Performance of an eHealth (NOMHAD) System Comprising Telemonitoring, Telenotification, and Telecoaching for Patients With Multimorbidity: Proof-of-Concept Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".