<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Data Management on Formize.com Blog</title><link>https://blog.formize.com/categories/data-management/</link><description>Recent content in Data Management on Formize.com Blog</description><generator>Hugo</generator><language>en</language><atom:link href="https://blog.formize.com/categories/data-management/index.xml" rel="self" type="application/rss+xml"/><item><title>Accelerating Synthetic Data Traceability for Healthcare Research with Formize</title><link>https://blog.formize.com/synthetic-data-traceability-for-healthcare-research/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.formize.com/synthetic-data-traceability-for-healthcare-research/</guid><description>&lt;h1 id="accelerating-synthetic-data-traceability-for-healthcare-research-with-formize">Accelerating Synthetic Data Traceability for Healthcare Research with Formize&lt;/h1>
&lt;h2 id="why-synthetic-data-traceability-matters-in-healthcare">Why Synthetic Data Traceability Matters in Healthcare&lt;/h2>
&lt;p>Healthcare AI projects rely on massive datasets that often contain protected health information (PHI). To protect patient privacy while still enabling high‑quality model training, organizations turn to &lt;strong>synthetic data&lt;/strong>—artificially generated records that mimic the statistical properties of real patient data.&lt;/p>
&lt;p>However, synthetic data introduces a new compliance challenge: &lt;strong>traceability&lt;/strong>. Regulators, ethics boards, and research sponsors increasingly demand evidence that:&lt;/p></description></item></channel></rss>