Facilitating Public Health Action through Surveillance Dashboards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 Often, by the time traditional reports are ready fordissemination they are outdated.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it