This Guidance provides nonprofit organizations, health information exchanges, and healthcare provider networks a way to curate personal health information (PHI) on behalf of patients. Patient advocacy nonprofits serve as a PHI broker between patients and researchers. These nonprofits only share PHI if they have consent and can inform patients about specific research and discoveries derived from patient data. This reinforces a patient’s willingness to share PHI, accelerating research outcomes. This Guidance helps nonprofits ingest, transform, anonymize, and deliver PHI data to academic and commercial research development organizations, all while keeping the patient informed and protecting patient consent.

For additional support in setting up an end-to-end framework for multimodal healthcare data, visit Guidance for Multi-Modal Data Analysis with AWS Health and ML Services.

Please note: [Disclaimer]

Architecture Diagram


Download the architecture diagram PDF 

Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Implementation Resources

A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.

The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.


Guidance for Multi-Modal Data Analysis with AWS Health and ML Services

This Guidance demonstrates how to set up an end-to-end framework to analyze multimodal healthcare and life sciences (HCLS) data.


The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.

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