Effectively Integrating FHIR Data from Azure Health Services

This blog is intended to be a follow up from the SQL Saturday 2022 in Oregon & SW Washington. In that session I presented an introduction to FHIR and JSON data produced from the Azure Health Services API’s.

With the recent updated mandates in the healthcare environment in the United States, Microsoft has continued to expand its capability to support the FHIR standard for integrating healthcare data. While the standard is well documented and Microsoft’s capabilities are expansive, it falls on data professionals to interpret that data and build meaningful reports and produce meaningful insights from the data as it is collected and integrated across environments. This requires a good working knowledge of JSON in SQL to manipulate complex data models. In the session, we did a short review of the FHIR standard and the overall implementation of FHIR in Azure. From there we reviewed the resulting data in the data lake and in Synapse. That was followed up with an overview into the heart of complex SQL using JSON functions in Synapse. Whether or not you are active in healthcare today, this will be an enlightening session on how to use JSON SQL functions within the Azure SQL platforms.

What is FHIR and why should you care?

FHIR stands for Fast Healthcare Interoperability Resources. this is the latest specification for interoperability in healthcare produced by HL7. To be clear the word fast has nothing to do with performance, but more about the ability to implement and integrate data quickly. With the latest regulations around the world in health care, this standard is the established standard for integrating healthcare data and we’ll continue to be on the forefront of this work. If you do any work in health care, you will need to understand FHIR because you will likely run across data formatted to the standard from many different sources.

FHIR is very well documented. In many ways when the standard is properly followed the JSON documents or other supported formats are effectively self-documenting. It is commonly understood that the core FHIR specification handles about 80% of the use cases in healthcare. It is designed to be flexible so that it can support specialized needs within regions or healthcare areas. For example, in the US there is a need to support race and ethnicity. The U.S. Core Implementation Guide provides guidance on the specification enhancements to support this need for U.S. healthcare organizations. You will find similar support for other countries as well as specific implementations for healthcare vendors such as Epic.

Neither the notebook, the presentation, or this blog is expected to be and exhaustive coverage of FHIR. before we move on to some of the other implementation pieces, it is important to understand one key aspect of FHIR is the basic building block called a resource. A resource is the core exchangeable content within the specification. All resources share the following characteristics:

  • A common way to define and represent the resource including data types and patterns
  • A common set of metadata which can be discovered easily
  • A human readable part

For more detailed information on the supported resources and other details around FHIR implementation, you should visit the following website:

Azure Health Services and the FHIR API

I will not be digging into a lot of the health care services information nor the FHIR support within Azure in this post. The important things to understand is that Microsoft has made a concerted effort to support this specification which includes technology and architectures for the extraction of data from various healthcare systems which will then use the FHIR APIs to standardize that extracted data into the FHIR spec typically in JSON files in the data lake. Because of the standardized format, Microsoft is able to supply a set of common schemas that can be used in serverless synapse to create external tables and views to accelerate the implementation and usage of data produced from the APIs. It is from this starting point that we are able to start working with the data in reporting and analytics solutions.

At this point I want to put a plug in for the company I work for. If you're interested in learning how Azure health services and the FHIR specification can be implemented at your company, we have FHIR Quick Start and FHIR Data Blueprint solutions. These solutions have been used by many other customers to achieve high levels of integration in their health care data estate. If you're interested in learning more, please reach out to us at: https://3cloudsolutions.com/get-started/

Working with the data from the FHIR API using JSON in SQL

As noted in the previous section, Azure Health Services comes with setup serverless tables and views to be used with the extracted data. However due to the complexity of FHIR, there are a number of columns within those tables and views which still contain JSON snippets. For example, there is one field for name which has several objects and arrays to support the specification. You cannot simply select the name from the table and use that as you move forward. There are many different fields like this throughout the data. For the rest of this blog and in the notebook, we will work through a number of scenarios to build a view of the patient resource that can be used for simple reporting. This view will contain a few JSON functions from SQL Server and solve simple to complex scenarios in the illustration.

The functions we will be using:

  • ISJSON
  • JSON_VALUE
  • OPENJSON

In addition to these functions, we will also be using the CROSS APPLY operator in SQL to join our data with relational data.

