The Challenges Toward Real-world Implementation of Digital Health Design Approaches: Narrative Review
Notice bibliographique
Résumé
BACKGROUND: Digital health represents an important strategy in the future of health care delivery. Over the past decade, mobile health has accelerated the agency of health care users. Despite prevailing excitement about the potential of digital health, questions remain on efficacy, uptake, usability, and patient outcome. This challenge is confounded by 2 industries, digital and health, which have vastly different approaches to research, design, testing, and implementation. In this regard, there is a need to examine prevailing design approaches, weigh their benefits and challenges toward implementation, and recommend a path forward that synthesizes the needs of this complex stakeholder group. OBJECTIVE: In this review, we aimed to study prominent digital health intervention design approaches that mediate the digital health space. In doing so, we sought to examine the origins, perceived benefits, contrasting nuances, challenges, and typical use-case scenarios of each methodology. METHODS: A narrative review of digital health design approaches was performed between September 2020 and April 2021 by referencing keywords such as "digital health design," "mHealth design," "e-Health design," "agile health," and "agile healthcare." The studies selected after screening were those that discussed the design and implementation of digital health design approaches. A total of 120 studies were selected for full-text review, of which 62 (51.6%) were selected for inclusion in this review. RESULTS: A review identifying the 5 overarching digital health design approaches was compiled: user-centered design, person-based design, human-centered design, patient-centered design, and patient-led design. The findings were synthesized in a narrative structure discussing the origins, advantages, disadvantages, challenges, and potential use-case scenarios. CONCLUSIONS: Digital health is experiencing the growing pains of rapid expansion. Currently, numerous design approaches are being implemented to harmonize the needs of a complex stakeholder group. Whether the end user is positioned as a person, patient, or user, the challenge to synthesize the constraints and affordances of both digital design and health care, built equally around user satisfaction and clinical efficacy, remains paramount. Further research that works toward a transdisciplinarity in digital health may help break down friction in this field. Until digital health is viewed as a hybridized industry with unique requirements rather than one with competing interests, the nuances that each design approach posits will be difficult to realize in a real-world context. We encourage the collaboration of digital and health experts within hybrid design teams, through all stages of intervention design, to create a better digital health culture and design ethos.
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 | 0,031 | 0,106 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».