Use of WhatsApp for Polyclinic Consultation of Suspected Patients With COVID-19: Retrospective Case Control Study
Bibliographic record
Abstract
BACKGROUND: Telephones, internet-connected devices (phablets, personal computers), chat platforms, and mobile apps (eg, Skype, Facebook Messenger, WhatsApp) can be exploited for telemedicine applications. WhatsApp and similar apps are also widely used to facilitate clinical communication between physicians. Moreover, WhatsApp is used by emergency department (ED) physicians and consulting physicians to exchange medical information during ED consultations. This platform is regarded as a useful app in the consultation of dermatological and orthopedic cases. Preventing overcrowding in the ED is key to reducing the risk of disease transmission, and teleconsulting practice is thought to be effective in the diagnosis, treatment, and reduction of transmission risk of disease, most notably during the COVID-19 pandemic. Video consultation is highly recommended in some countries on the grounds that it is likely to reduce the risk of transmission. WhatsApp-like apps are among the video consultation platforms that are assumed to reduce the risk of contamination by minimizing patient-physician contact. OBJECTIVE: The aim of this study was to investigate the effects of WhatsApp video consultation on patient admission and discharge times in comparison to bedside consultation in the evaluation of potential patients with COVID-19 visiting a COVID-19 outpatient clinic during the pandemic. METHODS: Patients who presented to the ED COVID-19 outpatient clinic between March 11 and May 31, 2020, and for whom an infectious disease specialist was consulted (via WhatsApp or at bedside) were included in the study in accordance with the inclusion and exclusion criteria. Eventually, 54 patients whose consultations were performed via WhatsApp and 90 patients whose consultations were performed at bedside were included in our study. RESULTS: The median length of stay in the ED of discharged patients amounted to 103 minutes (IQR 85-147.75) in the WhatsApp group and 196 minutes (IQR 141-215) in the bedside group. In this regard, the length of stay in the ED was found to be significantly shorter in the WhatsApp group than in the bedside group (P<.001). Among the consulted and discharged patients, 1 patient in each group tested positive for SARS-CoV-2 by polymerase chain reaction test and thus was readmitted and hospitalized (P=.62). The median length of stay of the inpatients in the ED was found to be 116.5 minutes (IQR 85.5-145.5) in the WhatsApp group and 132 minutes (IQR 102-168) in the bedside group. The statistical analysis of this time difference revealed that the length of stay in the ED was significantly shorter for patients in the WhatsApp group than in the bedside group (P=.04). CONCLUSIONS: Consultation via WhatsApp reduces both contact time with patients with COVID-19 and the number of medical staff contacting the patients, which contributes greatly to reducing the risk of COVID-19 transmission. WhatsApp consultation may prove useful in clinical decision making as well as in shortening process times. Moreover, it does not result in a decreased accuracy rate. The shortened discharge and hospitalization timespans also decreased the length of stay in the ED, which can have an impact on minimizing ED crowding. TRIAL REGISTRATION: ClinicalTrials.gov NCT04645563; https://clinicaltrials.gov/ct2/show/NCT04645563.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".