Business Models of eHealth Interventions to Support Informal Caregivers of People With Dementia in the Netherlands: Analysis of Case Studies
Notice bibliographique
Résumé
BACKGROUND: In academic research contexts, eHealth interventions for caregivers of people with dementia have shown ample evidence of effectiveness. However, they are rarely implemented in practice, and much can be learned from their counterparts (commercial, governmental, or other origins) that are already being used in practice. OBJECTIVE: This study aims to examine a sample of case studies of eHealth interventions to support informal caregivers of people with dementia that are currently used in the Netherlands; to investigate what strategies are used to ensure the desirability, feasibility, viability, and sustainability of the interventions; and to apply the lessons learned from this practical, commercial implementation perspective to academically developed eHealth interventions for caregivers of people with dementia. METHODS: In step 1, experts (N=483) in the fields of dementia and eHealth were contacted and asked to recommend interventions that met the following criteria: delivered via the internet; suitable for informal caregivers of people with dementia; accessible in the Netherlands, either in Dutch or in English; and used in practice. The contacted experts were academics working on dementia and psychosocial innovations, industry professionals from eHealth software companies, clinicians, patient organizations, and people with dementia and their caregivers. In step 2, contact persons from the suggested eHealth interventions participated in a semistructured telephone interview. The results were analyzed using a multiple case study methodology. RESULTS: In total, the response rate was 7.5% (36/483), and 21 eHealth interventions for caregivers of people with dementia were recommended. Furthermore, 43% (9/21) of the interventions met all 4 criteria and were included in the sample for the case study analysis. Of these 9 interventions, 4 were found to have developed sustainable business models and 5 were implemented in a more exploratory manner and relied on research grants to varying extents, although some had also developed preliminary business models. CONCLUSIONS: These findings suggest that the desirability, feasibility, and viability of eHealth interventions for caregivers of people with dementia are linked to their integration into larger structures, their ownership and support of content internally, their development of information and communication technology services externally, and their offer of fixed, low pricing. The origin of the case studies was also important, as eHealth interventions that had originated in an academic research context less reliably found their way to sustainable implementation. In addition, careful selection of digital transformation strategies, more intersectoral cooperation, and more funding for implementation and business modeling research are recommended to help future developers bring eHealth interventions for caregivers of people with dementia into practice.
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 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 | 0,024 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».