Data Visualization for Health

Health data is uniquely complex — multi-dimensional, time-sensitive, and critical for decision-making. Good visualization makes data understandable; bad visualization can lead to clinical errors.

Patient-Facing vs. Clinician-Facing Visualizations

The same data serves different purposes for different audiences:

Trend Visualization for Chronic Conditions

Chronic disease management relies on trend visualization:

• Time series: The most common and important health visualization
• Goal ranges: Show target ranges (e.g., blood pressure goals) as shaded areas
• Annotations: Mark significant events (medication changes, doctor visits, symptoms)
• Aggregation: Daily/weekly/monthly views with appropriate granularity

Risk Communication

Communicating risk is one of the hardest design challenges in health:

• Icon arrays: Show risk as people (e.g., 5 out of 100 people) — the most effective format
• Absolute vs. relative risk: Always show absolute risk, never just relative risk
• Comparative risk: Show how the user’s risk compares to the general population
• Uncertainty visualization: Show confidence intervals, not just point estimates

Accessibility in Health Data Visualization

Ensure visualizations work for all users:

• Colorblind-safe: Use patterns, textures, and labels in addition to color
• Data tables: Provide tabular alternatives for all chart data
• Screen reader friendly: Descriptive alt text and ARIA labels
• Zoomable: Allow users to zoom into detailed data
• Printable: Design visualizations that work in black and white

FHIR Data Visualization Patterns

FHIR resources map naturally to visualization patterns:

Related Chapters

• Accessibility as Baseline — Accessible data displays
• Behavioral Design — Visualization for motivation
• API Design & FHIR — Data sources for visualization