The value of technology to support caregiving for individuals living with heart failure (Preprint)
Notice bibliographique
Résumé
BACKGROUND The demand for health services to meet the chronic health needs of our aging population is significant and remains unmet due to a limited supply of clinical resources. Specifically in managing heart failure (HF), virtual care sought to address this gap during COVID-19, but highlighted an access issue for those who could not use technology-mediated healthcare services without the support of their informal caregivers (ICs). Because of the complexity of managing HF symptoms and recurrent exacerbations, many patients co-manage their illness with their ICs in a care dyad, working together to optimize the patient’s outcomes and health-related quality of life. However, most HF programs have missed the opportunity to consider the dyadic perspective despite dyadic behaviour and well-being interdependencies. OBJECTIVE This research sought to characterize the value of technology in supporting caregiving for individuals living with heart failure. METHODS Motivated by an observed unique pattern of engagement in patients enrolled in our Medly HF management program at the Peter Munk Cardiac Centre in Toronto, Canada, we conducted 20 semi-structured interviews with a diverse convenience sample of informal caregivers. All interviews were analyzed using the iterative refinement of a co-developed codebook.. The team kept reflexivity journals to reflect the impact of their positionality on their coding. Themes were first derived deductively using HF typologies (patient-oriented dyads, caregiver-oriented dyads, and collaboratively-oriented dyads), and then inductively refined and re-categorized based on concepts from the van Houtven et al. framework. RESULTS We believe there is a need to formally and intentionally expand HF technologies to be inclusive of dyadic needs and goals. We suggest defining three opportunities for where value can be added during technology design. First, identify how technology may be leveraged to increase psychological bandwidth, curb uncertainty, and provide peace of mind. We found actionable feedback to be highly desired by both partners. Second, develop technology that can serve as a member of the dyad’s support system. In our experience, automated prompts to patients for taking measurements can mimic the support typically provided by ICs and ease their load. Third, consider how technology can mitigate the dyad’s clinical knowledge requirements and learning curve. Our approach included real-time actionable feedback paired with a human-in-the-loop, nurse-led model of care. CONCLUSIONS Our findings identified a need to focus on improving the dyadic experience as a whole by building IC functionality into digital health self-management interventions. Through a shared model of care that supports the role of the patient in their own HF management, includes ICs to expand and enhance the patient’s capacity to care, and acknowledges the needs of ICs to care for themselves, we anticipate improved outcomes for both partners.
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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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».