eHealth and Home-Monitoring of Patients with Interstitial Lung Diseases; Worldwide Experiences and Perspectives
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".