The examples in the notebook are built on the tables resulting from working with the Azure FHIR API. I am unable to provide a sample of the data to use with the set of information in the notebook currently. However, the SQL will work if you have your own FHIR implementation and a Patient resource to work with. rather than rewrite the entire contents of the notebook in the blog post, here is a link to the notebook.

If you plan to implement this in the same way, you will need Azure Data Lake, Azure Synapse serverless, and Azure Data Studio. the notebook can be opened in Azure Data Studio. If you are unfamiliar with working with notebooks inside of Azure Data Studio, you are not alone. Check out this post which discusses how to implement your first notebook in Azure Data Studio.

Building our view and SQL with JSON functions

If you decide not to open the notebook but are curious what the view looks like here is a finished product that we created in the notebook.

SELECT TOP (20) p.resourceType + '/' +  p.id as PatientResourceID
    , p.resourceType as ResourceType
    , p.id as ResourceID 
    , cast(p.[meta.versionId] as int) as VersionID 
    , cast(p.[meta.lastUpdated] as DATETIME2(7)) as LastUpdated 
    , JSON_VALUE(p.[name], '$[0].family') as LastName
    , JSON_VALUE(p.[name], '$[0].given[0]') as FirstName
    , cast(p.active as bit) as IsActive
    , p.gender as Gender 
    , CAST(p.birthDate as date) as BirthDate
    , CASE WHEN p.[maritalStatus.coding] is null THEN NULL
           WHEN  JSON_VALUE(p.[maritalStatus.coding], '$[0].system') = 'http://terminology.hl7.org/CodeSystem/v3-MaritalStatus' 
                    THEN JSON_VALUE(p.[maritalStatus.coding], '$[0].code')
           ELSE NULL
           END as MaritalStatus 
    , CASE WHEN JSON_VALUE(p.[address], '$[0].use') = 'home' THEN JSON_VALUE(p.[address], '$[0].state')
            WHEN JSON_VALUE(p.[address], '$[1].use') = 'home' THEN JSON_VALUE(p.[address], '$[1].state')
            WHEN JSON_VALUE(p.[address], '$[2].use') = 'home' THEN JSON_VALUE(p.[address], '$[2].state')
            WHEN JSON_VALUE(p.[address], '$[3].use') = 'home' THEN JSON_VALUE(p.[address], '$[3].state')
            ELSE NULL
            END as HomeStateOrProvince
    , e.Ethnicity
    , r.Race
FROM fhir.Patient p
INNER JOIN (SELECT id, max([meta.versionId]) as currentVersion FROM fhir.Patient GROUP BY id) cp
    ON p.[meta.versionId] = cp.currentVersion
    AND p.id = cp.id
LEFT JOIN 
    (SELECT p.id
        , CASE WHEN JSON_VALUE(ext.value,'$.extension[0].url') = 'ombCategory'
            THEN
            CASE WHEN JSON_VALUE(ext.value, '$.extension[1].valueString') IS NOT NULL  THEN JSON_VALUE(ext.value, '$.extension[1].valueString')
                    WHEN JSON_VALUE(ext.value, '$.extension[0].valueString') IS NOT    NULL THEN JSON_VALUE(ext.value, '$.extension[0].valueString')
                    ELSE JSON_VALUE(ext.value, '$.extension[0].valueCoding.display')
                    END
            ELSE JSON_VALUE(ext.value, '$.valueCodeableConcept.coding[0].display')
            END AS Ethnicity 
        FROM 
        (
            SELECT fp.id, fp.extension FROM fhir.Patient fp
            INNER JOIN (SELECT id, max([meta.versionId]) as currentVersion FROM fhir.Patient GROUP BY id) cp
                ON fp.[meta.versionId] = cp.currentVersion
                AND fp.id = cp.id
            WHERE ISJSON(fp.extension) =1
        ) p 
        CROSS APPLY 
            OPENJSON(p.extension,'$'
            ) as ext
        WHERE JSON_VALUE(ext.value,'$.url') = 'http://hl7.org/fhir/us/core/StructureDefinition/us-core-ethnicity'
    ) e on e.id = p.id 
LEFT JOIN 
    (SELECT p.id
        , CASE WHEN JSON_VALUE(ext.value,'$.extension[0].url') = 'ombCategory'
            THEN
            CASE WHEN JSON_VALUE(ext.value, '$.extension[3].valueString') IS NOT NULL THEN JSON_VALUE(ext.value, '$.extension[3].valueString')
                    WHEN JSON_VALUE(ext.value, '$.extension[2].valueString') IS NOT NULL THEN JSON_VALUE(ext.value, '$.extension[2].valueString')
                    WHEN JSON_VALUE(ext.value, '$.extension[1].valueString') IS NOT NULL THEN JSON_VALUE(ext.value, '$.extension[1].valueString')
                    WHEN JSON_VALUE(ext.value, '$.extension[0].valueString') IS NOT NULL THEN JSON_VALUE(ext.value, '$.extension[0].valueString')
                    ELSE JSON_VALUE(ext.value, '$.extension[0].valueCoding.display')
                    END
            ELSE JSON_VALUE(ext.value, '$.valueCodeableConcept.coding[0].display')
            END AS Race 
        FROM 
        (
            SELECT fp.id, fp.extension FROM fhir.Patient fp
            INNER JOIN (SELECT id, max([meta.versionId]) as currentVersion FROM fhir.Patient GROUP BY id) cp
                ON fp.[meta.versionId] = cp.currentVersion
                AND fp.id = cp.id
            WHERE ISJSON(fp.extension) =1
        ) p 
        CROSS APPLY 
            OPENJSON(p.extension,'$'
            ) as ext
        WHERE JSON_VALUE(ext.value,'$.url') = 'http://hl7.org/fhir/us/core/StructureDefinition/us-core-race'
    ) as r on r.id = p.id 

