Which statement best defines big data in healthcare?

Which statement best defines big data in healthcare?

• A. Small, single-source datasets from a single hospital.
• B. Personal anecdotes without clinical data.
• C. Only genomic data.
• D. Large scale health information collected from multiple sources such as Medicare claims, hospital admissions, health surveys, wearables, genomics, and pathology.

Answer: (D)

Big data in healthcare means bringing together very large, diverse sets of health information from many different sources to enable broad analysis and insights. The statement that describes this best emphasizes data collected at scale from multiple origins—such as Medicare claims, hospital admissions, health surveys, wearables, genomics, and pathology. This variety and volume allow patterns, trends, and outcomes to be studied across populations, support predictive analytics, and inform care and policy decisions in both national and global contexts. Why this fits: healthcare data aren’t limited to a single source or type. By combining claims data with clinical records, lab results, and even wearable sensor information, researchers and clinicians can see the full picture of health, treatment pathways, and outcomes, which is what big data is meant to capture. Why the other options don’t fit: small, single-source datasets miss the scale and heterogeneity that define big data; personal anecdotes lack the structured, analyzable clinical data needed for meaningful patterns; focusing on only one data type (like genomics) ignores the breadth of information that supports broad analyses and population health insights.

Big data in healthcare means bringing together very large, diverse sets of health information from many different sources to enable broad analysis and insights. The statement that describes this best emphasizes data collected at scale from multiple origins—such as Medicare claims, hospital admissions, health surveys, wearables, genomics, and pathology. This variety and volume allow patterns, trends, and outcomes to be studied across populations, support predictive analytics, and inform care and policy decisions in both national and global contexts.

Why this fits: healthcare data aren’t limited to a single source or type. By combining claims data with clinical records, lab results, and even wearable sensor information, researchers and clinicians can see the full picture of health, treatment pathways, and outcomes, which is what big data is meant to capture.

Why the other options don’t fit: small, single-source datasets miss the scale and heterogeneity that define big data; personal anecdotes lack the structured, analyzable clinical data needed for meaningful patterns; focusing on only one data type (like genomics) ignores the breadth of information that supports broad analyses and population health insights.