Tag: healthcare

In this post, we explore the complexities of data privacy and controls on Amazon Web Services (AWS), examine how creating a landing zone within which to contain such data is important, and highlight the differences between creating a landing zone from scratch compared with using the AWS Landing Zone Accelerator (LZA) for Healthcare. To aid explanation, we use a simple healthcare workload as an example. We also explain how LZA for Healthcare codifies HIPAA controls and AWS Security Best Practices to accelerate the creation of an environment to run protective health information workloads in AWS.

The European Health Data Space (EHDS) will establish a common framework and infrastructure for the use of health data for research, innovation, policy-making, and regulatory activities in the European Union (EU). It will also create common standards and practices enabling EU citizens to better access, control, and share their electronic personal health data. Read this post to learn why Amazon Web Services (AWS) welcomes the EHDS as an important step towards unleashing the vast potential of health data to benefit citizens across Europe and beyond.

The Amazon Web Services (AWS) Well-Architected Framework is designed to help build resilient, secure, and efficient solutions. Understanding this framework can greatly benefit AWS customers looking to enhance and refine their cloud environments. This post shares insights into how NHS England, responsible for running the vital national IT systems which support health and social care, revolutionized their approach to the AWS Well-Architected Framework review process.

Healthcare organizations invest heavily in technology and data. Generative artificial intelligence (AI) empowers healthcare organizations to leverage their investments in robust data foundations, improve patient experience through innovative interactive technologies, boost productivity to help address workforce challenges, and drive new insights to accelerate research. This post highlights three examples of how generative AI on Amazon Web Services (AWS) is being used in healthcare and discusses ways to leverage this technology in a responsible, safe way.

The University of Pittsburgh, the University of Illinois Urbana-Champaign (UIUC), the University of California Davis Health Cloud Innovation Center (UCDH CIC)—powered by Amazon Web Services (AWS)—and the AWS Digital Innovation (DI) team have built a prototype that uses machine learning (ML) and generative artificial intelligence (AI) to transform the public health communications landscape by giving officials the tools they need to fight medical misinformation, disinformation, and malinformation.

UK Biobank, the world’s most comprehensive source of health data used for research, needed a purpose-built data platform with compute and data-storage capabilities that provided analysis tools in a centralized environment and the flexibility to manage increasing quantities of data. This led to the establishment and launch of the secure, cloud-based UK Biobank Research Analysis Platform (RAP), which is hosted on Amazon Web Services (AWS). Read this post to learn more about UK Biobank’s journey to becoming a globally accessible dataset for health researchers.

The Association of Schools and Programs of Public Health (ASPPH) — a nonprofit association with a vision for improved health and well-being for everyone, everywhere — partnered with Amazon Web Services (AWS) Professional Services (AWS ProServe) to move their curated data to a managed data lake on AWS. In this blog post, we share how ASPPH and AWS designed and built the data lake and the results of moving to a modern, scalable data architecture.

Healthcare authorities and providers use state-run controlled substance databases (CSDs) to track prescriptions and identify patients for substance abuse. CSDs help evaluate treatment options, screen patients who may be at risk for drug abuse problems, and make informed decisions about prescribing medication. This post explains how healthcare authorities can leverage CSD data to enhance their decision-making processes within business operations by using Amazon Web Services (AWS).

Amazon Rekognition, a code-free automated machine learning (AutoML) service from Amazon Web Services (AWS), showed impeccable diagnostic performance in categorizing various retinal diseases using optical coherence tomography (OCT) scans. This blog post details the steps to use Amazon Rekognition Custom Labels to train a model that categorizes retinal diseases and the process of training and fine-tuning convolutional neural networks (CNNs), the standard deep learning methodology.

Learn how the National Cancer Institute Imaging Data Commons (IDC) team migrated the Imaging Data Commons data to AWS using AWS DataSync. Plus, learn how to get started with IDC data, which is accessible at no cost through the AWS Open Data Sponsorship Program.