AWS Marketplace

Using HiPaaS to convert your data to FHIR to use with AWS HealthLake

Healthcare customers (providers and payors) must efficiently interoperate healthcare data to provide the right access to the data at right time to provide patients with best care. However, they face challenges on their journey due to the fragmented interoperability landscape. There are several existing standards and formats, including ANSI X12 Electronic Data Interchange (EDI), Health Level Seven (HL7) v2, and Consolidated Clinical Document Architecture (C-CDA), to name a few. These can be complex and require specialized knowledge to process. The increasing need for payers and providers to conform to Centers for Medicare and Medicaid Services (CMS) mandates is making health care data more interoperable. Interoperability is presenting a challenge due to the fragmented landscape and disparate data formats.

The HL7 Fast Healthcare Interoperability Resources (FHIR) standard has gained popularity as a specification for healthcare data after its introduction in 2012. With the recent Centers for Medicare and Medicaid Services (CMS) Interoperability mandate, FHIR became the standard. According to HL7, 95% of U.S. healthcare organizations and more than 35 countries still use the HL7v2 version of the messaging standard. Healthcare organizations are looking for help to convert their existing data to FHIR to meet the CMS mandate.

In this blog post, Sandeep, Bakha, Aparna, and I will show how to convert your healthcare data to FHIR format and load it into AWS HealthLake using HiPaaS FHIR data converter solution. This solution enables you to access and manage the data from various sources without the need for manual data entry or reconciliation.

About HiPaaS

HiPaaS is an AWS Marketplace Seller that builds cloud-based and healthcare-specific software solutions. HiPaaS AI-enabled payment reconciliation, revenue integrity, interoperability, and Clinical Decision Support (CDS) enable you to integrate your healthcare data and derive faster insights.

About AWS HealthLake

AWS HealthLake is a HIPAA-eligible service that provides FHIR APIs that help healthcare and life sciences companies securely store, transform, transact, and analyze health data in minutes to give a chronological view at the patient and population level.

AWS HealthLake uses the FHIR healthcare data format so that clinical data is aligned to a common data model, eliminating guesswork for downstream systems. This enables you to easily access and analyze the data using machine learning (ML) algorithms, without the need for manual data cleaning and preparation.

Prerequisites

To follow this tutorial, you will need:

  1. Access details around customer’s S3 bucket
  2. Access details for customer’s AWS HealthLake instance
  3. Select and download the sample patients csv file. (This is sample data generated from source)

Solution overview

The HiPaaS FHIR Data Converter accelerates data loading into AWS HealthLake. It provides conversions of healthcare data sources in different formats such as HL7v2, X12, C-CDA and custom comma separated values (CSV) into standard FHIR format. The data converter is built on the AWS serverless architecture to capitalize on speed and ease of autoscaling.

The following architecture diagram depicts the data conversion into FHIR format as it flows through the HiPaaS solution.

  1. When the file is placed in an Amazon S3 bucket, S3 event notification is triggered to the Input Data Loader service.
  2. The Input Data Loader Service internally checks the Folder Configuration information for the type of file load and based on the source file type. It then loads the file into the backend Amazon Aurora database. After the file is loaded into the database, the CSV Mapper service is invoked.
  3. The CSV Mapper service maps the inbound CSV to internal HiPaaS flat file. Once the CSV Mapper service is triggered, it first validates the data, which was loaded in the Aurora database. It then triggers the flat file mapping. After successful mapping, the FHIR mapper service is triggered.
  4. The FHIR Mapper Service helps in executing the map to convert flat file data to the required FHIR object. During the conversion, the mapper validates the data, checks the required dependencies and references, and completes the required lookups for FHIR. Once the FHIR Mapper service converts the flat file into FHIR object, it is ready to be loaded into AWS HealthLake data store.
  5. FHIR objects are loaded into AWS HealthLake data store for further healthcare data analytics. Refer to the following diagram.

HiPaaS FHIR data coverter diagram with AWS HealthLake

Solution walkthrough: Using HiPaaS to convert your data to FHIR to use with AWS HealthLake

For the purpose of this tutorial, we will show how to convert a CSV file to FHIR, which you can then load into AWS HealthLake. You may use the same process as a template for conversion of HL7v2, X12, C-CDA files.

Step 1: Subscribe to HiPaaS in AWS Marketplace

  1. Sign into AWS Marketplace using the Sign In link on top right of the page.
  2. In the top middle of AWS Marketplace, in the search bar, enter Custom CSV FHIR Data Converter for AWS HealthLake on SaaS provided by HiPaaS Inc. To go to the product page, choose the Custom CSV FHIR Data Converter for AWS HealthLake on SaaS product page link that comes up first.
  3. On the Custom CSV FHIR Data Converter for AWS HealthLake on SaaS product page top right, choose View Product Details. Review the details, and then choose Subscribe.
  4. On the contact detail page, enter your company name, name, phone, and email, and choose Register.
  5. You will receive an email within two business days from support@hipaas.com. The email contains your login URL, username, and temporary password to login to HiPaaS. If you don’t find the email, check your spam folder.
  6. After your first log in to HiPaaS, a prompt will appear to change your password and create a customer account. Enter a new password. Also, as part of your account creation, a prompt will appear to add AWS HealthLake and S3 bucket details. Enter your S3 bucket URL, AWS Access Key, AWS Secret Key, and the AWS Region your bucket is in. You can change these details at any time.

Step 2: Start using HiPaaS Converter by creating a CSV definition

  1. In the HiPaaS left menu, navigate to the CSV Definition screen by choosing Admin and then CSV Definition.
  2. On the center of the CSV Definition screen, select Choose File. Browse for the CSV file you downloaded in the Prerequisites step 3.
  3. Based on the location where your sample file is located, on the upper right of the popup window, choose the file by highlighting or selecting it. Choose Open.
  4. In the textbox on the top middle of the screen, enter the following:
    1. To save to the database, enter a Table Name.
    2. In the textboxes on top of the screen, enter Separator details.
    3. To create the extract tables in the HiPaaS Database, to the right of the Choose File button, choose Upload.

Step 3: Create mapping to existing HiPaaS flat file definition object

Now that you’ve created the CSV structure, you can map the flat file object to the inbuilt HiPaaS that in turn converts it to FHIR. To do that, follow these steps:

  1. To access the CSV Mapper screen, on the left navigation, choose Developer and then CSV Mapper.
  2. To add a new map, on the HiPaaS Mapper screen top right, choose the Add New button. A popup window opens.
  3. Enter the following details:
    1. FHIR object: patient
    2. Version: 2.0.0
    3. Profile: C4BB
    4. Map Name: stage_patient_flatfile_sample
    5. Source Object Name: extract_patient_sample
    6. Destination Object: patient_flatfile (this would autopopulate based on the selected FHIR object)
  1. In the top left of the popup, choose Create Map.
  2. Once the new map is created, the CSV fields show up in the Mapping Field column of the table. Map the required fields using the dropdown option on the Destination Field textboxes. To save the new mapping, on the left bottom of the popup window, choose Save.
  3. If any row is missed out while creating the mapping, or optionally if you want to edit the existing mapping, reach out to support@hipaas.com for additional guidance.

Step 4: Create the required folder in your S3 bucket

  1. Log in to your AWS account. In the search field on top of the page, enter S3.
  2. From the results under Services, choose S3 and choose the bucket where you want to upload it. For example, my bucket name is hipaas-files.
  3. Within your sample bucket, on the top right of the page, choose the Create Folder button. Enter the folder name as Sample and create it by choosing Save.

Step 5: Configure the created mapping to S3 input folders

  1. To access Folder configuration, on the left navigation, choose Admin and then Folder Config.
  2. To configure the created mapping to S3 input folder, choose Add New. A popup window will open.
  3. Enter the same details as in Step 3.3. To complete the configuration, add the S3 folder name and the FHIR Object MapName.

Step 6: Lookup and code definition changes

All common codes for standard objects such as patient, encounter, and condition are already configured in the system. There are over 50 out-of-the-box code definition and lookup tables provided for you to update as required. For additional information on adding new definition and code, reach out to support@hipaas.com.

Step 7: Drop the file in your S3 bucket and view the FHIR object in AWS HealthLake

  1. Using the same screen as on Step 4, choose the hipaas-files sample folder and choose Upload and then Add files. Select the file from your directory and choose Upload.
  2. To monitor the load status, on the left navigation, choose Admin and then Load Status.
  3. To see the FHIR object loaded, query the results in AWS HealthLake. To do that, log in to your AWS account and at the top of the page, enter AWS HealthLake in the search field.
  4. Choose View Data Stores and select the data store where you loaded the data.
  5. Since you used your S3 bucket for the source file, in the results under View Data Stores, choose S3. Select the data store where you loaded the data.
  6. Choose the Run Query option, and on the screen that opens up, in Query type select Search with GET, and in Resource type select Patient. In Search parameter, choose identifier and enter the patient member number from the sample CSV file to the Value-optional field on right center of screen. This search criteria can also be based on identifiers like medical record number (MRN), or any unique identification number for the patient.
  7. At the bottom right, choose Run query. The result shows the FHIR object loaded successfully in AWS HealthLake.

Start converting and loading CSV files to AWS HealthLake

You have now completed the initial testing of uploading the sample CSV file, converting it to FHIR format, and querying from AWS HealthLake. You can follow the same approach now for conversion of other healthcare data formats to FHIR objects in AWS HealthLake. For further clarifications on any of the optional steps, contact support@hipaas.com.

Conclusion

In this post, Sandeep, Bakha, Aparna, and I showed you how to use HiPaaS FHIR Data Converter to convert a CSV file into a common FHIR format. You can also use this tutorial to convert other data types, including HL7v2, X12 (EDI Message Standard), and C-CDA into FHIR format. For more information on other HiPaaS products, visit the HiPaaS homepage.

The components required for CMS Interoperability rules including Patient Access API, Provider Directory, Prior Authorization, Payer-to-Payer APIs are available out-of-the-box as a separate AWS Marketplace offering from HiPaaS. The CMS Interop solution is built using the HiPaaS FHIR Data Converter and AWS HealthLake.

About the authors

Aparna GeorgeAparna George is a Product Manager with HiPaaS Inc. She has been with HiPaaS since 2018 and led multiple customer implementations.

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Sandeep DeokuleSandeep Deokule is the Founder and CEO of HiPaaS Inc. Sandeep has extensive experience in the healthcare industry and was awarded one the most successful healthcare startup CEOs in 2021.

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Malini Chaterjee
Malini Chatterjee is a Senior Solutions Architect at AWS. She provides guidance to AWS customers on their workloads across a variety of AWS technologies. She brings a breadth of expertise in Data Analytics and Machine Learning.

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Bakha Nurzhanov
Bakha Nurzhanov is an Interoperability Solutions Architect at AWS, and is a technical leader for healthcare interoperability and innovation. Bakha supports global healthcare customers, builds healthcare interoperability depth in our global technical team, and leads healthcare innovation development. Prior to joining AWS, Bakha spent over 20 years working as an integration architect and developer at various healthcare provider organizations.