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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
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Step 1
Patients use the patient portal to import personal health data including electronic medical records (EMR), electronic health records (EHR), medical imaging, and omics data.
Step 2
The patient portal sends the data to the personal health record (PHR). The PHR is a harmonized, multimodal record implemented on Amazon HealthLake, Amazon HealthImaging, AWS HealthOmics, and Amazon Simple Storage Service (Amazon S3).
Step 3
A profile builder on AWS Lambda translates PHR to a patient profile in a content management system (CMS). Amazon AppFlow synchronizes the patient profile with a customer relationship management (CRM) system. A call center powered by Amazon Connect supports patients.
Step 4
Data scientists catalog, transform, and analyze data using AWS Glue and Amazon Athena. AWS Lake Formation provides a master patient index and manages data access.
Step 5
A consent manager governs sharing PHR data through AWS Clean Rooms into an Amazon S3 bucket, which is the data source for all downstream analysis.
Step 6
Researchers perform machine learning using Amazon SageMaker-managed Jupyter and R Studio notebooks.
Step 7
Researchers explore data visually using Amazon QuickSight and search data using Amazon OpenSearch Service.
Step 8
Researchers share de-identified data using AWS Data Exchange and Open Data on AWS.
Step 1
Patients use the patient portal to import personal health data including electronic medical records (EMR), electronic health records (EHR), medical imaging, and omics data.
Step 2
The patient portal sends the data to the personal health record (PHR). The PHR is a harmonized, multimodal record implemented on Amazon HealthLake, Amazon HealthImaging, AWS HealthOmics, and Amazon Simple Storage Service (Amazon S3).
Step 3
A profile builder on AWS Lambda translates PHR to a patient profile in a content management system (CMS). Amazon AppFlow synchronizes the patient profile with a customer relationship management (CRM) system. A call center powered by Amazon Connect supports patients.
Step 4
Data scientists catalog, transform, and analyze data using AWS Glue and Amazon Athena. AWS Lake Formation provides a master patient index and manages data access.
Step 5
A consent manager governs sharing PHR data through AWS Clean Rooms into an Amazon S3 bucket, which is the data source for all downstream analysis.
Step 6
Researchers perform machine learning using Amazon SageMaker-managed Jupyter and R Studio notebooks.
Step 7
Researchers explore data visually using Amazon QuickSight and search data using Amazon OpenSearch Service.
Step 8
Researchers share de-identified data using AWS Data Exchange and Open Data on AWS.
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.
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Operational ExcellenceRead the Operational Excellence whitepaper
Use infrastructure as code (IaC) with AWS CodePipeline or other continuous integration, continuous deployment (CI/CD) tools for fast iteration and consistent deployments. Use Amazon CloudWatch for application and infrastructure monitoring.
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SecurityRead the Security whitepaper
The services in this Guidance provide encryption in transit and at rest. You can use AWS Identity and Access Management (IAM), AWS Key Management Service (AWS KMS), and AWS Certificate Manager to manage user and role-based access, encryption keys, and secure socket layer (SSL)/transport layer security (TLS) X.509 certificates.
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ReliabilityRead the Reliability whitepaper
All services in this Guidance have service quotas. AWS service quotas prevent unexpected scaling up of the managed services in this Guidance. We recommend that you test this Guidance at scale to identify and address service quotas.
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Performance EfficiencyRead the Performance Efficiency whitepaper
Use synthetic data and experiment with a small number of patient records before scaling up or onboarding real patient records. Additionally, you should deploy this Guidance in the AWS Region that is closest to end users. You can extend this Guidance through Amazon CloudFront.
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Cost OptimizationRead the Cost Optimization whitepaper
This Guidance minimizes data transfer charges by performing data analytics in the AWS Cloud. Additionally, you can use the AWS Pricing Calculator to evaluate cost based on the services you deploy. No software licenses are required, though you may choose to add licensed software as needed.
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SustainabilityRead the Sustainability whitepaper
The resources in this Guidance scale up and down with demand, so they require only the minimum resources to operate.
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.
Related Content
Guidance for Multi-Modal Data Analysis with AWS Health and ML Services
Disclaimer
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.