Diagram that describes the features of Amazon HealthLake

AWS director of machine learning explains the significance of new Amazon HealthLake service

Taha Kass-Hout says the service’s secret sauce is its ability to create a comprehensive data set within a secure data lake that can be organized by different attributes, and then queried and analyzed with advanced analytics and machine learning.

During yesterday's re:Invent 2020 Machine Learning keynote, Matt Wood, AWS vice president of AI, announced Amazon HealthLake, a HIPAA-eligible service that enables healthcare providers, health insurance companies, and pharmaceutical companies to store, transform, query, and analyze health data in the cloud at petabyte scale.

Taha Kass-Hout, AWS director of machine learning
Taha Kass-Hout, AWS director of machine learning.

The new service provides these customers with the ability to use machine learning to spot trends and anomalies in health data so they can provide more precise care for individual patients and across entire populations.

One of the scientists behind the new service is Taha Kass-Hout, MD, MS, AWS director of machine learning. A physician and bioinformatician, Kass-Hout has developed a successful track record in the past two decades innovating on behalf of customers, and pioneering in healthcare and life sciences, precision medicine, and artificial intelligence. Prior to joining Amazon in 2017, he was the first chief health informatics officer for the US Food and Drug Administration (FDA), where he created openFDA and precisionFDA, part of President Obama’s 2015 Precision Medicine Initiative.  Previously, he served as director of health informatics solutions and operations for the US Centers for Disease Control and Prevention (CDC).

Prior to the announcement, Kass-Hout took some time to answer questions about the new service for Amazon Science.

Q. There’s a desire for our health care system to shift from reactive to proactive, to provide more preventative care. What role can Amazon HealthLake play in accelerating that trend?

First, Amazon HealthLake is a HIPAA-eligible service that allows our healthcare and life sciences customers to bring together their disparate health information previously stored in many different formats, and within various data silos, into a secure data lake they own and control. Emerging open standards, such as the Fast Healthcare Interoperability Resources (FHIR), aim to address this challenge by providing a consistent format to describe and exchange structured data across these systems.

However, much of this data is unstructured information, like clinical notes, PDF laboratory reports, insurance claims, X-ray and MRI images, recorded conversations, heart ECG or brain EEG traces, and more, which means the data needs to be extracted and transformed before it can be searched and analyzed. Amazon HealthLake ingests data in FHIR V4 format and then normalizes this information and tags the dates and any key descriptions of events, such as medications, procedures, diagnoses, across every encounter a patient might have throughout their health history. It then indexes all the information so it can be searched later. Now, you have a complete view of an individual patient’s history that is to a level of granularity where now you can apply advanced analytics or predict a bunch of interesting things with new machine learning models to all that data, not just a subset of it.

For example, today the most widely used clinical models to predict someone’s risk of disease oftentimes might have as few as 20 or 30 data points, like someone’s risk of a heart attack or failure. However, if you look at an individual's medical record, there may be at least 250,000 to 300,000 data points, including their medical notes. None of this is used today to manage patients or predict their outcomes. So, we believe the ability to read someone’s entire medical history will lead to better clinical decisions where health care providers can now discover trends and insights on their entire populations from this previously untapped information.

Q. What is the secret sauce of Amazon HealthLake?

At a high level, it’s the ability to create a comprehensive data set in a secure data lake that can be organized by different attributes, and then queried and analyzed with advanced analytics and machine learning. This ability to search and apply advanced analytics, or predict potential disease outcomes with machine learning models, including healthcare utilization metrics, or cost, is very powerful.

The benefit is that now you can make predictions much earlier than you could previously, or intervene quickly to improve care and reduce cost.  The other benefit is now you have access to all this information through a standards-based API, allowing you — with the patient’s consent — to share that data between health systems and with popular third-party applications, analytic platforms, etc. Providers can collaborate more effectively and patients can have unfettered access to their medical information. Using Amazon HealthLake, you now have a patient’s entire medical information structured and organized with a timeline, allowing you to run numerous models to assess risk of chronic disease, manage total medical expense, or predict a patient being readmitted to a hospital after being discharged—at an individual level as well as the population level.

