In healthcare quality improvement, which practice best ensures data visualization ethics?

• A. Use aggregated data, accurate scales, protect patient confidentiality, and obtain approvals for data sharing when needed.
• B. Cherry-pick data to show favorable trends
• C. Truncate axis scales to exaggerate effects
• D. Publish raw patient-identifiable data for transparency

Answer: (A)

Ethical data visualization in healthcare quality improvement means presenting information honestly while protecting patient privacy and following governance for data use. Using aggregated data helps prevent identifying individuals and keeps the focus on overall patterns rather than single patients. Wearing accurate scales is essential because the way axes are labeled or truncated can mislead readers about the magnitude of change or the true variability in the data. Protecting confidentiality means removing identifiers and applying de-identification practices so charts don’t reveal patient identities. Obtaining approvals for data sharing ensures proper oversight, consent where needed, and accountability for how data are used and disseminated. When these principles are followed, visualizations convey truthfulness and trust, supporting responsible quality improvement. Cherry-picking data to show favorable trends, truncating axes to exaggerate effects, or publishing raw patient-identifiable data undermine trust and privacy.