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 then normalizes this information where it 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, putting it in open standard formats—like the FHIR mandated format. 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.


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Want to watch a movie at the end of a long week, but not sure what to choose? Looking for a new show while you wait for the next season of Game of Thrones to start? So are millions of our Prime Video customers. The Prime Video Relevance team helps customers find relevant videos, channels and topics so they can find content they didn’t even known they were looking for, continuing to surprise them with the depth of our catalog.We tailor our recommendations through a variety of machine learning algorithms including deep learning neural networks, that you will help define and extend. We are looking for creative, customer and details obsessed machine learning scientists who can apply the latest research, state of the art algorithms and machine learning to build highly scalable recommendation and personalization systems. You'll have a chance to collaborate with talented teams of engineers and scientists to run these predictions on distributed systems at incredible scale and speed.As a member of the Prime Video Personalization organization, you will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At then of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide.Some examples of the things we work on:· Using Neural Networks and Deep Learning techniques to find titles that customers will enjoy· Build and operate services that deliver millions of recommendations per second· Extend models and algorithms to support our ever growing ways of consuming content (subscriptions, live, rentals etc), dealing with unique challenges such as observational bias and rapidly scaling dimensions· Constantly experimenting with changes to the underlying algorithms and models to deliver relevant content to a wide variety of customer experiencesIf you are ready to truly make an impact on a product that is used by millions of people around the world, including your own friends and family, then we would love to talk to you.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists and Engineers developing models to predict customer behavior and optimize the customer experience with Amazon Prime. This includes identifying who our customers are and providing them with personalized relevant content. As an ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, cohort identification in a semi-supervised setting, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from supervised learning, bandits, optimization, and RL.As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (XGBoost, Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.Major responsibilities· · Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.· · Leverage Bandits and Reinforcement Learning for Recommendation Systems.· · Develop offline policy estimation tools and integrate with reporting systems.· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.· · Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.· · Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.· · Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, WA, Seattle
Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists and Engineers developing models to predict customer behavior and optimize the customer experience with Amazon Prime. This includes identifying who our customers are and providing them with personalized relevant content. As an ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions.There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, cohort identification in a semi-supervised setting, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from supervised learning, bandits, optimization, and RL.As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of.You will also utilize and be exposed to the latest in ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various ML algorithms and techniques (XGBoost, Random Forests, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques.Major responsibilities· · Build and develop machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams.· · Leverage Bandits and Reinforcement Learning for Recommendation Systems.· · Develop offline policy estimation tools and integrate with reporting systems.· · Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.· · Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes.· · Work closely with the business to understand their problem space, identify the opportunities and formulate the problems.· · Use machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems.· · Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.
US, WA, Seattle
Are you excited about powering Amazon’s physical stores’ expansion through the application of Machine Learning and Big Data technologies? Do you thrive in a fast-moving, innovative environment that values data-driven decision making, scalable solutions, and sound scientific practices? We are looking for experienced scientists to build the next level of intelligence that will help Amazon physical stores grow and succeed.Our team is responsible for building the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. We are tackling cutting-edge, complex problems — such as predicting the optimal location for new Amazon stores — by bringing together numerous data assets from disparate sources inside and outside of Amazon, and using best-in-class modeling solutions to extract the most information out of them.You will have a proven track-record of delivering solutions using advanced science approaches. You will be comfortable using a variety of tools and data sources to answer high-impact business questions. You will transform one-off models into automated systems. You will be able to break down complex information and insights into clear and concise language and be comfortable presenting your findings to audiences with a broad range of backgrounds.Responsibilities:· Develop production software systems utilizing advanced algorithms to solve business problems.· Analyze and validate data to ensure high data quality and reliable insights.· Partner with data engineering teams across multiple business lines to improve data assets, quality, metrics and insights.· Proactively identify interesting areas for deep dive investigations and future product development.· Design and execute experiments, and analyze experimental results in collaboration with Product Managers, Business Analysts, Economists, and other specialists.· Leverage industry best practices to establish repeatable applied science practices, principles & processes.
US, WA, Seattle
Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. You will support interesting, analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant
US, VA, Arlington
Global Talent Management (GTM) at Amazon owns a suite of products which helps drive career development for hundreds of thousands of Amazonians across the world. GTM - Science utilizes a wide array of data sources to conduct analytics and create predictive models that fuel recommendations, actions, and insights in nearly a dozen software systems. The team itself is composed of a variety of scientists and engineers with varied backgrounds, coming together to create diverse and innovative solutions to the problems faced by the one of the world’s largest and fastest growing workforces.This role will support the advancement of key workforce planning products owned by the team. The role will be a scientific lead for forecasting in the organization and a thought leader for forecasting applications throughout HR. If you’re interested in building models used regularly by thousands of Amazonians, to inform talent management decisions, this role is for you. These are exciting fast-paced businesses in which work on extremely interesting analytical problems, in an environment where you get to learn from other experienced economists and apply econometrics at massive scale.You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company.· Build and operationalize econometric and statistical models· Perform model refreshes or updates to analyses as needed· Work collaboratively with economists and research scientists to assist in the design and implementation of analysis to answer challenging HR questions· Interpret and communicate results to outside customers· Aggregate and analyze data pulled from disparate sources (HR, Finance or other business systems) and related industry and external benchmarks; provide insights and a point of view on analysis and recommendations· Assist in the design and delivery of automated, scalable analytical models to stakeholders· Report results in a manner which is both statistically rigorous and compellingly relevant