Besides the traditional enterprise data warehouse, some health systems are starting to create data “lakes,” which contain new data types not found in traditional hospital clinical and financial data sets. The large cloud providers are moving to support this trend. Amazon Web Services Inc. just announced the general availability of Amazon HealthLake, which it says will allow organizations to ingest, store, query, and analyze their health data at scale.
Amazon says HealthLake uses machine learning to understand and extract meaningful medical information from unstructured data, and then organizes, indexes, and stores that information in chronological order to provide a holistic view of the patient.
Using Amazon HealthLake, organizations can move their FHIR-formatted health data from on-premises systems to a secure data lake in the cloud. Amazon says HealthLake uses machine learning to automate the extraction and transformation of unstructured health data so organizations can apply advanced analytics and customized machine learning models to their information.
The company said that customers who do not already have data in the FHIR format can work with AWS Connector Partners, such as Diameter Health, InterSystems, Redox, and HealthLX, which have built validated Amazon HealthLake connectors to transform existing healthcare data into FHIR format and move it to Amazon HealthLake.
One early customer is Chicago-based Rush University Medical Center, an academic medical center that includes a 671-bed hospital serving adults and children, the 61-bed Johnston R. Bowman Health Center, and Rush University. “Even while still in preview, Amazon HealthLake was an integral part of our COVID-19 response and our efforts to address health inequities. It has enabled us to quickly store disparate data from multiple data sources in FHIR format in order to gain critical insights in to the care of COVID-19 patients,” said Bala Hota, M.D., vice president and chief analytics officer at Rush University Medical Center, in a statement. “We have also used HealthLake’s integrated natural language processing to extract information such as medication, diagnosis, and previous conditions from doctors’ clinical notes and enrich patient records to examine barriers to healthcare access, providing our researchers additional data points for analytics. With the HealthLake API, we created a mobile app to provide insights into care gaps across the West Side of Chicago. Amazon HealthLake enables us to accelerate insights and drive decisions faster to better serve the Chicago community.”
Amazon HealthLake is part of a suite of analytics tools available to its customers. It uses machine learning models that understand medical terminology to identify and tag each piece of clinical information. The service then enriches data with standardized labels (e.g. medications, conditions, diagnoses, etc.) so the data can be easily searched and analyzed.
Amazon HealthLake also indexes events like patient visits into a timeline, giving medical professionals a holistic, chronological view of each patient’s medical history. Customers can then apply analytics and machine learning on top of this newly normalized and structured data.
“More and more of our customers in the healthcare and life sciences space are looking to organize and make sense of their reams of data, but are finding this process challenging and cumbersome,” said Swami Sivasubramanian, vice president of Amazon Machine Learning for AWS, in a statement. “We built Amazon HealthLake to remove this heavy lifting for healthcare organizations so they can transform health data in the cloud in minutes and begin analyzing that information securely at scale. Alongside AWS for Health, we’re excited about how Amazon HealthLake can help medical providers, health insurers, and pharmaceutical companies provide patients and populations with data-driven, personalized, and predictive care.”
