Remote Patient Monitoring for Global Emergencies: a case study in COVID-19 patients (Preprint)
Notice bibliographique
Résumé
Background: The COVID-19 pandemic has highlighted the critical need for telehealth and remote patient monitoring in health care delivery. Despite the growing use of on-body wearable sensors for continuous monitoring and predicting adverse events, their widespread adoption remains a significant challenge. While the pandemic has accelerated the acceptance of these technologies, achieving widespread integration requires their sustained incorporation into routine health care practices beyond emergencies. In this study, we extend the application of our previously developed remote patient monitoring system to patients with COVID-19. Objective: Our objective is to assess whether the metrics obtained from our previously developed system can provide additional insights into the recovery trajectory of individuals affected by COVID-19. This case study aims to demonstrate that remote patient monitoring systems can be adapted to diverse patient cohorts during emergencies. We aim to illustrate the ease of deployment, particularly when these systems are already integrated into the existing health care ecosystem. Methods: From November 2020 to July 2021, a total of 73 patients were recruited through the University of California, Los Angeles, Center for Smart Health, after having consented to participate in this study for 2 weeks. The research concentrated on an exploratory analysis, focusing on the detailed examination of characteristics and behaviors of patients with COVID-19 as captured by the remote patient monitoring system. We collected day-to-day changes in the following sensor measurements: daily activity, daily energy expenditure, indoor localization, SpO2, respiratory rate, heart rate, and temperature. Results: Out of the 73 patients satisfying the inclusion criteria, 41 successfully adhered to using the monitoring technology, with only 22 providing substantial watch data (>4 h). Among the participants, 39 used the pulse oximeter, 37 used the thermometer, and 36 used respiratory monitoring at night. This study demonstrated an overall increase in patients' activity levels toward the end of this study, with many beginning to leave their homes after 2 weeks. Additionally, respiratory rates shifted toward healthier lower levels, and oxygen saturation improved. Fatigue and headache were identified as the most prevalent symptoms, followed by cough and loss of smell. Conclusions: The conclusion highlights the critical importance of monitoring patients outside of hospital settings, especially during pandemics, when patients travel to hospitals or receive home visits by health care professionals, which could increase the risk of disease transmission. Studies demonstrating the benefits and efficacy of remote monitoring in home settings can better prepare health care professionals for future pandemic events. Continuous monitoring of a wide range of patient metrics, from activities to vital signs, and integration of these data into electronic health records would not only improve accuracy and reduce the burden of data collection but also pave the way for enhanced home care, offering higher quality care at a lower cost.
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 | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 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 ».