Application of a Web-based Self-assessment Triage Tool During the COVID-19 Pandemic: Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has sped up the implementation of telehealth solutions in medicine. A few symptom checkers dedicated for COVID-19 have been described, but it remains unclear whether and how they can affect patients and health systems. OBJECTIVE: This paper demonstrates our experiences with the COVID-19 risk assessment (CRA) tool. We tried to determine who the user of the web-based COVID-19 triage app is and compare this group with patients in the infectious diseases ward's admission room to evaluate who could benefit from implementing the COVID-19 online symptom checker as a remote triage solution. METHODS: We analyzed the answers of 248,862 people interacting with an online World Health Organization-based triage tool for assessing the probability of SARS-CoV-2 infection. These users filled in an online questionnaire between April 7 and August 6, 2020. Based on the presented symptoms, risk factors, and demographics, the tool assessed whether the user's answers were suggestive of COVID-19 and recommended appropriate action. Subsequently, we compared the sociodemographic and clinical characteristics of tool users with patients admitted to the Infectious Diseases Admission Room of J. Gromkowski Hospital in Wrocław. RESULTS: The CRA tool tended to be used by asymptomatic or oligosymptomatic individuals (171,226 [68.80%] of all users). Most users were young (162,432 [65.27%] were below 40 years of age) and without comorbidities. Only 77,645 (31.20%) of the self-assessment app users were suspected of COVID-19 based on their reported symptoms. On the contrary, most admission room patients were symptomatic-symptoms such as fever, cough, and dyspnea were prevalent in both COVID-19-positive and COVID-19-negative patients. COVID-19-suspected patients in the CRA tool group presented similar COVID-19 symptoms as those who presented to the admission room. These were cough (25,062/40,007 [62.64%] in the CRA tool group vs 138/232 [59.48%] in the admission room group), fever (23,123/40,007 [57.80%] in the CRA tool group vs 146/232 [62.93%] in the admission room group), and shortness of breath (15,157/40,007 [37.89%] in the CRA tool group vs 87/232 [37.50%] in the admission room group). CONCLUSIONS: The comparison between the symptomatology of the users interacting with the CRA tool and those visiting the admission room revealed 2 major patient groups who could have benefited from the implementation of the self-assessment app in preclinical triage settings. The primary users of the CRA tool were young, oligosymptomatic individuals looking for screening for COVID-19 and reassurance early in the COVID-19 pandemic. The other group were users presenting the typical symptoms suggestive of COVID-19 at that time. The CRA tool recognized these individuals as potentially COVID-19 positive and directed them to the proper level of care. These use cases fulfil the idea of preclinical triage; however, the accuracy and influence on health care must be examined in the clinical setting.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».