All posts by Sukhomoy Basak

Improving medical imaging workflows with AWS HealthImaging and SageMaker

Post Syndicated from Sukhomoy Basak original https://aws.amazon.com/blogs/architecture/improving-medical-imaging-workflows-with-aws-healthimaging-and-sagemaker/

Medical imaging plays a critical role in patient diagnosis and treatment planning in healthcare. However, healthcare providers face several challenges when it comes to managing, storing, and analyzing medical images. The process can be time-consuming, error-prone, and costly.

There’s also a radiologist shortage across regions and healthcare systems, making the demand for this specialty increases due to an aging population, advances in imaging technology, and the growing importance of diagnostic imaging in healthcare.

As the demand for imaging studies continues to rise, the limited number of available radiologists results in delays in available appointments and timely diagnoses. And while technology enables healthcare delivery improvements for clinicians and patients, hospitals seek additional tools to solve their most pressing challenges, including:

  • Professional burnout due to an increasing demand for imaging and diagnostic services
  • Labor-intensive tasks, such as volume measurement or structural segmentation of images
  • Increasing expectations from patients expecting high-quality healthcare experiences that match retail and technology in terms of convenience, ease, and personalization

To improve clinician and patient experiences, run your picture archiving and communication system (PACS) with an artificial intelligence (AI)-enabled diagnostic imaging cloud solution to securely gain critical insights and improve access to care.

AI helps reduce the radiologist burndown rate through automation. For example, AI saves radiologist chest x-ray interpretation time. It is also a powerful tool to identify areas that need closer inspection, and helps capture secondary findings that weren’t initially identified. The advancement of interoperability and analytics gives radiologist a 360-degree, longitudinal view of patient health records to provide better healthcare at potentially lower costs.

AWS offers services to address these challenges. This blog post discusses AWS HealthImaging (AWS AHI) and Amazon SageMaker, and how they are used together to improve healthcare providers’ medical imaging workflows. This ultimately accelerates imaging diagnostics and increases radiology productivity. AWS AHI enables developers to deliver performance, security, and scale to cloud-native medical imaging applications. It allows ingestion of Digital Imaging and Communication in Medicine (DICOM) images. Amazon SageMaker provides end-to-end solution for AI and machine learning.

Let’s explore an example use case involving X-rays after an auto accident. In this diagnostic medical imaging workflow, a patient is in the emergency room. From there:

  • The patient undergoes an X-ray to check for fractures.
  • The scanned acquisition device images flow to the PACS system.
  • The radiologist reviews the information gathered from this procedure and authors the report.
  • The patient workflow continues as the reports are made available to the referring physician.

Next-generation imaging solutions and workflows

Healthcare providers can use AWS AHI and Amazon SageMaker together to enable next-generation imaging solutions and improve medical imaging workflows. The following architecture illustrates this example.

X-ray images are sent to AWS HealthImaging and an Amazon SageMaker endpoint extracts insights.

Figure 1: X-ray images are sent to AWS HealthImaging and an Amazon SageMaker endpoint extracts insights.

Let’s review the architecture and the key components:

1. Imaging Scanner: Captures the images from a patient’s body. Depending on the modality, this can be an X-ray detector; a series of detectors in a CT scanner; a magnetic field and radio frequency coils in an MRI scanner; or an ultrasound transducer. This example uses an X-ray device.

2. Amazon SQS message queue: Consumes event from S3 bucket and triggers an AWS Step Functions workflow orchestration.

3. AWS Step Functions runs the transform and import jobs to further process and import the images into AWS AHI data store instance.

4. The final diagnostic image—along with any relevant patient information and metadata—is stored in the AWS AHI datastore. This allows for efficient imaging date retrieval and management. It also enables medical imaging data access with sub-second image retrieval latencies at scale, powered by cloud-native APIs and applications from AWS partners.

5. Radiologists responsible for ground truth for ML images perform medical image annotations using Amazon SageMaker Ground Truth. They visualize and label DICOM images using a custom data labeling workflow—a fully managed data labeling service that supports built-in or custom data labeling workflows. They also leverage tools like 3D Slicer for interactive medical image annotations.

