Healthcare Data Visualization: diferenças entre revisões

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= Healthcare Data Visualization =
= Healthcare Data Visualization =
The graphical representation of complex health-related information, has its reason to be in providing actionable insights to clinicians, researchers, administrators, policymakers, and patients. By transforming raw and heterogeneous datasets, such as electronic health records (EHRs), claims databases, or public health surveillance data, into intuitive charts, graphs, maps, dashboards, and interactive tools, healthcare data visualization can facilitate evidence-based decision-making, improve care coordination, and ultimately enhance patient outcomes. It stands as a critical component of Health Data Science, where advanced analytics, statistical modeling, machine learning, and domain expertise converge to accelerate learning health systems (Friedman et al., 2010 <ref name="achieving">Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med. 10 de novembro de 2010;2(57):57cm29.  
The graphical representation of complex health-related information, has its reason to be in providing actionable insights to clinicians, researchers, administrators, policymakers, and patients. By transforming raw and heterogeneous datasets, such as [[Electronic Health Records]] (EHRs), claims databases, or public health surveillance data, into intuitive charts, graphs, maps, dashboards, and interactive tools, healthcare data visualization can facilitate evidence-based decision-making, improve care coordination, and ultimately enhance patient outcomes. It stands as a critical component of Health Data Science, where advanced analytics, statistical modeling, machine learning, and domain expertise converge to accelerate learning health systems (Friedman et al., 2010 <ref name="achieving">Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med. 10 de novembro de 2010;2(57):57cm29.  
</ref>; McGinnis et al., 2013 <ref name="best care">Committee on the Learning Health Care System in America, Institute of Medicine. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America [Internet]. Smith M, Saunders R, Stuckhardt L, McGinnis JM, editores. Washington (DC): National Academies Press (US); 2013 [citado 6 de dezembro de 2024]. Disponível em: http://www.ncbi.nlm.nih.gov/books/NBK207225/ </ref>).
</ref>; McGinnis et al., 2013 <ref name="best care">Committee on the Learning Health Care System in America, Institute of Medicine. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America [Internet]. Smith M, Saunders R, Stuckhardt L, McGinnis JM, editores. Washington (DC): National Academies Press (US); 2013 [citado 6 de dezembro de 2024]. Disponível em: http://www.ncbi.nlm.nih.gov/books/NBK207225/ </ref>).
Borrowing from the work by David McCandless (Information is beautiful, 2010 <ref name="beautiful">McCandless D. Information is beautiful. New ed. London: Collins; 2012.  
Borrowing from the work by David McCandless (Information is beautiful, 2010 <ref name="beautiful">McCandless D. Information is beautiful. New ed. London: Collins; 2012.  
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*'''Explainable AI (XAI):'''
*'''Explainable AI (XAI):'''
**Visualizing model logic for trust and adoption.
**Visualizing model logic for trust and adoption.
**[File:https://miro.medium.com/v2/resize:fit:720/format:webp/1*0MdmP6Q68PKaKYhM08smQA.png|Explanation framework]
**[[Ficheiro:Https://miro.medium.com/v2/resize:fit:720/format:webp/1*0MdmP6Q68PKaKYhM08smQA.png|miniaturadaimagem|Explanation framework]]
*'''LLM Agents and Conversational Interfaces:'''
*'''LLM Agents and Conversational Interfaces:'''
**Voice or text queries of healthcare data, furthering accessibility and inclusiveness.
**Voice or text queries of healthcare data, furthering accessibility and inclusiveness.

