Health Data Anonymization and Privacy
summary
Anonymization is quantified: algorithms produce measurable re‑identification risk scores and analysts balance those against measurable data utility to set defensible thresholds. Linked EMR, claims, pharmacy and lab datasets cover the full continuum of care and reveal concrete signals such as post‑discharge adverse drug events; adding education, justice, immigration and device (phone and implantable) data multiplies analytic power. A commercial toolchain (startup Privacy Analytics) and governance for stigmatizing inferences are part of the solution.
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