6. Data scientists build or leverage built-in deep learning models using the annotated images on Amazon SageMaker. SageMaker offers a range of deployment options that vary from low latency and high throughput to long-running inference jobs. These options include considerations for batch, real-time, or near real-time inference.

7. Healthcare providers use AWS AHI and Amazon SageMaker to run AI-assisted detection and interpretation workflow. This workflow is used to identify hard-to-see fractures, dislocations, or soft tissue injuries to allow surgeons and radiologist to be more confident in their treatment choices.

8. Finally, the image stored in AWS AHI is displayed on a monitor or other visual output device where it can be analyzed and interpreted by a radiologist or other medical professional.

  • The Open Health Imaging Foundation (OHIF) Viewer is an open source, web-based, medical imaging platform. It provides a core framework for building complex imaging applications.
  • Radical Imaging or Arterys are AWS partners that provide OHIF-based medical imaging viewer.

Each of these components plays a critical role in the overall performance and accuracy of the medical imaging system as well as ongoing research and development focused on improving diagnostic outcomes and patient care. AWS AHI uses efficient metadata encoding, lossless compression, and progressive resolution data access to provide industry leading performance for loading images. Efficient metadata encoding enables image viewers and AI algorithms to understand the contents of a DICOM study without having to load the image data.

Security

The AWS shared responsibility model applies to data protection in AWS AHI and Amazon SageMaker.

Amazon SageMaker is HIPAA-eligible and can operate with data containing Protected Health Information (PHI). Encryption of data in transit is provided by SSL/TLS and is used when communicating both with the front-end interface of Amazon SageMaker (to the Notebook) and whenever Amazon SageMaker interacts with any other AWS services.

AWS AHI is also HIPAA-eligible service and provides access control at the metadata level, ensuring that each user and application can only see the images and metadata fields that are required based upon their role. This prevents the proliferation of Patient PHI. All access to AWS AHI APIs is logged in detail in AWS CloudTrail.

Both of these services leverage AWS Key Management service (AWS KMS) to satisfy the requirement that PHI data is encrypted at rest.

Conclusion

In this post, we reviewed a common use case for early detection and treatment of conditions, resulting in better patient outcomes. We also covered an architecture that can transform the radiology field by leveraging the power of technology to improve accuracy, efficiency, and accessibility of medical imaging.

Further reading

Stream change data to Amazon Kinesis Data Streams with AWS DMS

Post Syndicated from Sukhomoy Basak original https://aws.amazon.com/blogs/big-data/stream-change-data-to-amazon-kinesis-data-streams-with-aws-dms/

In this post, we discuss how to use AWS Database Migration Service (AWS DMS) native change data capture (CDC) capabilities to stream changes into Amazon Kinesis Data Streams.

AWS DMS is a cloud service that makes it easy to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to migrate your data into the AWS Cloud or between combinations of cloud and on-premises setups. AWS DMS also helps you replicate ongoing changes to keep sources and targets in sync.

CDC refers to the process of identifying and capturing changes made to data in a database and then delivering those changes in real time to a downstream system. Capturing every change from transactions in a source database and moving them to the target in real time keeps the systems synchronized, and helps with real-time analytics use cases and zero-downtime database migrations.

Kinesis Data Streams is a fully managed streaming data service. You can continuously add various types of data such as clickstreams, application logs, and social media to a Kinesis stream from hundreds of thousands of sources. Within seconds, the data will be available for your Kinesis applications to read and process from the stream.

AWS DMS can do both replication and migration. Kinesis Data Streams is most valuable in the replication use case because it lets you react to replicated data changes in other integrated AWS systems.

This post is an update to the post Use the AWS Database Migration Service to Stream Change Data to Amazon Kinesis Data Streams. This new post includes steps required to configure AWS DMS and Kinesis Data Streams for a CDC use case. With Kinesis Data Streams as a target for AWS DMS, we make it easier for you to stream, analyze, and store CDC data. AWS DMS uses best practices to automatically collect changes from a data store and stream them to Kinesis Data Streams.

With the addition of Kinesis Data Streams as a target, we’re helping customers build data lakes and perform real-time processing on change data from your data stores. You can use AWS DMS in your data integration pipelines to replicate data in near-real time directly into Kinesis Data Streams. With this approach, you can build a decoupled and eventually consistent view of your database without having to build applications on top of a database, which is expensive. You can refer to the AWS whitepaper AWS Cloud Data Ingestion Patterns and Practices for more details on data ingestion patters.

