Remote Patient Monitoring for Global Emergencies: a case study in COVID-19 patients (Preprint)
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".