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Record W4409736408 · doi:10.2196/64877

Evaluation of a Virtual Home Health Heart Failure Program: Mixed Methods Study

2025· article· en· W4409736408 on OpenAlexvenueno aff
Nilufeur McKay, Rosemary Saunders, Helene Metcalfe, Suzanne Robinson, Peter Palamara, Kellie Steer, Jeannie Yoo, Miles Ranogajec, Lisa Whitehead, Beverley Ewens

Bibliographic record

VenueJMIR Cardio · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Heart failure is a prevalent and debilitating condition, affecting millions globally and imposing a significant burden on patients, families, and health care systems. Despite advancements in medical treatments, the gap in effective, continuous, and personalized supportive care remains glaringly evident. To address this pressing issue, virtual health care services delivered by interdisciplinary teams represent a promising solution. Understanding the outcomes and experience of remote monitoring-enabled interdisciplinary chronic disease management programs can inform resource allocation and health care policy decisions. Objective: The purpose of this study was to evaluate the clinical and behavioral outcomes of patients undertaking a Virtual Home Health Heart Failure Program (VHHHFP) and explore the experiences of patients and health care practitioners (HCPs). Methods: The VHHHFP is a virtual postdischarge support service for patients with heart failure that includes an intensive 3-month period followed by a maintenance period delivered by an interdisciplinary team. A mixed methods study was conducted with patients and HCPs. Self-reported outcome data (KCCQ-12 [Kansas City Cardiomyopathy Questionnaire-12], PHQ-4 [Patient Health Questionnaire-4], PAM-13 [Patient Activation Measure-13], and PREMs [Patient Reported Experience Measures]) were obtained from the records of patients (N=49) who completed the intensive phase of the VHHHFP, and interviews were conducted with patients (n=9) and HCPs (n=6). A paired t test was used to compare quantitative data before and after the 3-month intervention, and a thematic qualitative analysis was undertaken of interview data. Results: Thirty-one of the 55 (77.5%) patients completed the baseline and 3-month follow-up KCCQ-12 assessment. The mean KCCQ-12 summary score at 3 months was 72.20 (SD 20.2), which was significantly higher than the mean summary score at baseline of 50.51 (SD 17.59; P<.001). These findings were similar for the KCCCQ-12 subscales: physical limitations (mean 47.09, SD 29.7 and mean 69.43, SD 22.6; P<.001), quality of life (mean 43.75, SD 21.7 and mean 62.91, SD 25.7; P<.001), symptom frequency (median 60.40, IQR 1-100 and median 91.70, IQR 35.40; P<.001), and social limitation (median 50.0, IQR 1-100 and median 82.50, IQR 32.50; P<.001). The PHQ-4 measure of psychological health was completed by 32 (80%) patients. The median scores at baseline and follow-up for total distress (median 1.50, IQR 0-7 and median 0.0, IQR 0-8; P<.02), and the anxiety subscale (median 1.0, IQR 0-6 and median 0.0, IQR 0-4; P<.02) reduced over time. Six hospital admissions were recorded (10.2% of 49 patients) within 30 days. Nine patient interviews aligned with the value-based health care (VBHC) Capability, Comfort, and Calm (CCC) framework. Three themes were identified, which are as follows: (1) enhanced patient capability, (2) improved patient comfort, and (3) positive influences on calm. Six health care professionals shared experiences of the VHHHFP, with three emerging themes: (1) improved patient capability through shared decision-making, (2) improving capability through care practices, and (3) promoting comfort and calm through virtual coordination and collaboration. Conclusions: The use of technologies to support the management of HF is an area of growth. This study contributes to the understanding of how remote patient monitoring with interdisciplinary chronic disease support, integrated into an existing system, can improve clinical outcomes for patients.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.454
Teacher spread0.410 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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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