AWS DMS sources for real-time change data

The following diagram illustrates that AWS DMS can use many of the most popular database engines as a source for data replication to a Kinesis Data Streams target. The database source can be a self-managed engine running on an Amazon Elastic Compute Cloud (Amazon EC2) instance or an on-premises database, or it can be on Amazon Relational Database Service (Amazon RDS), Amazon Aurora, or Amazon DocumentDB (with MongoDB availability).

Kinesis Data Streams can collect, process, and store data streams at any scale in real time and write to AWS Glue, which is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. You can use Amazon EMR for big data processing, Amazon Kinesis Data Analytics to process and analyze streaming data , Amazon Kinesis Data Firehose to run ETL (extract, transform, and load) jobs on streaming data, and AWS Lambda as a serverless compute for further processing, transformation, and delivery of data for consumption.

You can store the data in a data warehouse like Amazon Redshift, which is a cloud-scale data warehouse, and in an Amazon Simple Storage Service (Amazon S3) data lake for consumption. You can use Kinesis Data Firehose to capture the data streams and load the data into S3 buckets for further analytics.

Once the data is available in Kinesis Data Streams targets (as shown in the following diagram), you can visualize it using Amazon QuickSight; run ad hoc queries using Amazon Athena; access, process, and analyze it using an Amazon SageMaker notebook instance; and efficiently query and retrieve structured and semi-structured data from files in Amazon S3 without having to load the data into Amazon Redshift tables using Amazon Redshift Spectrum.

Solution overview

In this post, we describe how to use AWS DMS to load data from a database to Kinesis Data Streams in real time. We use a SQL Server database as example, but other databases like Oracle, Microsoft Azure SQL, PostgreSQL, MySQL, SAP ASE, MongoDB, Amazon DocumentDB, and IBM DB2 also support this configuration.

You can use AWS DMS to capture data changes on the database and then send this data to Kinesis Data Streams. After the streams are ingested in Kinesis Data Streams, they can be consumed by different services like Lambda, Kinesis Data Analytics, Kinesis Data Firehose, and custom consumers using the Kinesis Client Library (KCL) or the AWS SDK.

The following are some use cases that can use AWS DMS and Kinesis Data Streams:

  • Triggering real-time event-driven applications – This use case integrates Lambda and Amazon Simple Notification Service (Amazon SNS).
  • Simplifying and decoupling applications – For example, moving from monolith to microservices. This solution integrates Lambda and Amazon API Gateway.
  • Cache invalidation, and updating or rebuilding indexes – Integrates Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) and Amazon DynamoDB.
  • Data integration across multiple heterogeneous systems – This solution sends data to DynamoDB or another data store.
  • Aggregating data and pushing it to downstream system – This solution uses Kinesis Data Analytics to analyze and integrate different sources and load the results in another data store.

To facilitate the understanding of the integration between AWS DMS, Kinesis Data Streams, and Kinesis Data Firehose, we have defined a business case that you can solve. In this use case, you are the data engineer of an energy company. This company uses Amazon Relational Database Service (Amazon RDS) to store their end customer information, billing information, and also electric meter and gas usage data. Amazon RDS is their core transaction data store.

You run a batch job weekly to collect all the transactional data and send it to the data lake for reporting, forecasting, and even sending billing information to customers. You also have a trigger-based system to send emails and SMS periodically to the customer about their electricity usage and monthly billing information.

Because the company has millions of customers, processing massive amounts of data every day and sending emails or SMS was slowing down the core transactional system. Additionally, running weekly batch jobs for analytics wasn’t giving accurate and latest results for the forecasting you want to do on customer gas and electricity usage. Initially, your team was considering rebuilding the entire platform and avoiding all those issues, but the core application is complex in design, and running in production for many years and rebuilding the entire platform will take years and cost millions.

So, you took a new approach. Instead of running batch jobs on the core transactional database, you started capturing data changes with AWS DMS and sending that data to Kinesis Data Streams. Then you use Lambda to listen to a particular data stream and generate emails or SMS using Amazon SNS to send to the customer (for example, sending monthly billing information or notifying when their electricity or gas usage is higher than normal). You also use Kinesis Data Firehose to send all transaction data to the data lake, so your company can run forecasting immediately and accurately.

