Management of Frail and Older Homebound Patients With Heart Failure: A Contemporary Virtual Ambulatory Model
Bibliographic record
Abstract
BackgroundHeart 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.MethodsThis 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.ResultsA 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).ConclusionsNurse-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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".