What You Should Know:
These new capabilities in Amazon HealthLake will enable next-generation imaging workflows in the cloud and derive insights from multi-modal health data, while complying with HIPAA, GDPR, and other regulations.
Amazon HealthLake Imaging
Amazon HealthLake Imaging is a new HIPAA-eligible capability that makes it easy to store, access, and analyze medical images at a petabyte scale. This new capability is designed for fast, sub-second medical image retrieval in clinical workflows that can be accessed securely from anywhere (e.g., web, desktop, phone) and with high availability. Additionally, it can drive existing medical viewers and analysis applications from a single encrypted copy of the same data in the cloud with normalized metadata and advanced compression. As a result, it is estimated that HealthLake Imaging helps reduce the total cost of medical imaging storage by up to 40%.
Amazon HealthLake Analytics
Amazon HealthLake Analytics harnesses multi-modal data, which is highly contextual and complex, is key to making meaningful progress in providing patients with highly personalized and precisely targeted diagnostics and treatments. HealthLake Analytics makes it easy to query and derive insights from multi-modal health data at scale, at the individual or population levels, with the ability to share data securely across the enterprise and enable advanced analytics and ML in just a few clicks. This removes the need for you to execute complex data exports and data transformations.
HealthLake Analytics automatically normalizes raw health data from multiple disparate sources (e.g. medical records, health insurance claims, EHRs, medical devices) into an analytics and interoperability-ready format in a matter of minutes. Integration with other AWS services makes it easy to query the data with SQL using Amazon Athena, as well as share and analyze data to enable advanced analytics and ML. You can create powerful dashboards with Amazon QuickSight for care gap analyses and disease management of an entire patient population. Users can build and train many ML models quickly and efficiently in Amazon SageMaker for AI-driven predictions, such as risk of hospital readmission or overall effectiveness of a line of treatment.