Development of a Customizable Web-Based Dashboard for Remote Blood Pressure Monitoring: Feasibility and Usability Implementation Study
Notice bibliographique
Résumé
Background: Effective blood pressure (BP) management is essential for several disease states, including secondary stroke prevention. Remote BP monitoring used in combination with an electronic data capture (EDC) system offers potential for improved BP control for patients. While such systems currently exist, there are many patient-, provider-, and system-specific challenges these systems currently do not address. In addition, such systems are rarely described transparently and comprehensively in the literature with enough detail to be reproduced by other clinicians. Objective: This study aims to describe the development and design of a web-based EDC system tailored for remote BP monitoring and management to overcome common patient-, provider-, and system-specific challenges. Methods: The EDC system was developed for use in the Video-based Intervention to Reduce Treatment and Outcome Disparities in Adults living with Stroke (VIRTUAL) and transient ischemic attack clinical trial, a randomized study involving adult stroke survivors receiving care for secondary stroke prevention. The EDC system supports remote monitoring using a BP monitor, which was specifically and methodically chosen for its suitability to the population. The EDC system integrates the data from the remote monitors into a web-based application, facilitating real-time monitoring for health care team intervention. The system includes a BP dashboard displaying time-series data, visual alerts, and email notifications for out-of-range readings. Proxy measures assessed system effectiveness in addressing patient-, provider-, and system-specific needs. Results: Seventeen BP monitors were evaluated, and the BlipcareBlip BP800 was selected based on its cellular connectivity, ergonomic design, and application programming interface compatibility. The EDC system successfully linked patient data to the dashboard, enabling real-time visualization of BP trends and provider alerts. A total of 97% (184/190) of participants in the intervention arm engaged in BP monitoring, averaging 1.2 readings per day. Clinicians actively used the system, with mean monthly logins ranging from 19 to 44 per provider. Alerts were triggered for 69% (131/190) of participants, primarily for systolic BP abnormalities. Data transmission delays were minimal, with a median time of 1.4 minutes from data collection to dashboard entry. Outlier alerts were efficiently processed, with an average notification time of 17.9 hours. The system demonstrated high functionality in addressing patient-, provider-, and system-specific needs, supporting effective BP monitoring and intervention delivery. Conclusions: The VIRTUAL EDC system facilitates remote monitoring of BP in stroke survivors, offering a model for similar systems in clinical trials. Clinicians can analyze, track, and interpret monitored data in near real-time, remotely, which may enhance BP management approaches, particularly in communities with limited access to care. This approach has the potential to improve hypertension management for patients and clinicians when standard data transparency is insufficient, standard monitor features are incompatible with the user, or when integrating a commercial dashboard may threaten electronic health record security.
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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 | 0,017 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».