Facilitating Public Health Action through Surveillance Dashboards
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Notice bibliographique
Résumé
ObjectiveTo address the limitations of traditional static surveillancereporting by developing in-house infrastructure to create and maintaininteractive surveillance dashboards.IntroductionTraditionally, public health surveillance departments collect,analyze, interpret, and package information into static surveillancereports for distribution to stakeholders. This resource-intensiveproduction and dissemination process has major shortcomings thatimpede end users from optimally utilizing this information for publichealth action. Often, by the time traditional reports are ready fordissemination they are outdated. Information can be difficult to findin long static reports and there is no capability to interact with thedata by users. Instead, ad hoc data requests are made, resulting ininefficiencies and delays.Use of electronic dashboards for surveillance reporting is notnew. Many public health departments have worked with informationtechnology (IT) contractors to develop such technically sophisticatedproducts requiring IT expertise. The technology and tools now existto equip the public health workforce to develop in-house surveillancedashboards, which allow for unprecedented speed, flexibility, andcost savings while meeting the needs of stakeholders. At AlbertaHealth Services (AHS), in-house, end-to-end dashboard developmentinfrastructure has been established that provides epidemiologists anddata analysts full capabilities for effective and timely reporting ofsurveillance information.MethodsAn internal assessment of the available resources and infrastructurewithin AHS was conducted to iteratively develop a new analyticsmodel that provides a foundation for in-house dashboard developmentcapacity. We acquired SAS® and Tableau® software and conductedinternal training for skills development and to transition staff to thenew model. This model is highlighted below using our respiratoryvirus surveillance (RVS) dashboard as an example.For the RVS dashboard, stakeholder engagements wereconducted to understand the end users’ needs. Next, data access wasimproved, where possible, by securing direct access to source data(e.g. emergency department visits for influenza like illness (ILI),Health Link calls, hospital admissions, etc.) on existing databaseservers. SAS® code was written for routinely connecting withmultiple data sources, data management and analysis, data qualityassurance, and posting summary data on a secure Oracle® server.The Tableau® dashboard development application was then usedto connect to the summary data on the Oracle® server, create theinteractive dashboards and publish the final products to the AHSTableau server environment. Key users were consulted in the iterativedevelopment of the interface to optimize usability and relevantcontent.Finally, the product was promoted to stakeholders with acommitment to use their feedback to drive continuous improvement.ResultsIn-house generated surveillance dashboards provide more timelyaccess to comprehensive surveillance information for a broadaudience of over 108,000 AHS employees; within as little as 3 hoursof all data being available. They facilitate user-directed deep divesinto the data to understand a more complete surveillance picture aswell as stimulating hypothesis generation. Additionally they enhanceproductivity of personnel, by significantly reducing response timesfor ad hoc request and to generate reports, freeing up more time torespond to other emerging public health issues.Looking specifically at the RVS dashboard, its ability to bring allrelevant surveillance information to one place facilitates valuablediscussions during status update meetings throughout the influenzaseason. Among other things it has allowed Medical Officers ofHealth, emergency department staff, epidemiologists and others tomake informed decisions pertaining to public messaging, the needfor reallocating resources, such as staffing and handling the burden ofILI patients, as well as determining the necessity of opening influenzaassessment centers.ConclusionsSurveillance dashboards can facilitate public health action byassembling comprehensive information in one place in a timelymanner so that informed decisions can be made in emerging situations.
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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 | 0,010 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 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 découle