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For the data to be actionable, health systems must aggregate, structure, and normalize the information. Then, they must tag and index the data before finally putting it in chronological order.
The new AWS health IT tool aims to automate this time-consuming and error-prone process with machine learning.
“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.
“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,” Sivasubramanian continued.
The health IT tool automates the extraction of meaningful health information from unstructured data, then indexes and stores the information in chronological order.
The service leverages the Fast Healthcare Interoperability Resources (FHIR) industry standard format to enable interoperability across healthcare systems, pharmaceutical companies, clinical researchers, health insurers, patients, and more.
Amazon HealthLake moves FHIR-formatted health data from on-premises systems to a secure data lake in the cloud. Machine learning models that understand medical terminology categorize and tag each piece of clinical data so that the information can be easily searched and analyzed.
The health IT solution also indexes events such as patient visits into a timeline, providing clinicians with a complete chronological view of each patient’s individual medical history.
With structured data, HealthLake customers can apply advanced machine learning and data analytics to reveal individual and population health-level trends.
“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,” Sivasubramanian said.
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