Question 1

What is synthetic clinical data?

Accepted Answer

Synthetic clinical data is newly generated data that statistically resembles a real patient population without representing actual patients. It can be used for AI training, software testing, analytics, clinical trial simulation, and controlled partner sharing when privacy and utility are properly validated.

Question 2

Is synthetic data automatically HIPAA compliant?

Accepted Answer

No. Synthetic data is not automatically HIPAA compliant. A healthcare organization must demonstrate that the generated dataset does not retain, reveal, or allow reconstruction of identifiable patient information. This requires privacy testing, documented methodology, access controls, and typically an Expert Determination.

Question 3

How do you validate synthetic healthcare data?

Accepted Answer

Synthetic healthcare data should be validated across three areas: privacy, fidelity, and utility. Privacy testing includes membership inference and attribute disclosure tests. Fidelity testing checks distributions and correlations. Utility testing measures whether models trained on synthetic data perform effectively on held-out real-world data.

Question 4

What is a membership inference attack?

Accepted Answer

A membership inference attack tests whether an attacker can determine if a particular patient's record was included in the dataset used to train a synthetic data generator. Low attacker advantage indicates lower risk that the synthetic dataset exposes training-set membership.

Question 5

What documentation is needed for an HHS OCR audit of a synthetic data program?

Accepted Answer

A defensible synthetic data program should maintain an Expert Determination report, generation methodology specification, privacy validation report, fidelity and utility validation report, subgroup bias analysis, provenance and lineage records, workforce training evidence, access logs, and Business Associate Agreements where vendors handle protected health information.

Question 6

What is TSTR validation in synthetic data?

Accepted Answer

TSTR means Train on Synthetic, Test on Real. It measures whether an AI model trained on synthetic data performs well when evaluated against a held-out real-world dataset. It is a core utility test for determining whether synthetic data is useful for the intended clinical AI application.

Question 7

Which tools are used to generate synthetic healthcare data?

Accepted Answer

Common tools include CTGAN for tabular healthcare data, TabDDPM for diffusion-based generation, Synthea for longitudinal synthetic patient records, and commercial platforms such as MOSTLY AI, Replica Analytics, and Tonic.ai. The right tool depends on the data type, use case, privacy requirements, and validation strategy.

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