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Thought pieces about the use of blockchain and machine learning in healthcare and life sciences segments
The volume of healthcare data continues to increase exponentially—burdening healthcare organizations with the management of disparate data, interoperability challenges between source systems and the complexities of unstructured data.
Machine learning can be used to build models for patient risk scores, cost prediction and patient behavior, and tap into unstructured data, incorporate SDOH and IoT edge data, as well as reconcile with structured data.
Blockchain generates auditability and traceability between stakeholders. With a single source of truth that reduces errors and the need for reconciliation, it addresses patient privacy concerns and population health data requirements.
