Immuta for Healthcare and Life Sciences

Immuta for Healthcare
& Life Sciences

Precision medicine requires personalized data.

Personalized medicine requires an alignment of trust, access, and control of health data. But lack of confidentiality and the risks associated with operationalizing and sharing patient health data present a serious obstacle. Immuta provides personalized access and control of healthcare data, which allows patients, providers, and research institutions to work together for better patient outcomes, with lower costs.

Health Tracking

Health tracking data provides valuable insight into patient wellness, but lack of baseline data and the inability to easily aggregate data makes it difficult to use the data to augment wellness analytics. Immuta lets you catalog, control, and anonymize commercial health tracking data. This includes automated differential privacy on telemetry data, generalization restrictions to minimize linkage attacks on individuals, and the application of purpose-based restrictions to control research vs. clinical applications.


Immuta lets you abstract genomic databases (so you don’t have to copy entire genome sequences), removes the need for VCFs, and applies restrictions at the SNP-level. And because the policies on the genomic data are dynamic, you can apply many types of controls on your data for large-scale machine learning efforts.

Watch a conceptual demo of policy management on genomic data.

Medical Imaging

Currently, managing medical imaging data is complex and very manual. Disparate PACS servers, non-standard radiologist notes, and image quality validation dilute the growing power of machine learning. Immuta abstracts PACS servers, applies controls on imagery to focus on areas of interest (rather than whole image scans), and applies anonymization techniques to DICOM metadata for large-scale studies, allowing the management and exposure of image data to scale.

Patients, providers, and researchers achieve better outcomes with Immuta.

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AI, Healthcare and Our Friends at the University of Chicago

It’s been a big month for Immuta and our friends at the University of Chicago’s School of Medicine. In early May, our CPO Andrew Burt published a piece with Dr. Samuel Volchenboum, an associate professor of pediatrics and director of the Center for Research Informatics at the University of Chicago, in the Harvard Business Review...

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The first step to enabling algorithm-driven organizations starts by eliminating your data policy snowflakes. What’s a data policy snowflake you ask? In short, they’re the “from scratch” implementation of data policies in every database across your organization – uniquely. Just like snowflakes. Leaving no consistent way to manage, enforce, understand, and audit policies on data costing...

Machine Learning Algorithms Meet Data Governance

Machine learning algorithms may change computing, but they’re a bit of a black box. Still, there are ways to tame them with flexible data governance, according to tech startup exec Andrew Burt. As a lawyer on the staff of the FBI Cyber Division, Andrew Burt spent a good deal of time looking at the intersection...

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