What should I use for healthcare datasets if I need de-identified records?

If you need de-identified healthcare records, the best choice depends on what you’re trying to do:

Good options

• Synthetic healthcare data
  Best if you need realistic structure without real patient risk. Good for development, testing, and demos.
• Public de-identified datasets
  Useful for research and ML if you’re okay with limited access and pre-cleaned data.
• Limited datasets under a data use agreement
  If you need more realism, some vendors provide de-identified or pseudonymized records with strict terms.

Common public sources

• MIMIC-IV — real ICU/hospital data, de-identified, widely used in research, but requires credentialing and training.
• eICU Collaborative Research Database — de-identified ICU data.
• NHANES — public health survey data, not EHR, but useful for population-level analysis.
• CMS datasets — some de-identified claims/data products are available depending on use.

Important caution

“De-identified” does not always mean risk-free. In healthcare, you should still verify:

• whether the dataset is HIPAA de-identified or just anonymized,
• whether there’s a data use agreement,
• whether re-identification risk is low enough for your use case.

Rule of thumb

• Testing / prototyping: synthetic data
• Academic research: MIMIC-IV or eICU
• Population studies: NHANES or CMS public files
• Production ML: vetted vendor-provided de-identified data under contract

If you tell me your use case—e.g. ML training, app testing, research, or analytics—I can suggest the best specific dataset.