Management of Frail and Older Homebound Patients With Heart Failure: A Contemporary Virtual Ambulatory Model
Bibliographic record
Abstract
Background Heart failure (HF) affects many patients who are older and frail, presenting multiple physical barriers to accessing specialty care in a traditional ambulatory clinic model. Here, we present an assisted virtual care model in which a home visiting nurse facilitated video visits with a HF cardiologist to follow homebound, frail, and older patients with HF. Methods This is a pragmatic, quasi-experimental, pre–post, single-centre study. It included homebound, frail, and older patients with HF from 2015 to 2019 who were followed for 1 year; in-person clinic visits were completely replaced by nurse-facilitated virtual video visits. Outcomes evaluated included annualized hospitalization rate, number of hospitalization days, and number of emergency department visits. Results A total of 49 patients were included, with a median age of 86 (83-93) years, and were followed for 1 year after enrollment. Among patients enrolled, HF with preserved ejection fraction was the most common subtype (57%). Compared to the year prior to enrollment, patients had a lower mortality-adjusted all-cause annualized hospitalization rate in the year following enrollment (2.57 vs 1.78, P < 0.0001). Compared to the year prior, the number of mortality-adjusted all-cause hospitalization days was significantly lower in the year following enrollment (27.2 vs 21.4, P < 0.0001). There was a reduction in the number of all-cause annualized emergency department visits (3.10 vs 2.27, P = 0.003). Conclusions Nurse-assisted virtual visits may be a preferable strategy for homebound, frail, and older patients with HF to receive longitudinal care. This approach may represent a plausible strategy to care for other patients with significant barriers to accessing specialized cardiac care.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".