📋 Tender Overview
Amazon Web Services Inc. AWS an Amazon.com Inc. company 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 cant 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.Amazon HealthLake removes this heavy lifting by using highly accurate 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. Using Amazon HealthLake organizations can easily move their FHIR-formatted health data from on-premises systems to a secure data lake in the cloud. Amazon HealthLake uses specially tuned 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 patients medical history. Once this heavy lifting is completed customers can apply analytics and machine learning on top of this newly normalized and structured data. For example customers can apply analytics using Amazon QuickSight to understand patient and population-level trends as well as build powerful machine learning models with Amazon SageMaker to help make accurate predictions about the progression of disease the efficacy of clinical trials the eligibility of insurance claims and more. Amazon HealthLake also stores data in the FHIR format to facilitate the exchange of information so that it is easy for organizations researchers and practitioners to collaborate and accelerate breakthroughs in treatments deliver vaccines to market faster and discover health trends in patient populations. 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 who have built validated Amazon HealthLake connectors to transform existing healthcare data into FHIR format and move it to Amazon HealthLake.Amazon HealthLakes purpose-built analytics and machine learning capabilities are also now available to customers under AWS for Health a growing portfolio of solutions that simplifies how healthcare biopharma and genomics organizations discover assess and deploy cloud solutions to achieve better business and patient outcomes. For example solutions offered in AWS for Health are helping customers create holistic Electronic Health Records to help clinicians make data-driven care plans accelerate research and discovery to bring new therapies to market faster and powering population genomic initiatives to expand precision medicine accessibility.
