This is a guest post by Ernesto DiMarino, who is Head of Enterprise Applications and Data at Cortica. Cortica is on a mission to revolutionize healthcare for children with autism and other neurodevelopmental differences. Cortica was founded to fix the fragmented journey families typically navigate while seeking diagnoses and therapies for their children. To bring their vision to life, Cortica seamlessly blends neurology, research-based therapies, and technology into comprehensive care programs for the children they serve. This coordinated approach leads to best-in-class member satisfaction and empowers families to achieve long-lasting, transformative results.

At AWS re:Invent 2020, we previewed Amazon HealthLake, a fully managed, HIPAA-eligible service that allows healthcare and life sciences customers to aggregate their health information from different silos and formats into a structured, centralized AWS data lake, and extract insights from that data with analytics and machine learning (ML). Today, I'm very happy to announce that Amazon HealthLake is generally available to all AWS customers. The ability to store, transform, and analyze health data quickly and at any scale is critical in driving high-quality health decisions. In their daily practice, doctors need a complete chronological view of patient history to identify the best course of action. During an emergency, giving medical teams the right information at the right time can dramatically improve patient outcomes.

AWS just announced the General Availability of Amazon HealthLake, a HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud at petabyte scale. We believe that the combination of the innovation trends in healthcare (such as reimbursement models around data-driven evidence), standardization around interoperability (such as federal and global incentives and mandates in adopting the Fast Healthcare Interoperability Resources standard, or FHIR), and the advancement of scientific methods (such as with deep learning) enable our healthcare and life sciences (HCLS) customers to improve clinical and research efforts. Over the past decade, we've witnessed a digital transformation with healthcare organizations capturing huge volumes of patient information in electronic medical records (EMRs) every day, making the medical record a source of big data containing information regarding sociodemographics, medical conditions, genetics, and treatments. Making sense of all this data provides the biggest opportunity to transform care by tailoring disease treatment and prevention to individuals and populations. This so-called precision medicine takes into account the individual variability in genes, environment, and lifestyle for each individual.

Amazon HealthLake is a new, HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. You can bring your healthcare data into Amazon HealthLake using Fast Healthcare Interoperability Resources (FHIR) R4 APIs. If you don't have your data in FHIR R4, Amazon has collaborated with industry experts to build Amazon HealthLake connectors to help you with custom file and HL7 to FHIR R4 mappings. This post highlights one of those partners, Redox, and their Amazon HealthLake Connector. Developers at over 300 companies use the Redox platform to exchange data with more than 1,700 healthcare provider organizations.

Medical data is highly contextual and heavily multi-modal, in which each data silo is treated separately. To bridge different data, a knowledge graph-based approach integrates data across domains and helps represent the complex representation of scientific knowledge more naturally. For example, three components of major electronic health records (EHR) are diagnosis codes, primary notes, and specific medications. Because these are represented in different data silos, secondary use of these documents for accurately identifying patients with a specific observable trait is a crucial challenge. By connecting those different sources, subject matter experts have a richer pool of data to understand how different concepts such as diseases and symptoms interact with one another and help conduct their research.

As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs. With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools. Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity.

Calling machine learning "one of the most disruptive technologies we will ever encounter in our generation," Amazon Machine Learning Vice President Swami Sivasubramanian introduced a bevy of new tools Tuesday at AWS re:Invent. There will be nine new Amazon SageMaker capabilities, a HIPAA-eligible service for healthcare and life science organizations called Amazon HealthLake, and general availability for Amazon Neptune ML, he said. Sivasubramanian also announced previews for Amazon Redshift ML and Amazon Lookout for Metrics. "More than 100,000 customers use AWS for machine learning today. These tools are no longer a niche investment. Our customers are applying machine learning to the core of their business. Our customers are innovating in every industry," Sivasubramanian said.

Amazon Web Services (AWS) launched Amazon HealthLake--a new HIPAA-eligible platform that lets healthcare organizations seamlessly store, transform, and analyze data in the cloud. The platform standardizes unstructured clinical data (like clinical notes or imaging info) by in a way that makes it easily accessible and unlocks meaningful insights--an otherwise complex and error-prone process. For example, Amazon HealthLake can match patients to clinical trials, analyze population health trends, improve clinical decision-making, and optimize hospital operations. Amazon already has links in different parts of the healthcare ecosystem--now that it's taking on healthcare AI, smaller players like Nuance and Notable Health should be worried. Amazon has inroads in everything from pharmacy to care delivery: Amazon Pharmacy was built upon its partnerships with payers like Blue Cross Blue Shield and Horizon Healthcare Services, Amazon Care was expanded to all Amazon employees in Washington state this September, and it launched its Amazon Halo wearable in August.