Evaluation of a Virtual Home Health Heart Failure Program: Mixed Methods Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».