Telemedicine for COVID-19 management in Brazil: outcomes and health system implications from a prospective cohort study
Bibliographic record
Abstract
Background: The COVID-19 pandemic exposed vulnerabilities in traditional disease surveillance systems, particularly in data reporting and contact tracing. Telemedicine emerged as a promising approach to expand remote access to healthcare. This study aimed to evaluate a newly implemented telemedicine system designed to manage patients with COVID-19, reduce hospital overload, enable early case detection and isolation, ensure rapid response to clinical deterioration, simplify medical records, and provide ongoing patient support. Methods: A prospective cohort study was conducted using the E-care telemedicine system to assist adult patients presenting with COVID-19 symptoms at a Brazilian university between June 2021 and June 2024. Results: The E-care system delivered care to 6,129 patients, predominantly female, white university students. Physicians attended over 80% (4,903/6,129) of patients and prescribed medications to nearly 28% (1,411/5,041). Medical certificates for time off work were issued to 43% (2,635/6,129) of participants. COVID-19 tests were recommended for approximately 24% of patients, with a positivity rate above 81% among those who returned results. Only 66 patients (1.2%) required in-person care, and no COVID-19-related deaths were reported. Patient satisfaction was high, with 96% (5,584/6,129) expressing satisfaction or high satisfaction with the service. Conclusions: This study provides robust evidence supporting the successful implementation of a telemedicine system for managing COVID-19 cases. The large number of users highlights an unmet demand for virtual healthcare. Telemedicine was rapidly adopted, achieved high patient satisfaction, and contributed to reducing hospital burden, promoting early detection, and minimizing in-person consultations. These findings reinforce the value of telemedicine as an essential tool for health systems and policymakers to strengthen care delivery beyond the pandemic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".