Rethinking Complex Care Using Participatory Medical Cognition and User-Driven Learning Amidst Multimorbidity: Participatory Action Research Study
Notice bibliographique
Résumé
Background: Managing patients with multiple chronic comorbidities is complex and challenging within traditional health care systems due to the need for multidisciplinary expertise, longitudinal tracking, and coordination. The development of collaborative online platforms leveraging user-driven health care (UDHC) and medical cognition principles offers new avenues for addressing these complexities by facilitating remote, participatory, and evidence-informed case management. Objective: The aim of this study was to demonstrate the application of a collaborative online case-based blended learning ecosystem (CBBLE) integrated with a patient journey record (PaJR) for the comprehensive remote management and review of a complex patient case with multiple chronic conditions. The study also aimed to evaluate how participatory medical cognition through this platform supports decision-making, patient empowerment, and clinical outcomes in a resource-constrained rural setting. Methods: A single case study of a 44-year-old female patient from rural India with multiple chronic conditions-including type 2 diabetes mellitus, Meesmann corneal epithelial dystrophy postphototherapeutic keratectomy, recurrent infections, lateral epicondylalgia, and hypertension-was managed remotely from December 2024 to May 2025. Deidentified health data, patient-reported outcomes, biometric monitoring, images, and historical records were shared asynchronously via an online e-log book platform. A global community of multidisciplinary experts engaged in collaborative review, critical evidence appraisal (including artificial intelligence [AI]-assisted literature retrieval), and ongoing clinical discussions. The patient advocate facilitated detailed symptoms and lifestyle logging. This case is intended to illustrate feasibility rather than establish generalizability. Results: The participatory platform enabled multispecialty expert input and integrated patient context to optimize management. The patient reduced antidiabetic medication significantly and discontinued all blood pressure and heart rate medications by March 2025. Lifestyle modifications, muscle-strengthening exercises, and diet adjustments were effectively supported. Expert consensus reclassified her irregular heart rate symptoms as anxiety-related palpitations, safely withdrawing beta-blockers. Collaborative discussions guided conservative management of eye infections and pain syndromes. Despite ongoing challenges with some symptoms (eg, eye issues and arm function), the patient reported improved quality of life, confidence, and satisfaction from reduced medication burden and comprehensive monitoring. Conclusions: This case exemplifies the value of collaborative, multidisciplinary, and technology-enabled participatory medical cognition platforms for managing complex multimorbidity. By integrating patient-reported data, AI-supported evidence synthesis, and asynchronous expert consultation, such ecosystems can enable holistic, evidence-based care, reduce overtreatment, support patient empowerment, and enhance clinical education, particularly in resource-limited and geographically dispersed contexts. Although this study presents a single case, wider adoption of similar digital platforms could significantly improve management of complex patients and foster a new model of user-driven, participatory health care and learning.
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 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 | 0,006 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».