Impact of Remote Patient Monitoring Platform on Patients With Moderate to Severe Persistent Asthma: Observational Study
Notice bibliographique
Résumé
BACKGROUND: Asthma is one of the most common respiratory diseases, with an ever-growing health care burden. Remote patient monitoring (RPM) has gained increasing importance in the respiratory care area with the outbreak of the COVID-19 pandemic. In this pilot study, we introduced a novel platform that remotely monitors patients with chronic respiratory illnesses using Centers for Disease Control and Prevention guidelines to reduce hospitalizations and emergency department visits. OBJECTIVE: This study aimed to understand patient and physician engagement with a new virtual care solution (KevaTalk app and Keva365 platform) and the value, for both patients and providers, of using an RPM tool. We assessed real-world use of the platform from both physician and patient perspectives and the impact of devices on engagement and monitoring. METHODS: Participants with a history of moderate to severe persistent asthma, seen by a pulmonologist at a hospital, were included in this study. The inclusion criteria involved being aged ≥18 years and having access to an Android or iOS mobile device with internet. We provided patient questionnaires to assess the app's usefulness and evaluate its features. We monitored remote spirometry and oximetry data, app check-ins, alerts, and escalations during this study's time window. Data were reviewed daily and predetermined criteria were set to escalate for physician review based on the patient's symptoms and objective data. RESULTS: Overall, 25 patients were included in this pilot. The mean age was 57 (SD 10.7) years and a majority (n=23, 92%) were female. A baseline questionnaire, which was used to rate the app, indicated that the ease of check-in and ease of modification to the patient's asthma plan were the 2 highest rated features. In total, 2066 check-ins (1550 green, 506 yellow, and 10 red check-ins) and 1155 spirometry sessions were recorded during this 3-month period. Further, 64% (14/22) and 91% (20/22) of patients were found to have peak flows in their red and yellow zones at least once, respectively. During the course of this study, 484 alerts were recorded and evaluated by the team, of which 37.2% (n=180) required an escalation to the physician; this included a transfer to a medical facility, change in respiratory medication, or further education. CONCLUSIONS: In this pilot study, we demonstrated the feasibility of implementing a novel RPM platform in patients with asthma. Our platform showed high patient engagement and satisfaction and provided physicians with real-time subjective data to evaluate patients remotely that aids in clinical decision-making. The escalations prevented patients from having an exacerbation or flare up, which led to the prevention of an emergency department visit. Continuous monitoring of chronic disease has benefits over episodic monitoring. It allows for improved quality of life, better outcomes, and huge health care savings.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».