The following diagram illustrates the architecture.

In the following steps, you configure your database to replicate changes to Kinesis Data Streams, using AWS DMS. Additionally, you configure Kinesis Data Firehose to load data from Kinesis Data Streams to Amazon S3.

It’s simple to set up Kinesis Data Streams as a change data target in AWS DMS and start streaming data. For more information, see Using Amazon Kinesis Data Streams as a target for AWS Database Migration Service.

To get started, you first create a Kinesis data stream in Kinesis Data Streams, then an AWS Identity and Access Management (IAM) role with minimal access as described in Prerequisites for using a Kinesis data stream as a target for AWS Database Migration Service. After you define your IAM policy and role, you set up your source and target endpoints and replication instance in AWS DMS. Your source is the database that you want to move data from, and the target is the database that you’re moving data to. In our case, the source database is a SQL Server database on Amazon RDS, and the target is the Kinesis data stream. The replication instance processes the migration tasks and requires access to the source and target endpoints inside your VPC.

A Kinesis delivery stream (created in Kinesis Data Firehose) is used to load the records from the database to the data lake hosted on Amazon S3. Kinesis Data Firehose can load data also to Amazon Redshift, Amazon OpenSearch Service, an HTTP endpoint, Datadog, Dynatrace, LogicMonitor, MongoDB Cloud, New Relic, Splunk, and Sumo Logic.

Configure the source database

For testing purposes, we use the database democustomer, which is hosted on a SQL Server on Amazon RDS. Use the following command and script to create the database and table, and insert 10 records:

create database democustomer

use democustomer

create table invoices (
	invoice_id INT,
	customer_id INT,
	billing_date DATE,
	due_date DATE,
	balance INT,
	monthly_kwh_use INT,
	total_amount_due VARCHAR(50)
);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (1, 1219578, '4/15/2022', '4/30/2022', 25, 6, 28);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (2, 1365142, '4/15/2022', '4/28/2022', null, 41, 20.5);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (3, 1368834, '4/15/2022', '5/5/2022', null, 31, 15.5);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (4, 1226431, '4/15/2022', '4/28/2022', null, 47, 23.5);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (5, 1499194, '4/15/2022', '5/1/2022', null, 39, 19.5);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (6, 1221240, '4/15/2022', '5/2/2022', null, 38, 19);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (7, 1235442, '4/15/2022', '4/27/2022', null, 50, 25);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (8, 1306894, '4/15/2022', '5/2/2022', null, 16, 8);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (9, 1343570, '4/15/2022', '5/3/2022', null, 39, 19.5);
insert into invoices (invoice_id, customer_id, billing_date, due_date, balance, monthly_kwh_use, total_amount_due) values (10, 1465198, '4/15/2022', '5/4/2022', null, 47, 23.5);

To capture the new records added to the table, enable MS-CDC (Microsoft Change Data Capture) using the following commands at the database level (replace SchemaName and TableName). This is required if ongoing replication is configured on the task migration in AWS DMS.

EXEC msdb.dbo.rds_cdc_enable_db 'democustomer';
GO
EXECUTE sys.sp_cdc_enable_table @source_schema = N'SchemaName', @source_name =N'TableName', @role_name = NULL;
GO
EXEC sys.sp_cdc_change_job @job_type = 'capture' ,@pollinginterval = 3599;
GO

You can use ongoing replication (CDC) for a self-managed SQL Server database on premises or on Amazon Elastic Compute Cloud (Amazon EC2), or a cloud database such as Amazon RDS or an Azure SQL managed instance. SQL Server must be configured for full backups, and you must perform a backup before beginning to replicate data.

For more information, see Using a Microsoft SQL Server database as a source for AWS DMS.

Configure the Kinesis data stream

Next, we configure our Kinesis data stream. For full instructions, see Creating a Stream via the AWS Management Console. Complete the following steps:

  1. On the Kinesis Data Streams console, choose Create data stream.
  2. For Data stream name¸ enter a name.
  3. For Capacity mode, select On-demand.When you choose on-demand capacity mode, Kinesis Data Streams instantly accommodates your workloads as they ramp up or down. For more information, refer to Choosing the Data Stream Capacity Mode.
  4. Choose Create data stream.
  5. When the data stream is active, copy the ARN.

