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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.

By Staff writer
December 9, 2020
8 min read
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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.

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Principal Applied Scientist, Ads Measurement Science
US, NY, New York
The Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) organization of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of their ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. This Principal Applied Scientist leads the technical vision and execution for Amazon Advertising's performance measurement. Our measurement products and tools help customers understand the holistic impact of their spend on Amazon Ads and optimize their portfolios. This is a complex, multi-disciplinary scientific space spanning machine learning, causal inference, statistics and experimentation, natural language processing, and generative AI. As a GenAI-first organization, we build foundational and agentic models that power advertiser use cases across Ads, while empowering our Applied Scientists to directly build and ship products. You will guide a team of scientists with expertise across different domains to come together and innovate on some of the hardest problems in advertising, made more challenging by evolving privacy regulations and technological changes. In measuring the comprehensive impact of advertising, key challenges you will tackle include capturing impact: 1) both for identified and increasingly unidentified web traffic in light of an evolving global privacy landscape; 2) for outcomes across retailers, including both within and beyond Amazon; and 3) for both short- and long-term impacts of advertising. Key job responsibilities * Driving the scientific vision of the teams in your organization and you advise and influence its technical leadership on performance measurement models and products. * Identifying, tackling, and proposing innovative solutions to intrinsically hard, previously unsolved problems. You solve problems pragmatically, applying judgment and experience to balance trade-offs between competing interests. * Bringing clarity to complex problems, probing assumptions, illuminating pitfalls, fostering shared understanding, and guiding towards effective solutions. * Serving and being recognized by internal and external peers as a thought leader in modeled measurement in Ads. You will engage routinely with the broader scientific community, e.g. as a reviewer, program committee member, workshop organizer, or chair at peer-reviewed internal/external conferences. * Leading by example, with your papers, analyses, and code setting the standard in your organization for scientific engineering excellence. * Leading science and design reviews, aligning teams across your organization around coherent algorithmic strategies. * Influencing your team’s science and business strategy by driving one or more team roadmaps contributing to the organization’s roadmap and taking responsibility for some organizational goals. You drive multiple new product features from inception to production launch. * Guiding the career development of others, actively mentoring and educating the larger applied science community on trends, technologies, and best practices. You drive the documentation of scientific innovation. You attract bar-raising talent from academia and industry. * Staying abreast of academic and industry trends, knowing the state of the art, and building on top of existing solutions instead of reinventing when possible. About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
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