AWS Public Sector Blog

Tag: Amazon S3

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Using AWS for EHDS: A technical guide to building a secure health data platform

In an earlier post, Build secure and scalable data platforms for the European Health Data Space (EHDS) with AWS, we discussed a reference architecture for building secure and scalable data platforms for secondary usage of health data in alignment with the European Health Data Space (EHDS) using Amazon Web Services (AWS). This follow-up post walks you through the technical implementation details for building such federated data governance and analysis platforms using AWS. Whether you are a healthcare organization, technology provider, or systems integrator, this post aims to equip you with the technical knowledge to build a secure data platform for secondary usage of health data in alignment with the EHDS Regulation.

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Transforming government application systems using intelligent document processing on AWS

Government agencies handle vast volumes of bureaucratic documents daily, ranging from tax forms to medical records. This document-heavy workflow, often reliant on manual processing, can result in delays, errors, and increased operational inefficiencies, causing frustration among both employees and stakeholders. This post explores how intelligent document processing (IDP) solutions from Amazon Web Services (AWS) can modernize bureaucratic workflows, improve efficiency, and enhance service delivery within government agencies.

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Rapidly develop fully functional web applications with AWS App Studio

AWS App Studio is a low-code development platform for users to build, deploy, and manage applications without extensive coding knowledge. It is designed to streamline the application development process, making it more accessible to a wider range of users, including business users, IT professionals, and startups that don’t have large specialized IT teams. This post guides you through the process of creating a user-friendly create, read, update, and delete (CRUD) application to efficiently manage birth certificate applications using AWS App Studio.

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Strengthen foundation model queries through Amazon Bedrock-Amazon Alexa integration

Today, generative artificial intelligence (AI) is at the core of many of the decisions and technologies businesses across the globe are implementing. The linchpin to ensuring that generative AI is effective depends on the foundation models (FMs) storing the data. To make sure that those FMs are pulling the correct data, Amazon Web Services (AWS) has developed a solution through Amazon Bedrock to generate SQL to answer user questions about the data. The solution was developed to support the Federal Institute of São Paulo (IFSP) and has optimized their decision-making.

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Harnessing the power of generative AI in AWS GovCloud

In this post, we explore how generative artificial intelligence (AI), powered by services such as Amazon Bedrock and Amazon SageMaker, can be harnessed to meet the unique challenges of AWS GovCloud (US). We highlight use cases that demonstrate the potential of generative AI to enhance efficiency, automate workflows, and extract insights—all within a secure, compliant framework.

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Enhancing resource sharing with AWS Outposts

In this post, we explore how AWS services, open source software, and bespoke source code can help address resource sharing enhancements requested by public sector customers using AWS Outposts. This solution aims to provide greater flexibility, control, and cost management for organizations leveraging Outposts in multi-account environments, specifically focusing on key AWS resources, including Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Block Store (Amazon EBS), and Amazon Simple Storage Service (Amazon S3) on Outposts.

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Transforming financial markets: How FIA Tech built the Trade Data Network on AWS

This post discusses how FIA Tech, a leading technology provider for the exchange traded derivatives (ETD) industry, built the Trade Data Network (TDN) on Amazon Web Services (AWS). TDN is an industry initiative that provides a shared ledger of trading information to address the fragmentation and lack of transparency in ETD post-trade processing. The TDN initiative currently includes 17 banks/brokers and 40 investment managers and hedge funds with combined assets under management of more than $34 trillion.

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Extracting insights from PubMed articles using Amazon Q Business

PubMed Central (PMC) is a leading resource for biomedical literature, offering a vast repository of full-text biomedical and life sciences journal articles. While PMC offers immense potential to accelerate scientific progress, inform evidence-based practice, and drive innovation in the biomedical and life sciences fields, the sheer volume of data presents a significant challenge. This is where Amazon Q Business comes in. It’s a powerful service designed to streamline the process of analyzing vast amounts of scientific literature and provide valuable insights efficiently. Read this post to learn more.

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Unlocking healthcare data with Amazon Q Business

Healthcare organizations face a significant challenge in making sense of the vast amounts of unstructured medical data trapped in patient records, lab results, and clinical guidelines. Approximately 97 percent of the 50 petabytes (PB) of healthcare data generated annually goes unused. This untapped resource could hold the key to improving patient care and saving valuable clinician time. Read this post learn how we built an innovative solution on Amazon Web Services (AWS) generative artificial intelligence (AI) capabilities that lets clinicians unlock this data treasure trove.

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Operationalizing SAS on AWS with the American College of Radiology

The American College of Radiology (ACR) is a nonprofit organization and world leader supporting the radiology community. Founded in 1923, ACR represents more than 41,000 diagnostic and interventional radiologists, radiation oncologists, nuclear medicine physicians, and medical physicists. In this post, we share how ACR implemented a Statistical Analysis System (SAS) solution on Amazon Web Services (AWS) to accomplish their big data processing and analysis needs, resulting in better cost optimization, performance improvements, and improved scalability.