Configure the IAM policy and role

Next, you configure your IAM policy and role.

  1. On the IAM console, choose Policies in the navigation pane.
  2. Choose Create policy.
  3. Select JSON and use the following policy as a template, replacing the data stream ARN: 
    {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "kinesis:PutRecord",
                "kinesis:PutRecords",
                "kinesis:DescribeStream"
            ],
            "Resource": "<streamArn>"
        }
    ]
}

  4. In the navigation pane, choose Roles.
  5. Choose Create role.
  6. Select AWS DMS, then choose Next: Permissions.
  7. Select the policy you created.
  8. Assign a role name and then choose Create role.

Configure the Kinesis delivery stream

We use a Kinesis delivery stream to load the information from the Kinesis data stream to Amazon S3. To configure the delivery stream, complete the following steps:

  1. On the Kinesis console, choose Delivery streams.
  2. Choose Create delivery stream.
  3. For Source, choose Amazon Kinesis Data Streams.
  4. For Destination, choose Amazon S3.
  5. For Kinesis data stream, enter the ARN of the data stream.
  6. For Delivery stream name, enter a name.
  7. Leave the transform and convert options at their defaults.
  8. Provide the destination bucket and specify the bucket prefixes for the events and errors.
  9. Under Buffer hints, compression and encryption, change the buffer size to 1 MB and buffer interval to 60 seconds.
  10. Leave the other configurations at their defaults.

Configure AWS DMS

We use an AWS DMS instance to connect to the SQL Server database and then replicate the table and future transactions to a Kinesis data stream. In this section, we create a replication instance, source endpoint, target endpoint, and migration task. For more information about endpoints, refer to Creating source and target endpoints.

  1. Create a replication instance in a VPC with connectivity to the SQL Server database and associate a security group with enough permissions to access to the database.
  2. On the AWS DMS console, choose Endpoints in the navigation pane.
  3. Choose Create endpoint.
  4. Select Source endpoint.
  5. For Endpoint identifier, enter a label for the endpoint.
  6. For Source engine, choose Microsoft SQL Server.
  7. For Access to endpoint database, select Provide access information manually.
  8. Enter the endpoint database information.
  9. Test the connectivity to the source endpoint.
    Now we create the target endpoint.
  10. On the AWS DMS console, choose Endpoints in the navigation pane.
  11. Choose Create endpoint.
  12. Select Target endpoint.
  13. For Endpoint identifier, enter a label for the endpoint.
  14. For Target engine, choose Amazon Kinesis.
  15. Provide the AWS DMS service role ARN and the data stream ARN.
  16. Test the connectivity to the target endpoint.

    The final step is to create a database migration task. This task replicates the existing data from the SQL Server table to the data stream and replicates the ongoing changes. For more information, see Creating a task.
  17. On the AWS DMS console, choose Database migration tasks.
  18. Choose Create task.
  19. For Task identifier, enter a name for your task.
  20. For Replication instance, choose your instance.
  21. Choose the source and target database endpoints you created.
  22. For Migration type, choose Migrate existing data and replicate ongoing changes.
  23. In Task settings, use the default settings.
  24. In Table mappings, add a new selection rule and specify the schema and table name of the SQL Server database. In this case, our schema name is dbo and the table name is invoices.
  25. For Action, choose Include.

When the task is ready, the migration starts.

After the data has been loaded, the table statistics are updated and you can see the 10 records created initially.

As the Kinesis delivery stream reads the data from Kinesis Data Streams and loads it in Amazon S3, the records are available in the bucket you defined previously.

To check that AWS DMS ongoing replication and CDC are working, use this script to add 1,000 records to the table.

You can see 1,000 inserts on the Table statistics tab for the database migration task.

After about 1 minute, you can see the records in the S3 bucket.

At this point the replication has been activated, and a Lambda function can start consuming the data streams to send emails SMS to the customers through Amazon SNS. More information, refer to Using AWS Lambda with Amazon Kinesis.

