Architecture Assessment of the Chilean Epidemiological Surveillance System for Notifiable Diseases (EPIVIGILA): Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: To fulfill their epidemiological vigilance function, authorities require valid, complete, timely, precise, and reliable information. Advancements in new technologies have facilitated public health control through vigilance systems for notifiable diseases; these systems can gather large numbers of simultaneous notifications, process a wide array of data, and deliver updated information in real time to relevant decision-makers. A large worldwide deployment of new information technologies was seen during the COVID-19 pandemic; these technologies proved to be efficient, resourceful tools . Platform developers should seek self-evaluation strategies to optimize functionality or improve the capacity of national vigilance systems. These tools exist in the Latin American region at various development stages, although publications reporting architectural characteristics of these tools are scarce. International publications are more abundant a nd serve as a basis for comparing the standards that need to be met. OBJECTIVE: This study aimed to assess the architecture of the Chilean epidemiological surveillance system for notifiable diseases (EPIVIGILA), as compared to that of the international systems reported in scientific publications. METHODS: A search for scientific publications was conducted to identify systematic reviews that documented the architectural characteristics of disease notification and vigilance systems. EPIVIGILA was compared to other systems from countries in Africa, the Americas, Asia, Europe, and Oceania. RESULTS: The following aspects of the architecture were identified: (1) notification provenance, (2) minimum data set, (3) database users, and (4) data quality control. The notifying organizations, including hospitals, clinics, laboratories, and medical consultation offices, were similar among the 13 countries analyzed; this contrasted with Chile, where the reporting agent is the physician who can belong to an organization. The minimum data set include patient identification, disease data, and general codifications. EPIVIGILA includes all these elements, in addition to symptomatology, hospitalization data, type of medicine and treatment result, and laboratory test types. The database users or data analyzers include public health organizations, research organizations, epidemiological organizations, health organizations or departments, and the Centers for Disease Control and Prevention. Finally, for data quality control, the criteria most often used were completeness, consistency, validity, timeliness, accuracy, and competencies. CONCLUSIONS: An efficient notification and vigilance system must be capable of promptly identifying probable risks as well as incidence and prevalence of the diseases under surveillance. EPIVIGILA has been shown to comply with high quality and functionality standards, at the level of developed countries, by achieving total national coverage and by providing timely, trustworthy, and complete information at high-security levels, thus obtaining positive assessment from national and international authorities.
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 | 0,009 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 |
| 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 ».