Posted On: Nov 15, 2022
Amazon HealthLake announces new analytics capabilities, making it easier for customers to query, visualize, and build machine learning models on their HealthLake data. With this launch, HealthLake transforms customer data into an analytics-ready format in AWS Lake Formation in near real-time. This removes the need for customers to execute complex data exports and data transformations. Now customers can simply focus on querying the data with SQL using Amazon Athena, building visualizations using Amazon QuickSight or other third party tools, and using this data to build ML models with Amazon SageMaker.
Healthcare analytics use cases such as population health analysis and claims analytics require customers to use data from multiple, disparate sources such as EHR (Electronic Health Records), claims, and devices. This entails building complex data pipelines and executing extraction and transformations that often takes months of undifferentiated heavy lifting. HealthLake reduces the time from months to days by normalizing this data from multiple disparate sources into an interoperable format and further activating this data for analytics within AWS Lake Formation. Customers can then apply granular controls, share this data within the organization, and rapidly build applications such as patient longitudinal medical record. With a few clicks, customers can use a host of AWS services such as Amazon Athena, Amazon Quicksight, and Amazon SageMaker to build population health dashboards, execute claims analytics, and build care gap prediction models.