Q. If you were still a practicing physician today, what would most excite you about this solution?

What excites me most is that at the point of care physicians can now look at the individual in front of them and determine what's relevant at that time for each individual patient. They can also zoom out to look at the entire population, compare and manage the broader population with data-driven decisions. This will enable a higher quality of patient care, as physicians can use data to figure out what is working and what is not.

Imagine you have a diabetic patient whose condition you’re managing, and two months later their A1C or glucose level is still not responding to the treatment that you have prescribed. Imagine that you can have comparative analysis on that patient and figure out what other individuals might be similarly unique, and see what worked, or didn’t work for them. Now that you have this comprehensive information available to you about the patient, as well as the entire population, you can make point-of-care decisions that are driven by evidence from the overall data. That’s something really profound. It’s something that’s desperately needed to close gaps in care and ensure you’re providing the highest-quality care every patient deserves, and find out what is working and what isn’t for the larger population.

Q. If Amazon HealthLake had been available a decade ago when you were at the CDC and FDA, how might that have changed your approach to those roles?

No doubt we would have been able to find aberrations from the norm in the larger population much earlier. We could have done far more predictive analytics and figured out sooner whether interventions were working or not, for example, during the H1N1 pandemic I worked on. Having that ability to look across all information and then glean insights from the data, whether it was about an emerging outbreak, or evaluating certain conditions propagating within a community, and then identifying gaps in care, or what might have contributed to disparities in disease susceptibility, would have been immensely helpful.

At the FDA, the amount of information you're trying to manage is enormous. For example, take post-marketing surveillance. This is when a new drug is being approved and you're trying to track across the population to determine if there are any adverse reactions, or trying to understand why a certain part of the population is responding positively, while another isn’t. Oftentimes in these situations we struggled dealing with a lot of unstructured data that comes through in all forms, whether it’s a patient reporting information, or a physician, a pharmacist, or data that a pharma company is mandated to submit.

One of the greatest things about Amazon Web Services is not only are we removing the heavy lifting for all these components, but demystifying machine learning and artificial intelligence.
Taha Kass-Hout

Data is often unstructured like a handwritten note, containing typos, abbreviations, and spelling errors. There are a lot of lost signals in that large volume of text that a solution like Amazon HealthLake absolutely would help identify. That’s because Amazon HealthLake takes the meaning and context into account to extract and establish relationships between entities, such as a medication and its dosage for a medical condition and the associated adverse reaction. It would provide that opportunity to find a needle in a haystack, and provide earlier detection of any adverse events from the wide variety of unstructured medical data that's been collected.

If all of those tools were available 10 years ago, I could have imagined getting ahead of outbreaks or disease propagation in any community, and understanding the complexities associated with each occurrence. We then could have applied a combination of modeling and pattern recognition so we could deliver better outcomes for the public.

Q. Is the development of a service like Amazon HealthLake one of the reasons you decided to join Amazon four years ago?

Absolutely. I have been on a mission focused on making more informed health decisions, whether that’s at the point of care, or as a public health official trying to determine the right public health intervention at the population level. It is humbling to be part of the team building tools and machinery to help healthcare providers, public health officials and others carry out their missions securely, and at scale with the most advanced and accurate scientific tools. The democratization of these technologies so a clinician like myself can use these tools regardless of technical depth is of immense value.

One of the greatest things about Amazon Web Services is not only are we removing the heavy lifting for all these components, but demystifying machine learning and artificial intelligence. We are simplifying access to these tools so they can be plugged in and tailored to individual needs, whether you are at the bottom of the stack — someone with deep expertise —or a novice practitioner. The power of Amazon HealthLake is that you can bring all your data together in a secure environment that only you can access, and then derive trends, insights, and findings from all your data to make clinical decisions, recommendations, and perhaps new policies. That is the promise of a learning health system.

Related content

IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.