An Early Warning Mobile Health Screening and Risk Scoring App for Preventing In-Hospital Transmission of COVID-19 by Health Care Workers: Development and Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Hospitals have been identified as very high-risk places for COVID-19 transmission between health care workers and patients who do not have COVID-19. Health care workers are the most at-risk population to contract and transmit the infection, especially to already vulnerable patients who do not have COVID-19. In low-income countries, routine testing is not feasible due to the high cost of testing; therefore, presenting the risk of uncontrolled transmission within non-COVID-19 treatment wards. This challenge necessitated the development of an affordable intermediary screening tool that would enable early identification of potentially infected health care workers and for early real time DNA-polymerase chain reaction testing prioritization. This would limit the contact time of potentially infected health care workers with the patients but also enable efficient use of the limited testing kits. OBJECTIVE: The aims of this study are to describe an early warning in-hospital mobile risk analysis app for screening COVID-19 and to determine the feasibility and user-friendliness of the app among health care workers. METHODS: The primary result of this research project was the development of a mobile-based daily early warning system for in-hospital transmission of COVID-19. Overall, the Early Warning System for In-Hospital Transmission of COVID-19 (EWAS) mobile app was found to be feasible, with over 69% of the health care workers having logged more than 67% of the required times. Over 93% of the participants reported that the tool was easy to use. RESULTS: The primary result of this research project was the development of a mobile-based daily early warning system for in-hospital transmission of COVID-19. Overall, the Early Warning System for In-Hospital Transmission of COVID-19 (EWAS) mobile app was found to be feasible, with 69% of the health care workers (69/100) having logged more than 67% of the required times. Of the 100 participants, 93 reported that the tool was easy to use. CONCLUSIONS: The EWAS mobile app is a feasible and user-friendly daily risk scoring tool for preventing in-hospital transmission of COVID-19. Although it was not designed to be a diagnostic tool but rather a screening tool, there is a need to evaluate its sensitivity in predicting persons likely to have contracted COVID-19.
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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 | 0,004 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».