Evaluation of Hospice@Home for Home-Based Palliative Care: Development and Usability Pilot Study
Notice bibliographique
Résumé
Background: The demand for palliative care is rising due to population aging and increased chronic illness. However, access to timely palliative care remains limited, particularly for patients receiving home-based hospice care in rural areas. Digital health technologies present an opportunity to enhance care delivery and communication at home. Objective: This pilot study aimed to (1) develop the Hospice@Home system, a digital in-home hospice care solution; (2) explore preliminary indications of usability and feasibility among patients with terminal cancer and their caregivers; and (3) identify challenges for future implementation. Methods: Hospice@Home was developed following the human-centered, evidence-driven Adaptive Health Experience and Application Design approach. After the prototype was developed, alpha testing was conducted with 2 simulated patients to assess system functionality and identify technical issues. Usability was measured through structured observation and task completion success during these sessions. Feasibility was evaluated during a 3-week beta test involving 5 dyads of patients with terminal cancer and caregiver, recruited through a home-based palliative care agency. Challenges were identified through user feedback, field notes, and technical logs collected throughout the testing period. Results: Hospice@Home is a web app optimized for Android devices. It integrates wearable biosignal data-blood pressure, pulse rate, sleep patterns, and oxygen saturation-measured via the Samsung Galaxy Watch 6. It also allows self-reporting of body temperature, pain levels, bowel movements, and the severity of symptoms tailored to individual patients. Medication compliance, including scheduled and pro re nata analgesics, was recorded in a smart medication box and automatically transmitted to Hospice@Home. Over 3 weeks, 5 patients (aged 53-93 y) with terminal cancer and their caregivers (aged 38-63 y) explored the system. Both patients and caregivers appreciated the consolidated symptom reporting and real-time data sharing, noting that the system helped them feel more reassured and connected to clinical support. Usability was assessed via satisfaction ratings, averaging 3.3 (SD 0.5) for patients and 4.0 (SD 0.7) for caregivers (5-point scale). Feasibility was evaluated through task compliance; dyads completed ≥13 of 18 tasks during stable periods. No major technical issues were reported. Challenges to consistent system use included data entry fatigue, psychological barriers to using technology, fluctuations in cognitive and physical functioning, and a general preference among patients and caregivers for phone calls rather than using the in-app communication. Conclusions: Hospice@Home showed early signals of usability and feasibility among patients with terminal cancer and caregivers, but these preliminary observations require cautious interpretation given the pilot design. The findings highlight potential value in supporting home-based palliative care, while underscoring the need for ongoing refinement and iterative testing before making broader claims about effectiveness or clinical impact.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».