Leveraging Smart Telemedicine Technology to Enhance Nursing Care Satisfaction and Revolutionize COVID-19 Care: Prospective Cohort Study
Bibliographic record
Abstract
Background: Telemedicine has been utilized in the care of patients with COVID-19, allowing real-time remote monitoring of vital signs. This technology reduces the risk of transmission while providing high-quality care to both self-quarantined patients with mild symptoms and critically ill patients in hospitals. Objective: This study aims to investigate the application of telemedicine technology in the care of patients with COVID-19, specifically focusing on usability, effectiveness, and patient outcomes in both home isolation and hospital ward settings. Methods: The study was conducted between January 2022 and December 2022. More than 800 cases were monitored using the QOCA remote home care system, a telemedicine platform that enables remote monitoring of physiological data-including heart rate, blood pressure, temperature, and oxygen levels-through Internet of Things devices and a 4G-connected tablet. Of these, 27 patients participated in thie study: the QOCA remote home care system was deployed 36 times in the isolation ward and 21 times to those in home isolation. The QOCA remote care system monitored isolated cases through remote care packages and a 4G tablet. Case managers and physicians provided telemedicine appointments and medications. Innovative methods were developed to enhance usage, including online health education, remote care equipment instructions via QR core links, and video consultations for patients without smartphones. Results: A clinical nurse satisfaction survey revealed that most respondents found the content of the remote care package comprehensive and the interface easy to learn. They expressed a desire to continue using the system. The majority also agreed that using the remote care system and package would reduce their workload and that patients and caregivers could easily learn to use the package. While some respondents expressed concerns about network and Bluetooth connectivity, the majority (24/27, 89%) agreed to include the remote device as part of their routine equipment, with an average score of 84.8 points. Conclusions: The integration of telemedicine technology improves the quality of care while reducing the workload and exposure of health care workers to viruses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".