AWS Public Sector Blog

Tag: healthcare

Accelerating public health innovation with AWS Partners

Public health agencies are looking to modernize their infrastructure to make sure that their health solutions can scale equitably and reliably in any situation. Many governmental public health agencies across the US look to AWS and the AWS Partner Network to help them innovate quickly. Learn how AWS brought together three governmental public health agencies and partners to create scalable solutions that support public health.

The AWS Healthcare Accelerator Australia and New Zealand members, AWS Healthcare VC and Startups and APJ Healthcare teams, and Accelerator delivery partner ANDHealth met in Sydney to kick off the program.

AWS announces Australia and New Zealand startups selected for AWS Healthcare Accelerator for Aged Care and Digital Health

Today, AWS announced the 11 participants chosen for the AWS Healthcare Accelerator Australia/New Zealand for Aged Care and Digital Health. The cohort is developing a range of solutions, using the power of the cloud, to improve equitable access to health, social, and aged care services; support aged care and health service providers to deliver higher quality care; and promote new and productive ways of delivering services.

Automatically extracting email attachment data to reduce costs and save time for local public health departments

Local public health departments must notify public health agencies, like state health departments or the Centers for Disease Control and Prevention (CDC), of reportable conditions. These departments receive various types of reports of healthcare conditions through email, in addition to more traditional methods such as mail, fax, or phone calls. Local health departments can dramatically reduce the time and costs associated with manually processing email attachments and improve processing efficiency using automation. In this blog post, learn how to create an automated email attachment ingestion, storage, and processing solution powered by artificial intelligence (AI) and machine learning (ML) services from AWS.

Large scale AI in digital pathology without the heavy lifting

Pathology is currently undergoing a transformation. While microscopes still dominate many workflows, digital pathology combined with artificial intelligence (AI) is disrupting the space. AI tools can complement expert assessment with quantitative measurements to enable data-driven medicine. Ultivue is a healthcare technology (HealthTech) company that provides high-quality multiplex immunofluorescence assays and large-scale, AI-based computational pathology—built on AWS.

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.

AMILI helps advance precision medicine by building microbiome library on AWS

AMILI is a healthcare technology (HealthTech) company based in Singapore that seeks to advance precision medicine and personalized health and nutrition by harnessing the potential of the microbiome. AMILI uses artificial intelligence (AI) and machine learning (ML) on AWS to comprehensively quantify and characterize gut microbiomes. AMILI aims to build and curate the world’s largest multi-ethnic Asia microbiome database.

How Digithurst and Telepaxx built a secure and scalable radiology solution chain using AWS

Medical software development companies Digithurst and Telepaxx worked together to create an end-to-end cloud solution chain handling administration of patient data and their radiological scans; viewing and editing of scans; as well as long-term archiving. To develop a scalable, secure, and cost effective solution chain supporting further innovations, the companies turned to the AWS Cloud.

Children’s Brain Tumor Network (CBTN), Amazon Web Services (AWS) clinical leadership, and patient advocates met to discuss how technology can improve approaches to pediatric oncology research.

Pediatric cancer researchers use AWS to accelerate Cancer Moonshot

Earlier this year, US President Joe Biden set a goal to end cancer as we know it by improving prevention, screening, diagnosis, and treatment. To answer this call, AWS is expanding its ongoing work with the Children’s Brain Tumor Network (CBTN). Together, AWS and the CBTN will enable researchers and clinicians to share and analyze medical record, imaging, genomic, and other data in near real-time to speed development of new therapies for pediatric brain cancers.

Pictured: Adam Glasofer, MD, global head of healthcare for public sector VC and startups at Amazon Web Services (AWS), announces the new AWS Healthcare Accelerator Global Cohort for Workforce Development at the HLTH 2022 event in Las Vegas, November 16, 2022.

AWS launches AWS Healthcare Accelerator Global Cohort for Workforce Development

Supporting and protecting the healthcare workforce is an essential investment in the continuity of health services. That’s why AWS is choosing to focus on training, retaining, and deploying healthcare workers with the launch of a new AWS Healthcare Accelerator. This is AWS’s first ever global healthcare cohort focused on workforce development.

Predicting diabetic patient readmission using multi-model training on Amazon SageMaker Pipelines

Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs.