Connect with us

Amazon

Announcing the InterSystems HealthShare Message Transformation Service for Amazon HealthLake

Amazon HealthLake is a new HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. Amazon HealthLake removes the heavy lifting of organizing, indexing, and structuring patient information to provide a complete view of the health of individual patients and entire patient populations in a secure, compliant, and auditable manner. With the…

Published

on

Amazon HealthLake is a new HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. Amazon HealthLake removes the heavy lifting of organizing, indexing, and structuring patient information to provide a complete view of the health of individual patients and entire patient populations in a secure, compliant, and auditable manner. With the Amazon HealthLake APIs, organizations can easily store health data already in the Fast Healthcare Interoperability Resources (HL7 FHIR) industry standard to a secure data lake in the cloud.

For those organizations who don’t have their data in FHIR format, Amazon has partnered with industry leaders in healthcare interoperability to develop connectors, which help you with data transformations. This post highlights one of those organizations, InterSystems, and their solution validated for use with Amazon HealthLake, called the HealthShare Message Transformation Service.

About InterSystems

Established in 1978, InterSystems provides innovative data solutions for organizations with critical information needs in the healthcare, finance, and logistics sectors, and beyond. Their cloud-first data platforms solve interoperability, speed, and scalability problems for organizations around the globe. InterSystems was awarded the 2020 and 2021 KLAS Category Leader for Interoperability Platforms and was named a Visionary in the first Gartner Magic Quadrant for Cloud Database Management Systems (DBMS).

HealthShare Message Transformation Service

The HealthShare Message Transformation Service is a software as a service (SaaS)-based cloud service that provides a fully automated single interface method for healthcare message transformation into HL7 FHIR. The service enables automated conversion of existing data standards starting with HL7v2 to FHIR for use in Amazon HealthLake.

How it works

The InterSystems HealthShare Message Transformation Service enables you to upload an HL7v2 or InterSystems defined CSV formatted file into an Amazon Simple Storage Service (Amazon S3) bucket of your choosing. The service configures secure access to the S3 bucket and your Amazon HealthLake data store (in the your AWS account). After it’s configured, you can write HL7v2 messages or CSV files into your S3 bucket, and InterSystems processes the file, transforms it to FHIR R4, and populates Amazon HealthLake within seconds.

To better illustrate this, let’s look at an example HL7v2 MDM message for a clinical note put into an S3 bucket with a .txt extension:

MSH|^~&||HC6|||||MDM^T02|||2.5 EVN|T02|20210104094500 PID|||LD572046^^^HC6^MR||Smith^John||19301019|M|||1 Memorial Drive^^Cambridge^MA^02142||||||||063070516 PV1||O|||||ISCGP001^Moore^James|||||||EO|||||HSVN00006|||||||||||||||||||||||||20210104094500|20210104094500 TXA||Progress note||202101040945|JJ021^James^John||||ISCGP001^Moore^James|||19815952^TRANS OBX||FT|RTF^TRANS||Patient complaining of pain in right big toe. Toe red in color. To commence on antibiotics. ||||||R

The HealthShare Message Transformation Service from InterSystems converts the message into a properly formatted FHIR R4 DocumentReference and writes this to Amazon HealthLake, where Amazon HealthLake automatically applies natural language processing to the clinical note. The preceding HL7 message results in Amazon HealthLake suggesting an ICD-10 code of M79.674 (pain in right toes), condition of pain, as well as organ site of “big toe” and generic antibiotics for medication.

The following diagram illustrates the overall architecture of the HealthShare Message Transformation Service.

 

CureMatch to use the HealthShare Message Transformation Service with Amazon HealthLake

CureMatch™, Inc. is a San Diego-based digital health company focused on personalized medicine and combination therapy in oncology. CureMatch’s Decision Support System guides oncologists in the selection of cancer drugs that are customized for individual patients based on their molecular tumor profile. This provides them with actionable intelligence towards advanced cancer treatment options.

CureMatch plans to use these two solutions to improve efficiency and scale faster by automating data transformations and using the features within Amazon HealthLake to move to an even more AI-empowered and personalized approach.

“With the use of the HealthShare Message Transformation Service by InterSystems and Amazon HealthLake, we will be able to access and transform molecular profile data from EHR into FHIR to run advanced analytics and algorithms, providing clinical decision support and guidance on treatments to ultimately improve patient outcomes,” says Philippe Faurie, Vice President of Professional Services at CureMatch.

Conclusion

Amazon HealthLake transforms unstructured data using specialized machine learning models, like natural language processing, to automatically extract meaningful medical information from data, and provides powerful query and search capabilities. InterSystems, with their ability to quickly ingest and convert HL7v2 messages into FHIR and populate Amazon HealthLake, provides a quick and easy way to use Amazon HealthLake to unlock the full potential of your healthcare data.

Amazon HealthLake with the HealthShare Message Transformation Service from InterSystems can help extract meaningful information from raw, disparate, unstructured data across your organization. To learn more, visit the InterSystems Amazon Marketplace listing or the InterSystems product page.

About the Authors

Todd Sylvester is Director of Cloud Strategy at InterSystems.

 

Brian Warwick is a Solutions Architect supporting global AWS Partners who build healthcare solutions on AWS. Brian is passionate about helping customers leverage the latest in technology in order to transform the healthcare industry.

Source

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Amazon

Build ultra-low latency multimodal generative AI applications using sticky session routing in Amazon SageMaker

In this post, we explained how the new sticky routing feature in Amazon SageMaker allows you to achieve ultra-low latency and enhance your end-user experience when serving multi-modal models. Source

Published

on

By

In this post, we explained how the new sticky routing feature in Amazon SageMaker allows you to achieve ultra-low latency and enhance your end-user experience when serving multi-modal models.

Source

Continue Reading

Amazon

Streamline workflow orchestration of a system of enterprise APIs using chaining with Amazon Bedrock Agents

In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision. Source

Published

on

By

In this post, we explore how chaining domain-specific agents using Amazon Bedrock Agents can transform a system of complex API interactions into streamlined, adaptive workflows, empowering your business to operate with agility and precision.

Source

Continue Reading

Amazon

Unlock AWS Cost and Usage insights with generative AI powered by Amazon Bedrock

In this post, we explore a solution that uses generative artificial intelligence (AI) to generate a SQL query from a user’s question in natural language. This solution can simplify the process of querying CUR data stored in an Amazon Athena database using SQL query generation, running the query on Athena, and representing it on a…

Published

on

By

In this post, we explore a solution that uses generative artificial intelligence (AI) to generate a SQL query from a user’s question in natural language. This solution can simplify the process of querying CUR data stored in an Amazon Athena database using SQL query generation, running the query on Athena, and representing it on a web portal for ease of understanding.

Source

Continue Reading

Trending

Copyright © 2021 Today's Digital.