Category: Public Sector

Security is priority number one at AWS. Data stored in Amazon Simple Storage Service (Amazon S3) is private by default. However, some datasets are made to be shared. In this blog post, we cover the no-cost mechanisms data providers can utilize to create access control policies for their highly distributed open datasets.

Today, at the Amazon Web Services (AWS) Toronto Summit 2022, we announced that AWS EdStart, the AWS educational technology (EdTech) startup accelerator designed to help entrepreneurs build the next generation of online learning, analytics, and campus management solutions on the AWS Cloud, is now available to customers in Canada.

Artificial intelligence (AI) and machine learning (ML) are critical tools being used in healthcare research, autonomous applications, predictive maintenance, and also a key tool used to advance sustainability solutions. However, to use AI and ML to solve sustainability problems, innovators need specific datasets that are prepared for analysis and training of the models. To help create and accelerate sustainability solutions, the Amazon Sustainability Data Initiative (ASDI) today announced easier identification of sustainability datasets with integration in AWS Data Exchange and the launch of a sustainability hackathon.

Customer experience (CX) has emerged as a key priority in the US following the 2021 Biden Administration Executive Order (EO) to transform federal customer experience and service delivery. Application modernization enables agencies to simplify business processes and provide customers with flexible, interactive, and simple to use applications, resulting in improved CX. In this blog post, we present an AWS architecture framework that agencies can use to develop and deploy a modern application that helps improve CX.

King Hamad University Hospital (KHUH) and Bahrain Oncology Center is a 600-bed-hospital in Bahrain. Over the years, KHUH faced constraints with exponential growth of their on-premise storage needs, particularly with the medical images stored by their picture archiving and communication system (PACS). KHUH turned to AWS to develop a cost- and time-effective long-term storage solution, without making changes to their existing PACS, that reduced storage costs by 40%.

The healthcare technology (HealthTech) startup contextflow uses AWS to power its clinical decision support system that can help radiologists diagnose patients faster and with more accuracy. The software can search, retrieve, and compare historical diagnostic data to help guide radiologists in writing reports. It is currently being utilized to analyze lung computed tomography (CT) scans for interstitial lung diseases (ILD), chronic obstructive pulmonary disease (COPD), and lung cancer screenings.

Public sector organizations and commercial enterprises alike are reaping the benefits of AWS Ground Station, which lets customers command and control satellite communications, process satellite data, and scale their satellite operations. This article provides a general overview of AWS Ground Station, its key benefits, and general advice for getting started.

The Wings for Life World Run is a global charity run that has raised funds to help find a cure for spinal cord injury since 2014. Every year, the event gives 100% of its entry fees and donations to research supported by Wings for Life, a not-for-profit foundation that supports spinal cord research. Participants can join the event worldwide at the same time, using the Wings for Life World Run App. To scale to meet the spike in demand to the Wings for Life World Run App and give real-time leaderboard updates for the global virtual race, Wings for Life World Run uses AWS.

Data lakes can help hospitals and healthcare organizations turn data into insights and maintain business continuity, while preserving patient privacy. This blog post is part of a larger series about getting started with setting up a healthcare data lake. In this blog post, I detail how the solution has evolved at a foundational level over the series to include microservices. I describe the design decisions I’ve made and the additional features used. You can access code samples for this solution through a GitHub repo for reference.

To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.