SEATTLE--(BUSINESS WIRE)--Jul. 15, 2021-- Today, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced the general availability of Amazon HealthLake, a HIPAA-eligible service for healthcare and life sciences organizations to ingest, store, query, and analyze their health data at scale. Amazon 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. The result provides a holistic view of patient health. The service leverages the Fast Healthcare Interoperability Resources (FHIR) industry standard format to further enable interoperability by facilitating the exchange of information across healthcare systems, pharmaceutical companies, clinical researchers, health insurers, patients, and more. Amazon HealthLake is a new service that is part of AWS for Health, a comprehensive offering of AWS services and AWS Partner Network solutions used by thousands of healthcare and life sciences customers globally. AWS for Health provides proven and easily accessible capabilities that help organizations increase the pace of innovation, unlock the potential of health data, and develop more personalized approaches to therapeutic development and care. As part of AWS for Health, Amazon HealthLake further facilitates customers’ application of analytics and machine learning on top of their newly normalized and structured data. Doing so enables customers to examine trends like disease progression at the individual or population health level over time, spot opportunities for early intervention, and deliver personalized medicine.
The healthcare industry is being transformed through the cloud and the utilization of data, helping organizations uncover new insights and deliver improved patient care. Healthcare organizations are creating huge volumes of patient information every day, and the majority of this data is unstructured and contained in clinical notes, laboratory reports, insurance claims, medical images, recorded conversations, and graphs that are in different formats and spread across disparate systems. Before customers can derive a single insight (e.g. flag high-risk diabetic patients predicted to develop further complications), they have to aggregate, structure, and normalize this data. Then it must be tagged, indexed, and put in chronological order. This is a time-consuming and error-prone process. Some healthcare organizations use optical character recognition and build rule-based tools to automate the process of transforming unstructured data and extracting clinical information (e.g. diagnoses, medications, and procedures). However, these options are often inaccurate and can’t account for variations in spelling, typos, or grammatical errors. Even after organizations are able to aggregate and structure their data, they still need to build their own analytics and machine learning applications to reveal relationships in the data, discover trends, and make precise predictions. The cost and operational complexity of this work is prohibitive to most organizations. As a result, the vast majority of organizations cannot realize the full potential of their data to help improve the health of patients and communities.
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