Here is a sample of the results from that view:

PatientResourceIDResourceTypeResourceIDVersionIDLastUpdatedLastNameFirstNameIsActiveGenderBirthDateMaritalStatusHomeStateOrProvinceEthnicityRace
Patient/d8af7bfa-5008-4a0f-85d1-0af3448a31ddPatientd8af7bfa-5008-4a0f-85d1-0af3448a31dd22022-05-31 18:07:03.2150000DUCKDONALD1male1965-07-14NULLONNULLNULL
Patient/78cf7725-a0e1-44a4-94d4-055482781afbPatient78cf7725-a0e1-44a4-94d4-055482781afb12022-05-31 18:07:30.7490000GretzkyWayneNULLNULL1990-05-31NULLNULLNULLNULL
Patient/9e909e52-61a1-be50-1878-a12ef8c36346Patient9e909e52-61a1-be50-1878-a12ef8c3634642022-05-31 18:39:58.1780000EVERYMANADAMNULLmale1988-08-18MNULLNon Hispanic or LatinoWhite+Asian
Patient/585f3cc0-c727-4989-9214-a7a7b60a2adePatient585f3cc0-c727-4989-9214-a7a7b60a2ade12022-05-31 13:14:57.0640000DUCKDONALD1male1965-07-15NULLONNULLNULL
Patient/29a819c4-f553-8189-2354-9441b86d37efPatient29a819c4-f553-8189-2354-9441b86d37ef12022-05-18 15:18:40.1560000FORDELAINENULLfemale1992-03-10NULLNULLNULLNULL
Patient/d5fe6802-a680-e762-8f43-9659340b00acPatientd5fe6802-a680-e762-8f43-9659340b00ac32022-05-18 14:39:52.2550000EVERYMANADAMNULLmale1961-06-15SNULLNULLC
Patient/4d661053-a8d0-148c-7023-54508fd04a52Patient4d661053-a8d0-148c-7023-54508fd04a5212022-05-21 13:48:24.9720000EVERYMANsamNULLmale1966-05-07MNULLNot Hispanic or LatinoWhite

Wrapping it up

As you can see, understanding the specification well enough to build a complex SQL statement using JSON functions is required to work within FHIR effectively. Due to the complex nature of the nested JSON, you may not be able to reconcile this in tools such as power BI. Being able to build this out in SQL guarantees that you have provided you will report writers and analysts with a solid result set which can be used with confidence.

