How to convert unstructured medical data into Fast Healthcare Interoperability Resources (FHIR) files using Amazon HealthLake, a HIPAA-eligible service, and then analyze and get new insights
Benefits of AWS Health Lake
With AWS HealthLake, you can:
- Quickly and easily ingest health data — You can bulk import on-premises Fast Healthcare Interoperability Resources (FHIR) files, including clinical notes, lab reports, insurance claims, and more, to an Amazon Simple Storage Service (Amazon S3) bucket. You can then use the data in downstream applications or workflows.
- Use the FHIR REST API operations — HealthLake supports using the FHIR REST API operations to perform CRUD (Create/Read/Update/Delete) operations on your data store. FHIR search is also supported.
- Store your data in the AWS Cloud in a secure, HIPAA-eligibile manner that can be audited — You can store data in the FHIR format, so it can be easily queried. HealthLake creates a complete, chronological view of each patient's medical history, and structures it in the R4 FHIR standard format.
- Athena integration — HealthLake's integration with Athena means you can create powerful SQL-based queries that you can use to create and save complex filter criteria. Then, you can use this data in downstream applications such as SageMaker to train a machine learning model or Amazon QuickSight to create dashboards and data visualizations.
- Transform unstructured data using specialized machine learning (ML) models — HealthLake provides integrated medical natural language processing (NLP) using Amazon Comprehend Medical. Raw medical text data is transformed using specialized ML models. These models have been trained to understand and extract meaningful information from unstructured healthcare data. With integrated medical NLP, you can automatically extract entities (for example, medical procedures and medications), entity relationships (for example, a medication and its dosage), and entity traits (for example, positive or negative test result or time of procedure) data from your medical text. HealthLake then creates new resources based on the traits sign, symptom, and condition. These are added as new Condition, Observation, and Medication Statement resource types.
Note
After February 20, 2023, HealthLake data stores do not use integrated natural language processing (NLP) by default. If you are interested in turning on this feature on your data store, see below-
How do I turn on HealthLake's integrated natural language processing feature?
As of February 20, 2023, the default behavior of HealthLake data stores changed.
Current data stores: All current HealthLake data stores will stop using natural language processing (NLP) on base64-encoded DocumentReference resources. This means that new DocumentReference resources will not be analyzed using NLP, and no new resources will be generated based off of text in the DocumentReference resource type. For existing DocumentReference resources, the data and resources generated via NLP remain, but they will not be updated after February 20, 2023.
New data stores: HealthLake data stores created after February 20, 2023 will not perform natural language processing (NLP) on base64-encoded DocumentReference resources.
To turn on this feature you must create a case using AWS Support Center Console. To create your case, log in to your AWS account, and then choose Create case. To learn more about creating a case and case management, see Creating support cases and case management in the AWS Support User Guide.
HealthLake Use Cases
HealthLake assists healthcare companies in the analysis of population health trends, outcomes, and costs through population health management. This aids organizations in selecting the best care management solutions and determining which action is most suitable for a certain patient population.
Enhancing care quality by gathering a comprehensive picture of a patient's medical history, HealthLake helps healthcare facilities, health insurance providers, and life sciences businesses eliminate gaps in care, enhance care quality, and cut costs.
Enhancing hospital productivity with essential analytics and machine learning resources to boost productivity and cut down on waste.
Transform, analyze, and discover insights from unstructured healthcare data using Amazon HealthLake
The workflow steps are as follows:
- A document is uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.
- The document upload in Amazon S3 triggers an AWS Lambda function.
- The Lambda function sends the image to Amazon Textract.
- Amazon Textract extracts text from the image and stores the output in a separate Amazon Textract output S3 bucket.
- The final result is stored as specific FHIR resources (the extracted text is loaded in
DocumentReferenceas base64 encoded text) in Amazon HealthLake to extract meaning from the unstructured data with integrated Amazon Comprehend Medical for easy search and querying. - Users can create meaningful analyses and run interactive analytics using Amazon Athena.
- Users can build visualizations, perform ad hoc analysis, and quickly get business insights using Amazon QuickSight.
- Users can make predictions with health data using Amazon SageMaker ML models.
For more details please review this article
Guidance for Ingesting PDF and Image Files to AWS HealthLake
The workflow steps are as follows:
Step 1 Users upload a PDF file or an image file, ending with .tiff, .jpg, or .png.
Step 2 Creating a new file in Amazon Simple Storage Service (Amazon S3) invokes an AWS Lambda function.
Step 3 The Lambda function calls Amazon Textract APIs to parse the data.
Step 4 The Lambda function inserts the key-value pairs into AWS HealthLake.
Step 5 HealthLake transforms customer data into an analytics-ready format for AWS Lake Formation in near real-time to provide enhanced analytics capabilities. Lake Formation is built on AWS Glue. HealthLake automatically provides you with integrated natural language processing (NLP) using Amazon Comprehend Medical.
Step 6 Amazon Athena queries the HealthLake data.
Step 7 An S3 bucket stores the HealthLake data.
Step 8 Users can view the HealthLake data in a dashboard using Amazon QuickSight.
Step 9 Users can build, train, and deploy machine learning (ML) models with Amazon SageMaker using HealthLake data.
Step 10 HealthLake integrates with third-party tools.
For more details please review this article
If the data is not in FHIR format these connectors can help to convert the data into FHIR format
https://us-east-1.console.aws.amazon.com/healthlake/home?region=us-east-1#/marketplaces
A step-by-step process to create a Data Store in AWS HealthLake
Configuring a new IAM role to use HealthLake (IAM Administrator)
{ "Version": "2012–10–17", "Statement": [ { "Effect": "Allow", "Action": [ "s3:PutObject", "s3:GetObject" ], "Resource": "arn:aws:s3:::my-bucket/*" }, { "Effect": "Allow", "Action": [ "ram:GetResourceShareInvitations", "ram:AcceptResourceShareInvitation", "glue:CreateDatabase", "glue:DeleteDatabase" ], "Resource": "*" } ] }
Creating a HealthLake data store
Read the Patient data
Search the Patient data with GET operation
Patient data details
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Summary
For the ingestion, archiving, and analysis of clinical data using the Healthcare Interoperability FHIR (R4) specification (FHIR-Fast Healthcare Interoperability Resources), AWS HealthLake is a HIPAA-eligible service.
For more details please visit