Conclusion

With Kinesis Data Streams as an AWS DMS target, you now have a powerful way to stream change data from a database directly into a Kinesis data stream. You can use this method to stream change data from any sources supported by AWS DMS to perform real-time data processing. Happy streaming!

If you have any questions or suggestions, please leave a comment.

About the Authors

Luis Eduardo Torres is a Solutions Architect at AWS based in Bogotá, Colombia. He helps companies to build their business using the AWS cloud platform. He has a great interest in Analytics and has been leading the Analytics track of AWS Podcast in Spanish.

Sukhomoy Basak is a Solutions Architect at Amazon Web Services, with a passion for Data and Analytics solutions. Sukhomoy works with enterprise customers to help them architect, build, and scale applications to achieve their business outcomes.

Address Modernization Tradeoffs with Lake House Architecture

Post Syndicated from Sukhomoy Basak original https://aws.amazon.com/blogs/architecture/address-modernization-tradeoffs-with-lake-house-architecture/

Many organizations are modernizing their applications to reduce costs and become more efficient. They must adapt to modern application requirements that provide 24×7 global access. The ability to scale up or down quickly to meet demand and process a large volume of data is critical. This is challenging while maintaining strict performance and availability. For many companies, modernization includes decomposing a monolith application into a set of independently developed, deployed, and managed microservices. The decoupled nature of a microservices environment allows each service to evolve agilely and independently. While there are many benefits for moving to a microservices-based architecture, there can be some tradeoffs. As your application monolith evolves into independent microservices, you must consider the implications to your data architecture.

In this blog post we will provide example use cases, and show how Lake House Architecture on AWS can streamline your microservices architecture. A Lake house architecture embraces the decentralized nature of microservices by facilitating data movement. These transfers can be between data stores, from data stores to data lake, and from data lake to data stores (Figure 1).

Figure 1. Integrating data lake, data warehouse, and all purpose-built stores into a coherent whole

Figure 1. Integrating data lake, data warehouse, and all purpose-built stores into a coherent whole

Health and wellness application challenges

Our fictitious health and wellness customer has an application architecture comprised of several microservices backed by purpose-built data stores. User profiles, assessments, surveys, fitness plans, health preferences, and insurance claims are maintained in an Amazon Aurora MySQL-Compatible relational database. The event service monitors the number of steps walked, sleep pattern, pulse rate, and other behavioral data in Amazon DynamoDB, a NoSQL database (Figure 2).

Figure 2. Microservices architecture for health and wellness company

Figure 2. Microservices architecture for health and wellness company

With this microservices architecture, it’s common to have data spread across various data stores. This is because each microservice uses a purpose-built data store suited to its usage patterns and performance requirements. While this provides agility, it also presents challenges to deriving needed insights.

Here are four challenges that different users might face:

  1. As a health practitioner, how do I efficiently combine the data from multiple data stores to give personalized recommendations that improve patient outcomes?
  2. As a sales and marketing professional, how do I get a 360 view of my customer, when data lives in multiple data stores? Profile and fitness data are in a relational data store, but important behavioral and clickstream data are in NoSQL data stores. It’s hard for me to run targeted marketing campaigns, which can lead to revenue loss.
  3. As a product owner, how do I optimize healthcare costs when designing wellbeing programs for patients?
  4. As a health coach, how do I find patients and help them with their wellness goals?

Our remaining subsections highlight AWS Lake House Architecture capabilities and features that allow data movement and the integration of purpose-built data stores.

1. Patient care use case

In this scenario, a health practitioner is interested in historical patient data to estimate the likelihood of a future outcome. To get the necessary insights and identify patterns, the health practitioner needs event data from Amazon DynamoDB and patient profile data from Aurora MySQL-Compatible. Our health practitioner will use Amazon Athena to run an ad-hoc analysis across these data stores.

Amazon Athena provides an interactive query service for both structured and unstructured data. The federated query functionality in Amazon Athena helps with running SQL queries across data stored in relational, NoSQL, and custom data sources. Amazon Athena uses Lambda-based data source connectors to run federated queries. Figure 3 illustrates the federated query architecture.

