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Record W4413358966 · doi:10.5334/ijic.nacic24085

Hospital at Home in Calgary, Canada: An opportunity for real-world care planning and integration.

2025· article· en· W4413358966 on OpenAlexaboutno aff
Michelle Grinman, Juhina El-Hajj, Ryan Kozicky, Thomas Mullie, Jillian Walsh

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrated careNursingMedicineHealth careEconomic growth

Abstract

fetched live from OpenAlex

Introduction: The Complex Care Hub (CCH) is a hospital at home model for older adults and patients with complex medical conditions. As a collaboration between hospital physicians, nurses, pharmacists, community paramedics and home care, CCH provides substitutive acute care in patients' homes, leveraging a hybrid of in-person and virtual modalities. MethodsCCH was co-designed with an inter-professional, inter-organizational team spanning the healthcare system in Calgary, Canada. This included two Calgary acute care hospitals (including their General Internal Medicine and Family Medicine Hospitalist physician groups, Emergency Department, Radiology, Laboratory, Pharmacy and Day Medicine Clinics), Community Paramedics, Transition Services, Home Care and Primary Care in Calgary, Canada. Throughout this process we also involved patient advisors to support development of materials and processes impacting patient care and transitions across the system.The program evaluation leveraged the Quapruple Aim framework via a multi-methods study that included patient, caregiver and provider surveys and interviews, as well as quantitative data analysis on patient outcomes, healthcare utilization and cost. This included a comparison of CCH patients with retrospective propensity-matched controls, on the basis of demographic and clinical factors. For data from 208-2020, 24 of 278 CCH patients were matched to contemporaneous controls, and 238 were matched to historical controls at the same site. Currently, the same analysis is being conducted for admissions between 2020-2023. Results: CCH patients and caregivers responding to the survey reported an overall high quality of care. In the 2 years prior to the pandemic, patients' average rating of CCH care was 9.3 out of 0 (n=69). Of CCH patients surveyed during the COVID-9 pandemic (n=9), 97% of patients (n=9) were "satisfied" or "very satisfied," 00% reported that they were treated with "respect and dignity," and 80% felt prepared to manage their conditions upon discharge. Health-related quality of life measured by the EQ-5D visual analogue scale found an average improvement of 9.8 points from admission (n=48) to 30 days post discharge. Furthermore, there were no unexpected deaths of CCH patients during the first 5 years of the program.Length of stay (in days) appeared to be twice as long for CCH patients versus controls. However, when separating by subgroups, the AA group showed no statistically significant difference in length of stay, while the EFD subgroup had a statistically significant increase in length of stay over twice that of controls. AA subgroup cost estimates suggest 35% lower cost of index admissions with a further avoidance of 3% in the 80 days post-dishcarge with an overall reduction in cost of 22%. EFD subgroup showed 78% higher cost during the index admissions (approximately $9700 versus $000 for controls) with but showed a dramatic cost avoidance of 65% in the 80 days post-discharge ($5800 versus $24000) with an overall equal cost over 6 months in both arms. When all patients were analyzed together, the same pattern was observed as for the EFD subgroup, which comprised 75% of admissions. At the time of writing this abstract the analysis for data from 2020 to 2023 is in progress.ConclusionsHospital at Home is an emerging model of care that is able to safely provide home-based acute care and enhance transitions of care via real-world care-planning. The ability to care for patients outside of hospital walls increases capacity while reducing the need to build new physical infrastructure and is able to achieve the quadruple aim. Next Steps: As HAH gains momentum internationally, there is a need to formally recognize this model as a clinical entity that requires a specialized skillset and linkages with the community to improve patient outcomes and reduce potentially avoidable hospitalizations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.029
GPT teacher head0.382
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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