At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN) announced Amazon HealthLake, a HIPAA-eligible service for healthcare and life sciences organizations. Amazon HealthLake aggregates an organization’s complete data across various silos and disparate formats into a centralized AWS data lake and automatically normalizes this information using machine learning. The service identifies each piece of clinical information, tags, and indexes events in a timeline view with standardized labels so it can be easily searched, and structures all of the data into the Fast Healthcare Interoperability Resources (FHIR) industry standard format for a complete view of the health of individual patients and entire populations. As a result, Amazon HealthLake makes it easier for customers to query, perform analytics, and run machine learning to derive meaningful value from the newly normalized data. Organizations such as healthcare systems, pharmaceutical companies, clinical researchers, health insurers, and more can use Amazon HealthLake to help spot trends and anomalies in health data so they can make much more precise predictions about the progression of disease, the efficacy of clinical trials, the accuracy of insurance premiums, and many other applications. To learn more about Amazon HealthLake, visit here.
As machine learning becomes more mainstream, companies across every vertical business are trying to apply it to their data to deliver meaningful business value. Healthcare is applying machine learning to improve operations and patient care, with AWS customers like 3M, Anthem, AstraZeneca, Bristol Myers Squibb, Cerner, the Fred Hutchinson Cancer Research Center, GE Healthcare, Infor, Pfizer, and Philips embracing the cloud and machine learning to get more value out of their vast data troves. From family history and clinical observations to diagnoses and medications, healthcare organizations are creating huge volumes of patient information every day with the goal of getting a full view of a patient’s health and applying analytics and machine learning to improve care, analyze population health trends, and improve operational efficiency. However, clinical data is complex and renowned for being siloed, incomplete, incompatible, and stored in on-premises systems spread across multiple locations. Getting all this information aggregated and in the FHIR format is a start toward the goal of standardizing structured data, but the majority of data remains unstructured and still needs to be tagged, indexed, and structured in chronological order to make all of the data understandable and able to query. Some healthcare organizations build rule-based tools to automate the process of transforming unstructured data (e.g., medical histories, physician notes, and medical imaging reports) and tagging clinical information (e.g., diagnoses, medications, and procedures), but these solutions often fail because the data needs to be normalized across disparate systems and because the tools can’t account for every possible variation in spelling, unintended typos, and grammatical errors. Other organizations use general-purpose optical character recognition (OCR) software to process data sources, but these tools lack the medical expertise to be effective and so organizations resort to manual data entry by medical professionals which adds expense to the digitization process. Even if organizations are able to aggregate and structure their data, they still need to build their own analytics and machine learning applications to uncover relationships in the data, discover trends, and make precise predictions. The cost and operational complexity of doing all this work is prohibitive to most organizations; and as a result, the vast majority of organizations end up missing out on the untapped potential to use their data to improve the health of patients and communities.
Amazon HealthLake offers medical providers, health insurers, and pharmaceutical companies a service that brings together and makes sense of all their patient data, so healthcare organizations can make more precise predictions about the health of patients and populations. The new HIPAA-eligible service enables organizations to store, tag, index, standardize, query, and apply machine learning to analyze data at petabyte scale in the cloud. Amazon HealthLake allows organizations to easily copy health data from on-premises systems to a secure data lake in the cloud and normalize every patient record across disparate formats automatically. Upon ingestion, Amazon HealthLake uses machine learning trained to understand medical terminology to identify and tag each piece of clinical information, index events into a timeline view, and enrich the data with standardized labels (e.g., medications, conditions, diagnoses, procedures, etc.) so all this information can be easily searched. For example, organizations can quickly and accurately find answers to their questions like, “How has the use of cholesterol-lowering medications helped our patients with high blood pressure last year?” To do this, customers can create a list of patients by selecting “High Cholesterol” from a standard list of medical conditions, “Oral Drugs” from a menu of treatments, and blood pressure values from the “Blood Pressure” structured field – and then they can further refine the list by choosing attributes like time frame, gender, and age. Because Amazon HealthLake also automatically structures all of a healthcare organization’s data into the FHIR industry format, the information can be easily and securely shared between health systems and with third-party applications, enabling providers to collaborate more effectively and allowing patients unfettered access to their medical information.
