Application and Preliminary Outcomes of Remote Diagnosis and Treatment During the COVID-19 Outbreak: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in the self-quarantine of countless people due to possible infection. This situation makes telemedicine necessary as it can overcome geographical barriers, increase the number of people served, and provide online clinical support for patients. However, the outcomes of telemedicine have not yet been evaluated. OBJECTIVE: The aim of our study is to describe the epidemiological features and clinical symptoms of patients receiving remote diagnosis and treatment at the online outpatient clinic of our hospital, as well as to analyze the outcomes and advantages of telemedicine, during the COVID-19 pandemic. METHODS: Data from patients receiving remote diagnosis and treatment via consultation services for COVID-19 concerns at the online outpatient clinic of Henan Provincial People's Hospital from January 24 to February 17, 2020, were collected. A retrospective analysis was performed on epidemiological features, clinical symptoms, and preliminary outcomes. RESULTS: Online inquiry, consultation, and suggestions were provided for patient concerns related to COVID-19. Our hospital also offered offline noncontact drug delivery services following online ordering and payment. A total of 4589 patients receiving remote diagnosis and treatment were recruited. The daily number of online outpatient visits initially increased and then decreased, reaching its peak on January 28 when the daily number of online outpatient visits totaled 612. Of 4589 patients, 1940 (42.3%) were males and 2649 (57.7%) were females (age range: 78 days to 85 years). Most patients were aged 20-39 years (n=3714, 80.9%) and came from Henan Province (n=3898, 84.9%). The number of patients from other provinces was 691 (15.1%). During the online consultations, patients discussed the following symptoms: fever (n=2383), cough (n=1740), nasal obstruction (n=794), fatigue (n=503), and diarrhea (n=276). A total of 873 orders of noncontact drug delivery following online payment was completed. The daily number of such orders gradually stabilized after the initial, steady increase. For offline drug delivery orders, the median (IQR) was 36 (58). An online satisfaction survey was filled out postconsultation by patients; of the 985 responses received, 98.1% (n=966) of respondents were satisfied with the service they received. CONCLUSIONS: Remote diagnosis and treatment offered via online outpatient consultations effectively reduced the burden on hospitals, prevented overcrowding, reduced the risk of cross-infection, and relieved patients' anxiety during the COVID-19 outbreak. This plays an essential role in pandemic management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".