AWS Public Sector Blog

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Extracting, analyzing, and interpreting information from Medicaid forms with AWS

Extracting, analyzing, and interpreting information from Medicaid forms with AWS

What if paper forms could be processed at the same speed as digital forms? What if their contents could be automatically entered in the same database as the digital forms? Medicaid agencies could analyze data in near real time and drive actionable insights on a single dashboard. By using artificial intelligence (AI) and machine learning (ML) services from AWS, Medicaid agencies can create this streamlined solution. In this walkthrough, learn how to extract, analyze, and interpret relevant information from paper-based Medicaid claims forms.

Building a resilient and scalable clinical genomics analysis pipeline with AWS

At the Baylor College of Medicine Human Genome Sequencing Center (BCM HGSC), we aim to advance precision medicine and research in genomics. In that effort, we joined the ambitious All of Us Research Program funded by the National Institutes of Health (NIH) to help deliver genomic data to over one million individuals across the United States. In early 2019, we estimated that processing whole genome samples for this megaproject would imply a scale-up of over four times the production workload of our center. We used AWS to support our new pipeline demands, which saved time, reduced costs, and created new opportunities for future development.

Building a serverless web application architecture for the AWS Secure Environment Accelerator (ASEA)

Government departments work hard to meet required security framework controls for cloud services, and obtaining an Authority to Operate (ATO) can sometimes take up to 18 months. To assist with this process, AWS developed the open-source AWS Secure Environment Accelerator (ASEA), a tool designed to help deploy and operate secure multi-account AWS environments. This post describes how government departments can more simply deploy a web application consisting of a single-page application (SPA), backend API, and database within ASEA.

pFaces targets heterogenous hardware configurations (HWCs) combining compute nodes (CNs) of CPUs, GPUs and hardware accelerators (HWAs). A web-based interface helps developers design parallel algorithms and run them on targeted HWCs.

TUM researcher finds new approach to safety-critical systems using parallelized algorithms on AWS

Mahmoud Khaled, a PhD student at TUM and a research assistant at LMU, researches how to improve safety-critical systems that require large amounts of compute power. Using AWS, Khaled’s research project, pFaces, accelerates parallelized algorithms and controls computational complexity to speed the time to science. His project findings introduce a new way to design and deploy verified control software for safety-critical systems, such as autonomous vehicles.

Using a serverless architecture to collect and prioritize citizen feedback

Just as companies listen to their customers to align the business with their client needs, government organizations listen to their citizens to improve the citizen experience. In order to get feedback, many organizations use different tools with a multi-channel approach, such as customer comments, Helpdesk calls, emails, social media, or mobile apps. Using Amazon Simple […]