“There has been an explosion of digitized health data in recent years with the advent of electronic medical records, but organizations are telling us that unlocking the value from this information using technology like machine learning is still challenging and riddled with barriers,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “With Amazon HealthLake, healthcare organizations can reduce the time it takes to transform health data in the cloud from weeks to minutes so that it can be analyzed securely, even at petabyte scale. This completely reinvents what’s possible with healthcare and brings us that much closer to everyone’s goal of providing patients with more personalized and predictive treatment for individuals and across entire populations.”
By aggregating, labeling, indexing, and structuring all their data, Amazon HealthLake makes it easy for customers to query, analyze, and use machine learning to make sense of their data. Customers can use other AWS analytics and machine learning services with Amazon HealthLake like Amazon QuickSight for interactive dashboards and Amazon SageMaker for easily building, training, and deploying custom machine learning models. For example, healthcare organizations can use Jupyter Notebook templates in Amazon SageMaker to quickly and easily run analysis for common tasks like diagnosis predictions, hospital re-admittance probability, and operating room utilization forecasts. Healthcare and life science organizations can use Amazon HealthLake to get a complete view of patient and population health, derive insights using analytics and machine learning, and discover previously obscured relationships and trends.
Cerner Corporation, a global healthcare technology company, is focused on using data to help solve issues at the speed of innovation – evolving healthcare to enhance clinical and operational outcomes, help resolve clinician burnout, and improve health equity. “At Cerner we are committed to transforming the future of healthcare through cloud delivery, machine learning, and AI. Working alongside AWS, we are in a position to accelerate innovation in healthcare. That starts with data. We are excited about the launch of Amazon HealthLake and its potential to quickly ingest patient data from various diverse sources and transform the data to perform advanced analytics to unlock new insights and serve many of our initiatives across population health,” said Ryan Hamilton, SVP, Population Health, Cerner.
Ciox Health is a health technology company that is dedicated to improving U.S. health outcomes by transforming clinical data into actionable insights. “At Ciox, we work to enable greater health by improving the way health information is managed,” said Sasidhar Mukkamala, SVP of Data Management, Ciox Health. “Much of the health information that we ingest is unstructured, like notes and handwritten PDFs, and it is a challenge to find solutions that allow us to realize the full analytic value of that data. With 60 percent of the market share in risk adjustments, this is a huge opportunity. We are excited about getting started with Amazon HealthLake and its potential to help us meet this need and deliver better risk adjustments, predictions, billing, and much more, all informed by health data.”
Konica Minolta Precision Medicine (KMPM) is a life science company dedicated to the advancement of precision medicine to more accurately predict, detect, treat, and ultimately cure disease. “We are building a multi-modal platform at KMPM to handle a significant amount of health data inclusive of pathology, imaging, and genetic information. Amazon HealthLake will allow us to unlock the real power of this multi-modal approach to find novel associations and signals in our data. It will provide our expert team of data scientists and developers the ability to integrate, label, and structure this data faster, and discover insights that our clinicians and pharmaceutical partners require to truly drive precision medicine,” said Kiyotaka Fujii, President of Global Healthcare, Konica Minolta.
Orion Health is a global, award-winning provider of health information technology, advancing population health and precision medicine solutions for the delivery of care across the entire health ecosystem. “At Orion Health, we believe that there is significant untapped potential to transform the healthcare sector by improving how technology is used and providing insights into the data being generated. We are pleased to find a like-minded company in AWS who, with Amazon HealthLake, is now taking the next step in using machine learning to help make sense of health data in a secure, complaint, and auditable way,” said Anne O’Hanlon, Product Director, Orion Health. “Data is frequently messy and incomplete, which is costly and time consuming to clean up. We are excited to work alongside AWS to deliver new ways for patients to interact with the healthcare system, supporting initiatives such as the 21st Century Cures Act designed to make healthcare more accessible and affordable, and Digital Front Door, which aims to improve health outcomes by helping patients receive the perfect care for them from the comfort of their home. Expanding the relationship we enjoy with AWS gives us an opportunity to innovate and explore new ways to deliver patient-centered healthcare and high quality health outcomes that help people live a healthier life.”
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