Using Goal-Directed Design to Create a Novel System for Improving Chronic Illness Care
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Notice bibliographique
Résumé
BACKGROUND: A learning health system enables patients, clinicians, and researchers to work together to choose care based on the best evidence, drive discovery as a natural outgrowth of patient care, and ensure innovation, quality, safety, and value in health care; all in a more real-time fashion. OBJECTIVE: Our paper describes how goal-directed design (GDD) methods were employed to understand the context and goals of potential participants in such a system as part of a design process to translate the concept of a learning health system into a prototype collaborative chronic care network (C3N), specifically for pediatric inflammatory bowel disease. METHODS: Thirty-six one-on-one in-depth interviews and observations were conducted with patients (10/36, 28%), caregivers (10/36, 28%), physicians/researchers (10/36, 28%), and nurses (6/36, 17%) from a pediatric gastroenterology center participating in the ImproveCareNow network. GDD methods were used to determine the context and goals of participants. These same methods were used in conjunction with idealized design process techniques to help determine characteristics of a learning health system for this pediatric health care ecology. Research was conducted in a clinic and, in the case of some patients and caregivers, at home. RESULTS: Thematic analysis revealed 3 parent-child dyad personas (ie, representations of interviewees' behavior patterns, goals, skills, attitudes, and contextual information) that represented adaptation to a chronic illness over time. These were used as part of a design process to generate scenarios (potential interactions between personas and the learning health system under design) from which system requirements were derived. These scenarios in turn helped guide generation, prioritization, design, measurement, and implementation of approximately 100 prototype interventions consistent with the aim of C3N becoming a learning health network. CONCLUSIONS: GDD methods help ensure human goals and contexts inform the design of a network of health care interventions which reflect the shape and purpose of a C3N in pediatric chronic illness care. Developing online and in-person interventions according to well-documented context and motivations of participants increases the likelihood that a C3N will enable all participants to act in ways that achieve their goals with grace and dignity. GDD methods complemented quality-improvement methods to generate prototypes consistent with clinical and research aims, as well as the goals of patient disease management.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 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 découle