profit home-based primary care team in Toronto, Canada. It was founded in 2007 through consultation with local older adults, caregivers and service providers identifying the type of services that mattered most to their care. It was further developed in collaboration with local community partners. The team is a health worker-led multidisciplinary team, with physicians, nurse practitioners, occupational therapists, physiotherapists, social workers and administrators working together in a horizontal, trust-rooted leadership structure. Through regular intra-team consultation and annual patient/caregiver surveys, the team evolves its operational structure responsively to patient and community needs. The goal of House Calls is to help homebound older adults age and die in place, through a three-pronged program: geriatric-focused interdisciplinary primary care, urgent care support and home-based palliative care. We designed a retrospective cohort study to assess rates of hospitalization, ED visits and time spent in the community for House Calls patients from January 1, 2015 to January 1, 2022, when compared to similarly frail, homebound older adults in the same geographic areas. The study will also measure House Calls program implementation, through rates of interprofessional and urgent care visits. Study results are pending. We hypothesize that this home-based primary care model results in reduced ED visits and hospitalizations, and more successful aging at home as measured by time spent in the community. Through the description of this program and its evaluation, we hope to contribute valuable, evidence-based knowledge to inform the care of older adults, in a challenging international climate of aging populations, limited research and often stigma-based funding challenges. We hope to use our results to improve the quality of care offered at House Calls, and inform wider implementation of similarly structured home-based primary care teams. References Lapointe-Shaw L, Jones A, Ivers NM, Rahim A, Babe G, Stall NM, Sinha SK, & Costa AP. Homebound status among older adult home care recipients in Ontario, Canada. J Am Geriatr Soc 2022; 70(2):568–578. Sinha, S.,Nolan, M. Bringing long-term care home. NIA. Nov 2020. Qiu, W. Q., Dean, M., Liu, T., George, L., Gann, M., Cohen, J. Bruce, M. L. Physical and mental health of homebound older adults: an overlooked population. J Am Geriatr Soc 2010; 58(12):2423–2428. Musich, S., Wang, S. S., Hawkins, K., Yeh, C. S. Homebound older adults: Prevalence, characteristics, health care utilization and quality of care. Geriatr Nurs. 2015; 36(6):445–450.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".