Edição atual desde as 19h25min de 23 de dezembro de 2024

Authors (HEADS-INFORM 2025):

  • Henrique Pereira
  • Olívia Oliveira

Healthcare Data Visualization

The graphical representation of complex health-related information, has its reason to be in providing actionable insights to clinicians, researchers, administrators, policymakers, and patients. By transforming raw and heterogeneous datasets, such as Electronic Health Records (EHRs), claims databases, or public health surveillance data, into intuitive charts, graphs, maps, dashboards, and interactive tools, healthcare data visualization can facilitate evidence-based decision-making, improve care coordination, and ultimately enhance patient outcomes. It stands as a critical component of Health Data Science, where advanced analytics, statistical modeling, machine learning, and domain expertise converge to accelerate learning health systems (Friedman et al., 2010 [1]; McGinnis et al., 2013 [2]). Borrowing from the work by David McCandless (Information is beautiful, 2010 [3]) and Hans Rosling et al (Factfulness, 2018 [4]), although not healthcare specific, Information can be derived from data in many ways, but from them we can clearly deduce that revealing hidden insights from data, focusing on connecting a narrative to validated facts and then demonstrating that connection with visual elegance can often illuminate, otherwise indiscernable, datasets. Healthcare Data Visualization can be trully enhanced from the field of data communication:

  • a) a contextualized, fact-based presentations of global trends can debunk misconceptions and foster a more balanced understanding of the world (Hans Rosling, 2018 [3]);
  • b) also, we must no forget the power of aesthetically engaging and conceptually clear data visualizations, in order to capture attention to specific data points, and make complex concepts accessible (David McCandless, 2010 [4])
  • c) keep in mind the unofficial universal principles:
    • Clarity,
    • Context,
    • Storytelling,
    • Design appeal

Importance and Applications in Healthcare

In the rapidly evolving healthcare landscape, data visualization has enabled stakeholders to discern trends, detect patterns, and find anomalies that could, otherwise, remain hidden in large, complex datasets (Dunskiy, 2023 [5]; Kodjin, 2023 [6]). Public health officials can identify time series and clusters of disease outbreaks, while hospital administrators can track patient flow, bed occupancy, and resource utilization at a glance (Chornyy, 2024 [7]; Rind et al., 2013 [8]; West et al., 2015 [9]). Clinicians benefit from patient-level visual analytics, such as longitudinal line charts tracking vital signs, biomedical results or risk scores, providing immediate contextual insights for improved diagnosis and treatment (Evans, 2024 [10]). By balancing evidence-based rigor and an engaging visual design, healthcare data visualization can better inform at diferent levels: from the patient level, to population health management, to highlight social determinants of health, and even promote data-driven policies (Friedman et al., 2010 [1]; McGinnis et al., 2013 [2]).

Best Practices

Effective healthcare data visualization and sharing requires a certain fusion of rigorous methodology, thoughtful design, adherence to user needs, and also a bit of creativity allied to some degree of Art. Nevertheless there are some best practices (possibly derived from the unofficial universal principles we stated before) which we have to pay a bit of attention:

  • Clinical Context is Key: Tailor visuals to user needs. A physician may require a dashboard focused on medication adherence and vital signs, while a public health analyst might prefer geographic and temporal trends (Dunskiy, 2023 [5]; Rind et al., 2013 [8]).
  • Simplicity and Clarity: Avoid overcomplication! Less is more! Foundational works like Cleveland’s The Elements of Graphing Data (1985) [11] and Tufte’s The Visual Display of Quantitative Information (2001)[12] underscore minimalism and clarity, while Rosling’s Factfulness[4] shows how grounding data in reality (facts) and avoiding sensationalism leads to more meaningful insights.
  • Color and Accessibility: Choose accessible color palettes and ensure legends, labels, and contrasts are clear (Chornyy, 2024 [7]; Ware, 2020 [13]). McCandless’s visuals in Information is Beautiful[3] underscore how carefully selected colors and aesthetics enhance user engagement and comprehension. Also keep in mind that some audience might not “view” things like you (i.e. daltonism ↔ color codes for daltonism, blindness ↔ braille)
  • Data Integrity and Validation: Rely only on validated data to build trust. Misrepresentations lead to misleading conclusions and can erode confidence (Abudiyab & Alanazi, 2022 [14]). Whenever data transformation is made to better understant the information to be captured, that should always be documented and pertinent
  • Compliance with Regulations and Privacy: Follow proper guidelines and de-identify patient data (i.e. GDPR in Europe). Respecting privacy and building trust are fundamental (Evans, 2024 [10]; West et al., 2015 [9]).

