Amazon Web Services has introduced Amazon HealthLake, a HIPAA-eligible service for healthcare and life sciences organisations to ingest, store, query, and analyse their health data at scale. Amazon HealthLake uses machine learning to understand and extract meaningful medical information from unstructured data, and then organises, 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 organisations increase the pace of innovation, unlock the potential of health data, and develop more personalised 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 normalised 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 personalised medicine.
The healthcare industry is being transformed through the cloud and the utilisation of data, helping organisations uncover new insights and deliver improved patient care. Healthcare organisations 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 normalise this data. Then it must be tagged, indexed, and put in chronological order. This is a time-consuming and error-prone process. Some healthcare organisations 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 organisations 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 organisations. As a result, the vast majority of organisations cannot realise 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 organisations can apply advanced analytics and customised machine learning models to their information. Using Amazon HealthLake, organisations 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 standardised labels (e.g. medications, conditions, diagnoses, etc.) so the data can be easily searched and analysed. Amazon HealthLake also indexes events like patient visits into a timeline, giving medical professionals a holistic, chronological view of each patient’s medical history. Once this heavy lifting is completed, customers can apply analytics and machine learning on top of this newly normalised 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 organisations, 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 HealthLake’s 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 organisations 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.
“More and more of our customers in the healthcare and life sciences space are looking to organise 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 organisations so they can transform health data in the cloud in minutes and begin analysing 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, personalised, and predictive care.”
Amazon HealthLake is available today in US East (N. Virginia), US East (Ohio), and US West (Oregon), with additional region availability coming soon.
Rush University Medical Center is 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. For more than 180 years, the Medical Center has been leading the way in developing innovative and often life-saving treatments. “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 Dr. Bala Hota, Vice President and Chief Analytics Officer at Rush University Medical Center. “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.”
Cortica, a preview customer of Amazon HealthLake, provides healthcare services to children with autism and other brain conditions. The unique Cortica Care Model seamlessly blends neurology, research-based therapies, and technology into truly comprehensive care programs for the children they serve. “At Cortica, we’re on a mission to revolutionise healthcare for children with autism and other developmental differences,” said Ernesto DiMarino, Head of Enterprise Applications and Data at Cortica. “In a matter of weeks rather than months, Amazon HealthLake empowered us to create a centralised platform that securely stores patients’ medical history, medication history, behavioral assessments, and lab reports. This platform gives our clinical team deeper insight into the care progression of our patients. Using predefined notebooks in Amazon SageMaker with data from Amazon HealthLake, we can apply machine learning models to track and prognosticate each patient’s progression toward their goals in ways not otherwise possible. Through this technology, we can also share HIPAA-compliant data with our patients, researchers, and healthcare partners in an interoperable manner, furthering important research into autism treatment.”
CureMatch is a digital health company focused on personalised medicine and combination therapy in oncology. CureMatch’s Decision Support System guides oncologists in the selection of cancer drugs that are customised for individual patients based on their molecular tumor profile, providing actionable intelligence to support advanced cancer treatment options. “With the use of the HealthShare Message Transformation Service by InterSystems and Amazon HealthLake, we will be able to access and transform molecular profile data from the EHR into FHIR format to run advanced analytics and algorithms, providing clinical decision support and guidance to assist oncologists with personalised cancer treatment options,” said Philippe Faurie, Vice President of Professional Services at CureMatch.
MEDHOST provides market-leading enterprise, departmental, and healthcare engagement solutions to more than 1,000 healthcare facilities. Its integrated product portfolio includes intuitive and cloud-based clinical, financial, and operational solutions, including YourCare Everywhere, a robust health and wellness consumer engagement platform. “The vast majority of MEDHOST’s more than 1,000 healthcare facility customers want to develop solutions to standardise patient data in FHIR format and build dashboards and advanced analytics to improve patient care, but that is difficult and time consuming today,” said Pandian Velayutham, Senior Director of Engineering at MEDHOST. “With Amazon HealthLake, we can meet our customers’ needs by creating a compliant FHIR data store in just days rather than weeks with integrated natural language processing and analytics to improve hospital operational efficiency and provide better patient care.”