Performance of an eHealth (NOMHAD) System Comprising Telemonitoring, Telenotification, and Telecoaching for Patients With Multimorbidity: Proof-of-Concept Study
Notice bibliographique
Résumé
BACKGROUND: Management of patients with multiple chronic diseases is a growing public health challenge, especially in rural sectors where access to physicians may be limited. Connected medical devices monitoring vital signs, associated with eHealth program and structured telephone support, may improve complex patient management through early detection of disease complications, positive impact on patients' health, and health resources consumption optimization. OBJECTIVE: The aim of this study was to evaluate the technical performance and user experience of the NOMHAD eHealth system in patients with multimorbidity. METHODS: This was a pilot, single-arm, interventional study. Patients with multimorbidity with any combination of chronic heart failure (CHF), chronic obstructive pulmonary disease, and diabetes were followed for 80-100 days using the NOMHAD eHealth system. This system used connected devices telemonitoring symptoms and vital signs (eg, body weight, oxygen saturation, pulse rate, blood pressure, and blood glucose), associated with structured telecoaching and educational support by call center nurses. An overall risk indicator (ORI) was automatically computed after each data teletransmission. The ORI was color coded; green indicated no action required; yellow, orange, and red (low to high priority, respectively) generated telenotifications and indicated to the nurses the need for a telecoaching action. Each ORI was calculated by combining 7 clinical stability system indicators based on symptom questionnaires and vital signs. Technical accuracy of the system was assessed by comparing system-generated ORIs with ORIs recalculated from raw data. Ease of use, usefulness, satisfaction, and acceptability of the system were assessed through patient adherence to self-assessments, and through self-administered questionnaires to patients, call center nurses, and physicians. RESULTS: A total of 23 patients were enrolled in this study and participated between April 2016 and March 2017 at 5 study centers in France. All patients were successfully equipped and evaluable for analysis. Mean age was 68.5 (SD 10.4) years and most patients were men (n=20). The most common multimorbidity was CHF + diabetes (n=15), followed by patients with all 3 diseases (n=5). Mean effective follow-up was 78.7 (SD 24.2) days. The system generated 6263 ORIs, as several ORIs could be generated on a single day for any patient. Overall system sensitivity was 99.2% (95% CI 98.9-99.4) and overall specificity was 91.3% (95% CI 87.7-94.1). Most patients (20/23, 87%) were satisfied with the system and agreed that it helped them to better understand and manage their diseases, and 19/23 (83%) valued the nurse regular contacts. Nurses and physicians were generally satisfied with the system and considered it useful. All users indicated they would agree to long-term use of the system. CONCLUSIONS: This study provides evidence that the NOMHAD eHealth system is accurate, acceptable, informative, and feasible for patients with multimorbidity, supporting further investigation of its clinical benefits. TRIAL REGISTRATION: Agence Nationale de Sécurité du Médicament et des Produits de Santé 2015-A01106-43; https://ictaxercb.ansm.sante.fr/Public/index.php.
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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 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 ».