AWS Public Sector Blog

Tag: Amazon QuickSight

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Track application resiliency in public sector organizations using AWS Resilience Hub

The Amazon Web Services (AWS) Resilience Hub provides you with a single place to define your resilience goals, assess your resilience posture against those goals, and implement recommendations for improvement. In this post, we discuss how we can track the resiliency of software applications and infrastructure using AWS Resilience Hub to provide “always available” services and monitor changes to the application availability.

AWS branded background design with text overlay that says "Unlocking data governance for multiple accounts with Amazon DataZone"

Unlocking data governance for multiple accounts with Amazon DataZone

This post discusses how Amazon Web Services (AWS) can help you successfully set up an Amazon DataZone domain, aggregate data from multiple sources into a single centralized environment, and perform analytics on that data. Additionally, this post provides a sample architecture as well as a walkthrough on how to set up that architecture. Ultimately, this post serves as a valuable resource if you’re seeking to optimize your data management processes and derive actionable insights to drive business growth.

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Modern data strategy for government tax and labor systems

Introduction Government authorities such as tax, unemployment insurance, and other finance agencies across the US and globally are seeking ways to innovate. They are trying to unlock insights from their data, deliver better customer experiences, and improve operations using cutting-edge technologies such as generative artificial intelligence (AI), machine learning (ML), and other data analytics tools. […]

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Build population health systems to enhance healthcare customer experiences on AWS

As the amount of health data increases, different healthcare, life sciences, population health, and public health organizations are working to modernize their data infrastructure, unify their data, and innovate faster with technologies like artificial intelligence and machine learning (AI/ML). In this blog post, we dive deep on architecture guidance that enables healthcare providers to improve patient care.

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.

Automating returned mail to members enrolled during Medicaid unwinding

Automating returned mail to keep members enrolled during Medicaid unwinding

As the COVID-19 public health emergency (PHE) begins to wind down, starting April 1, 2023, state Medicaid agencies (SMAs) will have one year to “unwind” temporary COVID-era changes and return to pre-pandemic ways of working. For nearly a year, AWS has supported SMAs with in-house Medicaid expertise to identify unwinding issues and develop solutions to address them. The top four concerns that SMAs have shared for unwinding are constituent outreach and engagement, staffing shortages, returned mail, and reporting capabilities. In this blog post, explore how AWS can help with automating returned mail to support members in keeping their coverage.

Analyzing vehicle fleet location data from a data lake with AWS

At AWS, many public sector customers operate fleets of vehicles (e.g. emergency response, public transportation) that generate location data, which is ultimately stored in a data lake. These customers frequently ask how they can quickly visualize this data and extract insights that can help them optimize how they operate their vehicle fleets. In this post, learn how to use Amazon Athena and Amazon Location Service to perform ad hoc reverse geocoding on a notional dataset of vehicle location history, and visualize the results on an Amazon QuickSight map.

4 ways AWS can help with Medicaid unwinding

Beginning on April 1, 2023, state Medicaid agencies (SMA) will have one year to “unwind” temporary COVID-era changes and return to pre-pandemic ways of working. A major part of that will be re-verifying that all 91 million members still qualify to receive Medicaid benefits. For nearly a year, AWS has supported SMAs with in-house Medicaid expertise to identify unwinding issues and develop solutions to address them. The top four concerns that SMAs have shared are in approaching outreach and engagement, staffing shortages, returned mail, and reporting capabilities. Learn how AWS can help states across the country overcome these challenges across different scenarios.

Supporting state agencies with Medicaid unwinding outreach: Creating a multi-lingual two-way messaging system

A key focus for the Department of Health and Human Services (HHS) and state Medicaid agencies is making sure those eligible for Medicaid maintain coverage and supporting transition to alternatives. Medicaid agencies need to conduct outreach to make their millions of members aware of the process for redetermination. With cloud-based tools from AWS, state agencies can conduct this outreach using no code/low code, serverless, elastic services that can scale to two billion text messages a day. In this blog post, learn how to set up a multi-lingual, interactive SMS message campaign that can automatically verify and update member information on file based on member responses.

Designing an educational big data analysis architecture with AWS

In this blog post, learn a high-level architecture, built on AWS, that uses a graph database to analyze unstructured and structured educational data that can, for example, help inform a recommendation to a student for the appropriate courses to take in their next semester based on multiple personalized data factors.