Figure 3. Amazon Athena federated query

Figure 3. Amazon Athena federated query

The patient care team could use an Amazon Athena federated query to find out if a patient needs urgent care. It is able to detect anomalies in the combined datasets from claims processing, device data, and electronic health record (HER) as show in Figure 4.

Figure 4. Federated query result by combining data from claim, device, and EHR stores

Figure 4. Federated query result by combining data from claim, device, and EHR stores

Healthcare data from various sources, including EHRs and genetic data, helps improve personalized care. Machine learning (ML) is able to harness big data and perform predictive analytics. This creates opportunities for researchers to develop personalized treatments for various diseases, including cancer and depression.

To achieve this, you must move all the related data into a centralized repository such as an Amazon S3 data lake. For specific use cases, you also must move the data between the purpose-built data stores. Finally, you must build an ML solution that can predict the outcome. Amazon Redshift ML, combined with its federated query processing capabilities enables data analysts and database developers to create a platform to detect patterns (Figure 5). With this platform, health practitioners are able to make more accurate, data-driven decisions.

Figure 5. Amazon Redshift federated query with Amazon Redshift ML

Figure 5. Amazon Redshift federated query with Amazon Redshift ML

2. Sales and marketing use case

To run marketing campaigns, the sales and marketing team must search customer data from a relational database, with event data in a non-relational data store. We will move the data from Aurora MySQL-Compatible and Amazon DynamoDB to Amazon Elasticsearch Service (ES) to meet this requirement.

AWS Database Migration Service (DMS) helps move the change data from Aurora MySQL-Compatible to Amazon ES using Change Data Capture (CDC). AWS Lambda could be used to move the change data from DynamoDB streams to Amazon ES, as shown in Figure 6.

Figure 6. Moving and combining data from Aurora MySQL-Compatible and Amazon DynamoDB to Amazon Elasticsearch Service

Figure 6. Moving and combining data from Aurora MySQL-Compatible and Amazon DynamoDB to Amazon Elasticsearch Service

The sales and marketing team can now run targeted marketing campaigns by querying data from Amazon Elasticsearch Service, see Figure 7. They can improve sales operations by visualizing data with Amazon QuickSight.

Figure 7. Personalized search experience for ad-tech marketing team

Figure 7. Personalized search experience for ad-tech marketing team

3. Healthcare product owner use case

In this scenario, the product owner must define the care delivery value chain. They must develop process maps for patient activity and estimate the cost of patient care. They must analyze these datasets by building business intelligence (BI) reporting and dashboards with a tool like Amazon QuickSight. Amazon Redshift, a cloud scale data warehousing platform, is a good choice for this. Figure 8 illustrates this pattern.

Figure 8. Moving data from Amazon Aurora and Amazon DynamoDB to Amazon Redshift

Figure 8. Moving data from Amazon Aurora and Amazon DynamoDB to Amazon Redshift

The product owners can use integrated business intelligence reports with Amazon Redshift to analyze their data. This way they can make more accurate and appropriate decisions, see Figure 9.

Figure 9. Business intelligence for patient care processes

Figure 9. Business intelligence for patient care processes

4. Health coach use case

In this scenario, the health coach must find a patient based on certain criteria. They would then send personalized communication to connect with the patient to ensure they are following the proposed health plan. This proactive approach contributes to a positive patient outcome. It can also reduce healthcare costs incurred by insurance companies.

To be able to search patient records with multiple data points, it is important to move data from Amazon DynamoDB to Amazon ES. This also will provide a fast and personalized search experience. The health coaches can be notified and will have the information they need to provide guidance to their patients. Figure 10 illustrates this pattern.

Figure 10. Moving Data from Amazon DynamoDB to Amazon ES

Figure 10. Moving Data from Amazon DynamoDB to Amazon ES

The health coaches can use Elasticsearch to search users based on specific criteria. This helps them with counseling and other health plans, as shown in Figure 11.

Figure 11. Simplified personalized search using patient device data

Figure 11. Simplified personalized search using patient device data

Summary

In this post, we highlight how Lake House Architecture on AWS helps with the challenges and tradeoffs of modernization. A Lake House architecture on AWS can help streamline the movement of data between the microservices data stores. This offers new capabilities for various analytics use cases.

For further reading on architectural patterns, and walkthroughs for building Lake House Architecture, see the following resources: