Maize leverages machine-learning, advanced understanding of clinical workflows, and statistical ranking to identify why an access is appropriate, while also highlighting the most suspicious accesses for Privacy Teams to proactively audit.

Machine Learning

Maize identifies treatment, payment and healthcare operations (TPO) reasons for access, by connecting the evidence found in a patient record (i.e. appointments, labs ordered, medication prescribed, clinical notes added, etc.) to the EMR access log. The Maize tool reviews 100% of the access log and automatically approves between 95-99% of the log using the TPO context in a patient’s record.

Enhanced Explanations

Maize learns relationships and presents them to Privacy Officers for approval. It understands that some users do not have direct TPO connections with every patient accessed, but that does not deem it inappropriate. The Maize tool enhances explanations by filling in missing facts and relationship.


The 1%-2% of accesses that can not be explained using direct evidence, collaborative groups or enhanced explanations are then ranked using a number of different metrics to prioritize the most suspicious accesses for review. These ranking features are tailored to each customer using the health system’s policy.

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See how Maize Analytics technology works.