Description
Healthcare datasets contain both personally identifiable information (PII) and personal health information (PHI) that needs to be de-identified in order to protect patient confidentiality and ensure HIPAA compliance. This privacy data is easily detected when it’s provided in columns labeled with names such as “SSN,” First Name,” “Full Name,” and “DOB;” however, it is much harder to detect when it is hidden within columns labeled “Doctor Notes,” “Diagnoses,” or “Comments.” HealthVerity, a leader in the HIPAA-compliant exchange of real-world data (RWD) to uncover patient, payer and genomic insights and power innovation for the healthcare industry, ensures healthcare datasets are de-identified from PII and PHI using elaborate privacy procedures. During this session, we will demonstrate how to use a low-code/no-code platform to simplify and automate data pipelines that leverage prebuilt ML models to scan data for PHI/PII leakage and quarantine those rows in Unity Catalog when leakage is identified and move them to a Databricks clean room for analysis. Talk by: Pouya Barrach-Yousefi and Simon King Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databrick…
Description from YouTube. Full content on the video page.
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