Common Hints and Tips

  • Iterative Prototyping and User-Centered Design: If the end user is not you (i.e. a Dashboard or graphical representation that is to be presented by someone else) remember to engage end-users from the start. Iteration and refinement are key to ensuring that the final visualization meets the predefined goals, be them clinical or operational (Shneiderman & Plaisant, 2010 [15]).
  • Interactive Elements: Incorporate tooltips, filters, and drill-down functionalities (especially in dashboards). Interactivity, as seen in many successful data storytelling examples from McCandless and in other demonstrations at AMIA and IEEE VIS conferences, encourages deeper exploration (Rind et al., 2013 [8]).
  • Storytelling Approach: Present data as a compelling narrative! Weaving data into visually compelling narratives might not be an easy task, and specially healthcare data (usually highly dimensional), which should also tell a cohesive story. By structuring visualizations to move from overview to detail (top-down), and adding explanatory annotations, Health Data Scientists can engage audiences more effectively (Few, 2012 [16]; Few, 2013 [17]; Few, 2013 [18]).
  • Proper Scaling and Context: Normalize data (if needed) and highlight reference ranges or care quality benchmarks. Contextualizing information, ensures viewers understand both the broader landscape and the finer details (Kodjin, 2023 [6]).

Common Visualization Types and Their Healthcare Use Cases

Whether using line charts to illustrate changes in patient vitals or Sankey diagrams to depict patient flow, the goal is to combine factual accuracy with clear, engaging design. The following table may lack in exaustiveness, but sill has very good examples:

Chart Type Healthcare Use Cases Best Practices References
Line Chart Monitoring patient vitals, seasonal disease trends Keep it simple, highlight key events, clarify axes Python & R Graph Galleries [19] [20], Cleveland (1985)[11], Tufte (2001)[12]
Bar Chart Comparing adherence rates, resource allocation Sort bars meaningfully, use consistent scales Python & R Graph Galleries [19] [20], Few (2012)[16]
Histogram Distributions of age, lab values Appropriate bin width, highlight clinical thresholds Python & R Graph Galleries [19] [20], Tufte (2001)[12]
Box/Violin Plot Distribution of blood pressure or satisfaction scores Emphasize medians, quartiles, outliers Python & R Graph Galleries [19] [20], Wilkinson (2005)[21]
Scatter Plot BMI vs. blood pressure correlations Add trend lines, consider transparency for large datasets Python & R Graph Galleries [19] [20], Ware (2021)[13]
Heatmap Correlation matrices, ICU occupancy patterns, antibiotics resistance surveillance Balanced color maps, clear legend Python & R Graph Galleries [19] [20]
Choropleth/Maps Disease prevalence by region, resource distribution Color gradients, scale bars, contextual overlays Python & R Graph Galleries [19] [20], Dicker et al. (2006)[22]
Network Graph Referral patterns, epidemiological links Cluster for clarity, highlight key nodes Python & R Graph Galleries [19] [20], Rind et al. (2013)[8]
Sankey Diagram Patient flow through care pathways Limit complexity, consistent color coding Python & R Graph Galleries [19] [20], West et al. (2015)[9]
Radar/Spider Chart Comparing performance metrics Limit variables, normalize scales, consider alternatives Python & R Graph Galleries [19] [20], Few (2012)[16]

Common Pitfalls in Health Data Visualization (and How to Avoid Them)

  • Overcomplication: Stick to clear, familiar visuals.
  • Data Misinterpretation: Include proper labeling, annotations, and definitions (Anello, 2023 [23]; Dicker et al. (2006)[22]).
  • Ignoring User Feedback (when dashboarding): Engage users early, iterating design, ensuring even complex clinical concepts remain understandable.
  • Privacy Violations: Always anonymize patient data or simply use aggregations of data (avoid focusing on single cases).

Platforms, and Tools

There is a plethora of tools to be used in Data Visualization and the following are just the most used.

Open Source Solutions

  • Python (Matplotlib, Seaborn, Plotly, Bokeh): Flexible, widely used, good community support[24].
  • R (ggplot2, Shiny): The grammar-of-graphics approach (Wilkinson, 2005 [21]) and interactive dashboarding with Shiny.
  • D3.js: Maximum customization and interactivity, well-aligned with the creative ethos demonstrated in McCandless’s work[3].

Commercial and Enterprise Platforms

  • Tableau, Power BI, QlikView/Qlik Sense, Sisense, Looker: Offer varying degrees of user-friendliness and scalability (Dunskiy, 2023 [5]; Evans, 2024[10]).

Workflows and Frameworks

Because Data Visualization does not “sprout” espontaneously from anywhere, there are workflows and frameworks that are associated in a way or the other:

  • Extract Transform and Load (ETL) Pipelines: Ensures data cleanliness and correct structure before visualization (Friedman et al., 2010 [1]).
  • Machine Learning Integration: Displaying of model outputs visually for clinical validation (Rind et al., 2013[8])
  • Self-Service BI: Empower non-technical stakeholders to create or adjust visualizations. This approach is mainly commercial

Core and Widely used Visualization Frameworks

Framework/Platform Pros Cons
Matplotlib/Seaborn (Python) - Robust, stable, and well-documented- Integrates seamlessly with Python ML/data stacks- Good for static, publication-quality figures - Limited interactivity compared to modern JS-based tools- More code-heavy; requires boilerplate for complex visuals
Plotly (Python/R) - Interactive charts out-of-the-box- Supports multiple languages (Python, R, JS)- Strong community and extensive gallery - Enterprise features require paid licensing- Less customization depth than D3.js
ggplot2 (R) - Elegant, grammar-of-graphics approach- Large ecosystem of extensions (ggthemes, gganimate)- Ideal for statistical plots and quick data exploration - Interactivity not native (requires Shiny or Plotly integration)- Steeper learning curve for those unfamiliar with grammar-of-graphics
D3.js (JavaScript) - Maximum customization and flexibility- Can build highly interactive, innovative visuals- Vast community and extensive examples - Steep learning curve- Requires front-end development skills (HTML/CSS/JS)- More time-consuming for standard charts
Tableau - User-friendly interface, minimal coding- Strong built-in mapping and dashboarding features- Large support community and training resources - Proprietary and potentially costly for large teams- Limited customization vs. code-based solutions
Power BI - Integrates well with Microsoft ecosystem- Straightforward interface for quick dashboards- Affordable licensing for some tiers - Less flexible customization compared to coding libraries- Dependent on Microsoft stack for seamless integration
QlikView/Qlik Sense - Associative data model for complex, multi-source data- Strong self-service analytics features- Good at handling large, disparate datasets - Licensing and training costs- Steeper learning curve for non-technical users
Shiny (R) - Rapid development of interactive dashboards- Integrates with R’s analytical capabilities- Good for prototyping EHR analytics tools - Performance can lag with very large datasets- Requires knowledge of R and reactive programming

Data Preparation and Analytics Workflows (Not Visualization-First)

Tool Pros Cons
Alteryx - User-friendly drag-and-drop interface; - Strong data blending, prep, and spatial analytics, - Integrates well with downstream visualization tools - Proprietary with licensing costs- Less flexible for custom-coded solutions, - Not a visualization tool by itself
KNIME - Open-source core and modular node-based workflows, - Extensive integration with Python, R, and ML libraries, - Strong community support and active development - Less intuitive than Alteryx for some users, - Limited native visualization, often requires external tools, - Steeper learning curve for non-technical users

Other Visualization-First Tools (RAWGraphs, Datawrapper, Superset)

Tool Pros Cons
RAWGraphs - Open-source, web-based tool for unique and creative chart types- No coding required, great for quick exploratory visualization- Ideal for early-stage analysis or presentation graphics - Not designed for fully interactive dashboards- Limited customization and interactivity- Doesn’t easily scale into production workflows
Datawrapper - Easy-to-use browser-based chart and map creation- Clean, publication-quality visuals suitable for reporting- Ideal for non-technical users (policy briefs, patient info) - Mainly static or lightly interactive visualizations- Limited flexibility in customization- May require enterprise plans for advanced features
Superset (Apache) - Open-source BI and data exploration platform- Connects to many SQL databases easily- Provides a variety of interactive charts and dashboards - Fewer chart types compared to specialized JS libraries- Requires some technical know-how for setup and customization- Not as “drag-and-drop” friendly as commercial BI tools

Future? Or today’s present?

This section is only meant to tease past, present and future of Health Data Visualization

Pursue Cross-Disciplinary Inspirations

While this article primarily draws from healthcare-specific research and best practices, insights from other domains can enrich our approach. Incorporating these into healthcare data visualization ensures that metrics and analytics not only inform but also inspire evidence-based action and continuous improvement.

Conclusion

Healthcare Data Visualization stands at the intersection of clinical insight, analytical rigor, and thoughtful design. By making sure we follow a few best practices, leveraging appropriate tools, and always respect the privacy and regulatory standards, health data scientists can really turn raw data into actionable insights without sacrificing the interpertability of shown data. In doing so, healthcare visualizations not only inform stakeholders, but also inspire informed decision-making, improve patient outcomes, and contribute to a more equitable, efficient, and evidence-based healthcare ecosystem.

References

  1. ↑ 1,0 1,1 1,2 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med. 10 de novembro de 2010;2(57):57cm29.
  2. ↑ 2,0 2,1 Committee on the Learning Health Care System in America, Institute of Medicine. Best Care at Lower Cost: The Path to Continuously Learning Health Care in America [Internet]. Smith M, Saunders R, Stuckhardt L, McGinnis JM, editores. Washington (DC): National Academies Press (US); 2013 [citado 6 de dezembro de 2024]. Disponível em: http://www.ncbi.nlm.nih.gov/books/NBK207225/
  3. ↑ 3,0 3,1 3,2 3,3 McCandless D. Information is beautiful. New ed. London: Collins; 2012.
  4. ↑ 4,0 4,1 4,2 Rosling H, Rosling O, Rönnlund AR. Factfulness: ten reasons we’re wrong about the world--and why things are better than you think. First edition. New York: Flatiron Books; 2018. 342 p.
  5. ↑ 5,0 5,1 5,2 Dunskiy I. Healthcare Data Visualization. 2023 [citado 6 de dezembro de 2024]. Healthcare Data Visualization: Examples & Key Benefits. Disponível em: https://demigos.com/blog-post/healthcare-data-visualization/
  6. ↑ 6,0 6,1 Krylov A. Healthcare Data Visualization: Examples, Benefits | Kodjin [Internet]. 2023 [citado 6 de dezembro de 2024]. Disponível em: [1](https://kodjin.com/blog/healthcare-data-visualization-importance-benefits/)
  7. ↑ 7,0 7,1 Chornyy R. Healthcare Data Visualization: Insights for Better Decision-Making. 2024 [citado 6 de dezembro de 2024]. Data Visualization in Healthcare: Examples, Benefits & Challenges. Disponível em: [2](https://binariks.com/blog/data-visualization-in-healthcare/)
  8. ↑ 8,0 8,1 8,2 8,3 8,4 Rind A. Interactive Information Visualization to Explore and Query Electronic Health Records. FNT in Human–Computer Interaction. 2013;5(3):207–98.
  9. ↑ 9,0 9,1 9,2 West VL, Borland D, Hammond WE. Innovative information visualization of electronic health record data: a systematic review. Journal of the American Medical Informatics Association. 1 de março de 2015;22(2):330–9.
  10. ↑ 10,0 10,1 10,2 Evans H. Velvetech. 2024 [citado 6 de dezembro de 2024]. Data Visualization in Healthcare: Navigating the Impact. Disponível em: [3](https://www.velvetech.com/blog/healthcare-data-visualization/)
  11. ↑ 11,0 11,1 Cleveland WS. The elements of graphing data. 10.[print.]. Monterey, Cal: Wadsworth; 1989. 323 p.
  12. ↑ 12,0 12,1 12,2 Tufte ER. The visual display of quantitative information. 2nd ed., 8th print. Cheshire, Conn: Graphics Press; 2001. 190 p.
  13. ↑ 13,0 13,1 Ware C. Information Visualization [Internet]. 4th ed. Elsevier; 2021 [citado 6 de dezembro de 2024]. Disponível em: [4](https://linkinghub.elsevier.com/retrieve/pii/C20160023951)
  14. ↑ Abudiyab NA, Alanazi AT. Visualization Techniques in Healthcare Applications: A Narrative Review. Cureus. 11 de novembro de 2022;14(11):e31355.
  15. ↑ Shneiderman B, Plaisant C. Designing the user interface: strategies for effective human-computer interaction. 5th ed. Boston: Addison-Wesley; 2010. 606 p.
  16. ↑ 16,0 16,1 16,2 Few S. Show me the numbers: designing tables and graphs to enlighten. second edition. Burlingame, Calif: Analytics Press; 2012. 351 p.
  17. ↑ Few S. Information dashboard design: displaying data for at-a-glance monitoring. Second edition. Burlingame, California: Analytics Press; 2013. 246 p.
  18. ↑ Few S. 35. Data Visualization for Human Perception. Em: The Encyclopedia of Human-Computer Interaction [Internet]. 2nd ed. Interaction Design Foundation - IxDF; 2014 [citado 7 de dezembro de 2024]. Disponível em: [5](https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perception)
  19. ↑ 19,0 19,1 19,2 19,3 19,4 19,5 19,6 19,7 19,8 19,9 - Holtz Y. The Python Graph Gallery. 2024 [citado 7 de dezembro de 2024]. Python Graph Gallery. Disponível em: [6](https://python-graph-gallery.com/)
  20. ↑ 20,0 20,1 20,2 20,3 20,4 20,5 20,6 20,7 20,8 20,9 - Holtz Y. The R Graph Gallery. 2024 [citado 7 de dezembro de 2024]. The R Graph Gallery – Help and inspiration for R charts. Disponível em: [7](https://r-graph-gallery.com/)
  21. ↑ 21,0 21,1 Wilkinson L. The Grammar of Graphics [Internet]. New York: Springer-Verlag; 2005 [citado 6 de dezembro de 2024]. (Statistics and Computing). Disponível em: [8](http://link.springer.com/10.1007/0-387-28695-0)
  22. ↑ 22,0 22,1 - Dicker RC, Coronado F, Koo D, Parrish RG. Principles of epidemiology in public health practice; an introduction to applied epidemiology and biostatistics. [Internet]. 3rd ed. 2006 [citado 6 de dezembro de 2024]. (Self-study course). Disponível em: [9](https://stacks.cdc.gov/view/cdc/6914)
  23. ↑ - Anello L. Medium. 2024 [citado 6 de dezembro de 2024]. Data Visualization Techniques for Healthcare Data Analysis — Part III. Disponível em: [10](https://towardsdatascience.com/data-visualization-techniques-for-healthcare-data-analysis-part-iii-7133581ba160)
  24. ↑ Medium [Internet]. 2024 [citado 6 de dezembro de 2024]. Data Visualization Techniques for Healthcare Data Analysis. Disponível em: [11](https://python.plainenglish.io/data-visualization-techniques-for-healthcare-data-analysis-23ac1815d7b9)
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