eHealth and Home-Monitoring of Patients with Interstitial Lung Diseases; Worldwide Experiences and Perspectives
Notice bibliographique
Résumé
Rationale/Aim: The COVID-19 pandemic has threatened continuity of care and research for patients with interstitial lung diseases (ILDs). This has led to increased use and interest in novel care models including eHealth and home-monitoring. The objective of this study was to gain more insights in worldwide experiences and perspectives on eHealth use and home-monitoring for patients with ILDs. Additionally, we assessed whether the COVID-19 pandemic impacted use of eHealth and home-monitoring. Methods: Healthcare providers (HCPs) with expertise in ILDs were invited to participate in an online survey of 28 questions. eHealth was defined as the use of technology to improve health and/or quality of healthcare, and online home-monitoring as tracking clinical results measured at home by patients using an online application. Results: In total, 284 HCPs from 54 countries completed the survey;89.1% were pulmonologists, 7.0% rheumatologists ,1.8% specialist nurses, and 2.0% others. 8.1% of the HCPs had used eHealth before the COVID-19 pandemic, and an additional 42.3% started using eHealth during the pandemic. Almost half of the participants without eHealth experience stated that they would like to use eHealth, but do not know how to set it up. Among HCPs with eHealth experience, the most used applications are video consultations (67.4%), online patient portals (29.9%),online home-monitoring (21.4%), and online self-help applications (8.9%). Technical (72.5%), reimbursement (50.0%), reliability (44.0%), privacy (39.1%), and ethical issues (22.5%) were identified as the biggest challenges for implementation of eHealth. The vast majority (96.5%) of HCPs believe there is additive value in home-monitoring. Most HCPs (92.0%) believe it can improve quality of care, and can be used for research (59.4%) and registry (52.2%) purposes. 75% of HCPs would like to have online access to data collected by patients and 74.3% would like to receive an automated warning if results indicate worsening of disease. HCPs think integrating home spirometry, patient-reported outcome measures, physical activity levels and home-based oxygen saturation in an online home-monitoring application could be useful (Figure 1). Conclusion: The COVID-19 pandemic has led to an increase in the use of eHealth and home-monitoring in ILD. Worldwide, HCPs are interested in further implementation of eHealth and home-monitoring, both for improvement of regular care as well as for research purposes. Further collaborations outside the medical field are needed with patients, technicians, policymakers, legislative bodies and insurance companies, to safely and sustainably implement eHealth and home-monitoring as novel models of care.
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 | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| 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 ».