Resources summary:

Moving Synapse Databases Between Subscriptions – Practical Guidance

One of the tasks, we often do with migration projects is move large volumes of data. Depending on how you are configured, you may need to do the migration project in a development or UAT environment as opposed to a production environment. This is particularly true if you have policies in place on your production subscription that don’t allow the individuals doing the migration and validation tasks to work in that subscription.

Just Copy It… Nope

So you can copy Azure SQL Database using the Azure Portal, PowerShell, Azure CLI, and T-SQL. However, this functionality is limited to Azure SQL Database and does not work for Azure Synapse databases (a.k.a. SQL Pools). Early in 2021, the ability to use the copy functionality to copy databases between subscriptions is also supported but requires security work to make sure the permissions in the database servers and networking allow that to happen.

You can find out more about copying Azure SQL Database in this Microsoft Doc.

Just Restore It… Nope

You can restore to the current server or another server on the same subscription. However, you are unable to restore across subscription boundaries at this time. If you need to move to another server in the current subscription, the process is straightforward, you can use the restore process in Synapse to restore to the current server using a different name. You can also restore to a different server in either the current or different resource group in the same subscription. The restore technique is used in our move process, so details on how to restore a Synapse database will be in the next section.

Let’s Move a Synapse Database

The process to move a Synapse database to another subscription requires some planning and pre-work. The first thing you need to do is create a new SQL Server in the same subscription you have your current Synapse environment. Because you can’t simply create servers, I would recommend that you add an Azure SQL Database to the server as a placeholder. An S0 should be sufficient to keep this server in place for what we are doing. DO NOT ADD anything to this server that will not be migrated. This is a temporary holding place for migrating databases. (This also works for other SQL Databases, but other options may work as well but are not the focus of this post.)

Now that you have the migration server created, the next step is to create a restore point. While this is not required because you can use the automatic restore points, creating a user-defined restore point is recommended. A user-defined restore point, allows you to choose the status of the database you want to migrate, rather than relying on the automatic points and trying to make sure you pick the right time (in UTC of course).

Once you have set the restore point, in the database you want to migrate, select Restore to open the panel to restore your database.

On the restore page, you have a number of options to complete.

  • Restore point type: Choose User-defined restore points
  • SQL pool name: This is not a big deal. The name is the database name used during the migration process and is not the final name used in the target server. Make sure it is something you know.
  • Restore point: Select the restore point you created for this purpose.
  • Server: Choose the migration server you created as the target.
  • Performance level: This one is more interesting. I typically choose a smaller performance level for this restore. Keep in mind that Azure needs to allocate resources to support the restore. Because this is not a final deployment, smaller may go faster. However, NO SLAs exist for this process. That means your mileage will vary. We have seen restores happen in 30 minutes one day and over 5 hours the next. It will be very dependent on the data center and how busy it is. This time variation must be accounted for in your planning.

The next step is to move the server using the Move operation on the server page. You have the option to move to another resource group or another subscription. In our case, we will choose another subscription. IMPORTANT: You will need Contributor permissions in the target subscription in order to move the server to that subscription.

After you have moved the server to the target subscription, you need to set a restore point for that database on the migration server. Then you can restore that database to the target server. It is very important that you use the naming convention and performance levels that you need for this restore as it is the final step in the process. Once again the restore process has no Microsoft SLA and as a result may take longer than planned. You need to have contingencies in place if you are working in a deployment window or have time restrictions.

Finally, you need to clean up the migration server. I would recommend either scaling down or pausing the Synapse database to give you a backup for a while if needed. Once the database is validated on the target server, you can remove the Synapse database (removes storage costs). I would recommend keeping this server as your migration server to use in the future. You can use this process to create copies of databases for development and UAT or similar needs from production instances.

Other Thoughts and Considerations

Here are my final thoughts on this process. First, the fact that no SLA on the restore process is provided by Microsoft has created issues for us in some cases. We have had to extend deployment windows during production deployments on occasion. My recommendation is to plan for the worst case and finish early if all comes together on time.

This process works! You can use it with other SQL assets and you can use it in multiple directions. Keep the migration server around so you can support other processes. If you clean most of it out, the cost of maintaining it is the S0 SQL Database.

One final thought, this is Azure. Thus, this guidance could change tomorrow. We have been using this for about 12 months when this was written. I hope this helps some of you move these databases to support your business and development needs.