ArticleOnline journal of public health informatics2016
Beyond simple charts: Design of visualizations for big health data.
Article in Online journal of public health informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visual communication of public health data: a scoping review.Frontiers in digital health · 2025Pooled it
- Visualizing health inequality data: guidance for selecting and designing graphs and maps.International journal for equity in health · 2025Review
- Visualization of Medical Record with 3D Human Body Models.Healthcare (Basel, Switzerland) · 2025Article
- Interactive exploration of adverse events and multimorbidity in CKD.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2024Observational
- Human factors methods in the design of digital decision support systems for population health: a scoping review.BMC public health · 2024Article
- Interactive exploration of a global clinical network from a large breast cancer cohort.NPJ digital medicine · 2022Article
- QDS-COVID: A visual analytics system for interactive exploration of millions of COVID-19 healthcare records in Brazil.Applied soft computing · 2022Article
- Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review.Journal of medical Internet research · 2022Article
- Building an Interactive Geospatial Visualization Application for National Health Care-Associated Infection Surveillance: Development Study.JMIR public health and surveillance · 2021Article
- Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm.BMC medical informatics and decision making · 2021Observational
- Engagement With a Behavior Change App for Alcohol Reduction: Data Visualization for Longitudinal Observational Study.Journal of medical Internet research · 2020Observational
- Experts' perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study.BMC medical research methodology · 2020Article
- Molecular Epidemiological Information System to Support Management of Multidrug-Resistant Tuberculosis in Thailand: Abstract.Online journal of public health informatics · 2020Article
- Bioelectronic Medicine-Ethical Concerns.Cold Spring Harbor perspectives in medicine · 2019Review
- Usability Evaluation of Visual Representation Formats for Emergency Department Records.Applied clinical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data's utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data.
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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.