Use of WhatsApp for Polyclinic Consultation of Suspected Patients With COVID-19: Retrospective Case Control Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».