A Web- and App-Based Connected Care Solution for COVID-19 In- and Outpatient Care: Qualitative Study and Application Development
Bibliographic record
Abstract
BACKGROUND: From the perspective of health care professionals, coronavirus disease (COVID-19) brings many challenges as well as opportunities for digital health care. One challenge is that health care professionals are at high risk of infection themselves. Therefore, in-person visits need to be reduced to an absolute minimum. Connected care solutions, including telehealth, remote patient monitoring, and secure communications between clinicians and their patients, may rapidly become the first choice in such public health emergencies. OBJECTIVE: The aim of the COVID-19 Caregiver Cockpit (C19CC) was to implement a free-of-charge, web- and app-based tool for patient assessment to assist health care professionals working in the COVID-19 environment. METHODS: Physicians in Argentina, Germany, Iran, Italy, Portugal, Switzerland, and the United States explained their challenges with COVID-19 patient care through unstructured interviews. Based on the collected feedback, the first version of the C19CC was built. In the second round of interviews, the application was presented to physicians, and more feedback was obtained. RESULTS: Physicians identified a number of different scenarios where telemedicine or connected care solutions could rapidly improve patient care. These scenarios included outpatient care, discharge management, remote tracking of patients with chronic diseases, as well as incorporating infected physicians under quarantine into telehealth services. CONCLUSIONS: The C19CC is the result of an agile and iterative development process that complements the work of physicians. It aims to improve the care and safety of people who are infected by COVID-19.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".