ArticleApplied clinical informatics2021
Health Intelligence Atlas: A Core Tool for Public Health Intelligence.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Design, Application, and Actionability of US Public Health Data Dashboards: Scoping Review.Journal of medical Internet research · 2025Article
- Greater agreement is required to harness the potential of health intelligence: a critical interpretive synthesis.Journal of clinical epidemiology · 2023Review
- Article
- Improving COVID-19 Research of University Hospitals in Germany: Formative Usability Evaluation of the CODEX Feasibility Portal.Applied clinical informatics · 2022Article
- Your Tweets Matter: How Social Media Sentiments Associate with COVID-19 Vaccination Rates in the US.Online journal of public health informatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe dramatic increase in complexity and volume of health data has challenged traditional health systems to deliver useful information to their users. The novel coronavirus disease 2019 (COVID-19) pandemic has further exacerbated this problem and demonstrated the critical need for the 21st century approach. This approach needs to ingest relevant, diverse data sources, analyze them, and generate appropriate health intelligence products that enable users to take more effective and efficient actions for their specific challenges.
objectivesThis article characterizes the Health Intelligence Atlas (HI-Atlas) development and implementation to produce Public Health Intelligence (PHI) that supports identifying and prioritizing high-risk communities by public health authorities. The HI-Atlas moves from post hoc observations to a proactive model-based approach for preplanning COVID-19 vaccine preparedness, distribution, and assessing the effectiveness of those plans.
resultsDetails are presented on how the HI-Atlas merged traditional surveillance data with social intelligence multidimensional data streams to produce the next level of health intelligence. Two-model use cases in a large county demonstrate how the HI-Atlas produced relevant PHI to inform public health decision makers to (1) support identification and prioritization of vulnerable communities at risk for COVID-19 spread and vaccine hesitancy, and (2) support the implementation of a generic model for planning equitable COVID-19 vaccine preparedness and distribution.
conclusionThe scalable models of data sources, analyses, and smart hybrid data layer visualizations implemented in the HI-Atlas are the Health Intelligence tools designed to support real-time proactive planning and monitoring for COVID-19 vaccine preparedness and distribution in counties and states.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.