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iris-fhirsqlbuilder

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Showcase of the FHIR SQL Builder from IRIS

What's new in this version

Initial Release

iris-fhirsqlbuilder

This project shows how to use the FHIR SQL Builder to generate SQL statements for the FHIR Server from IRIS.

The end result will be available on Tableau.

Tableau

Prerequisites

  • IRIS Community Edition
  • Git
  • Docker
  • Tableau Desktop

Installation

Git clone this repository.

git clone https://github.com/grongierisc/iris-fhirsqlbuilder.git

Run the following command to start IRIS.

docker-compose up -d

FHIR SQL Builder

For your convenience, this project contains preloaded data in the FHIR Server.

Preloaded data is available in the following folder.

data/fhir

Configure an FHIR Repository to analyze

Once IRIS is up and running, go to the FHIR SQL Builder and generate the SQL statements for the FHIR Server.

http://localhost:33783/csp/fhirsql/index.html

or

http://localhost:33783/csp/fhirsql/index.csp (if you are running IRIS for health 2023.1 or lower)

Login / password : SuperUser / SYS

FHIRSQLIntro

Then register the current FHIR Server with the FHIR SQL Builder.

To do so, click on New Analysis.

FHIRSQLNewAnalysis

Then click on Register FHIR Server.

FHIRSQLRegisterFHIRServer

Fully the form with the following values:

  • FHIR Server Name: FHIRSERVER
  • Host: localhost
  • Port: 52773
  • Credentials: SuperUser / SYS
    • Click on New Credentials to create a new user with the right credentials.
  • FHIR repository url : /fhir/r4

Then click on Save.

Now we can generate an analysis.

  • Selectivity : 100

FHIRSQLAnalysis

Then click on Lunch Analysis.

Create projections

Once the analysis is done, we can create projections.

FHIRSQLProjections

To help you in this task, you can import the following projections.

misc/ExportFHIRtoSQL.json

Click on Import Projections.

Then click on Import.

ImportProjections

Select your analysis and click on Import.

If you want to see the projections, click on Edit Projections.

EditProjections

Generate SQL statements

Once the projections are created, we can generate the SQL statements.

FHIRSQLGenerateSQL

Select your analysis, the projections and the schema.

Then click on Projection.

Usage

Now that we have the SQL statements, we can play with them into IRIS.

Go to the SQL UI :

http://localhost:33783/csp/sys/exp/%25CSP.UI.Portal.SQL.Home.zen?$NAMESPACE=FHIRSERVER

SQLUI

Let’s play with few SQL statements.

Patient

Display the patient as a table :

SELECT 
ID, AddressCity, BirthDate, Gender, Key, NameFamily, NameGiven, NamePrefix, NameUse
FROM AA.Patient

Result :

ID AddressCity BirthDate Gender Key NameFamily NameGiven NamePrefix NameUse
1 Shirley 1954-06-13 male Patient/1 O’Hara248 Carroll471 Mr. official
172 Somerset 1975-08-12 male Patient/172 Jast432 Frankie174 Mr. official
459 Swansea 2009-05-04 female Patient/459 Rohan584 Gabriele201 official
705 Weston 1945-12-19 female Patient/705 Frami345 Kallie862 Mrs. official
1002 Weston 1945-12-19 female Patient/1002 Davis923 Lean294 Mrs. official
1510 Boston 1995-03-26 female Patient/1510 Hettinger594 Margie619 Ms. official

Join patient with an external table

Create an external table :

create TABLE AB.DriverLicense (
Code varchar(255) not null,
Valide TINYINT not null

)

Insert data into the external table :

INSERT INTO AB.DriverLicense (Code, Valide) VALUES ('S99916528', 1);

INSERT into AB.DriverLicense (Code, Valide) VALUES ('S99950276', 0);

Join the external table with the patient table :

SELECT 
P.NameFamily, P.NameGiven,DL.*
FROM AA.Patient P
inner join AB.DriverLicense DL on P.IdentifierValue = DL.Code

Result :

ID AddressCity BirthDate Gender IdentifierTypeCodingCode IdentifierValue Key NameFamily NameGiven NamePrefix NameUse Code Valide
1 Shirley 1954-06-13 male DL S99916528 Patient/1 O’Hara248 Carroll471 Mr. official S99916528 1
172 Somerset 1975-08-12 male DL S99950276 Patient/172 Jast432 Frankie174 Mr. official S99950276 0
459 Swansea 2009-05-04 female Patient/459 Rohan584 Gabriele201 official
705 Weston 1945-12-19 female DL S99938125 Patient/705 Frami345 Kallie862 Mrs. official
1002 Weston 1945-12-19 female DL S99975762 Patient/1002 Davis923 Lean294 Mrs. official
1510 Boston 1995-03-26 female DL S99981337 Patient/1510 Hettinger594 Margie619 Ms. official

What is neat to note here is that we can join the FHIR Server with an external table.

Then it’s in the imported projections we ask to project not all the identifiers but only the driver license.

From ExportFHIRtoSQL.json :

        {
          "name": "IdentifierTypeCodingCode",
          "type": "String",
          "path": "Patient.identifier.type.coding.where(code = 'DL').code",
          "index": false
        },
        {
          "name": "IdentifierValue",
          "type": "String",
          "path": "Patient.identifier.where(type.coding.code = 'DL').value",
          "index": false
        }

Play with iknowpy

We can also play with iknowpy.

iknowpy is a natural language processing library available on python.

We can use it to extract information from a text.

Let’s create an SQL function in python :

CREATE FUNCTION sqliknowparser(tText VARCHAR(50000))
    RETURNS VARCHAR(50000)
    LANGUAGE PYTHON
{
    import iknowpy
engine = iknowpy.iKnowEngine()

# index some text
text = tText
engine.index(text, 'en')

t_output = ""

# or make it a little nicer
for s in engine.m_index['sentences']:
    for e in s['entities']:
        if e['type'] == 'Concept':
            t_output = t_output  + e['index']+ "|"

return t_output[:-1]

}

Then we can use it in a SQL statement :

SELECT
ID, DescriptionText, Key, LifecycleStatus, SubjectReference, $piece(sqliknowparser(DescriptionText),'|',1) as entities
FROM AA.Goal 
union
SELECT 
ID, DescriptionText, Key, LifecycleStatus, SubjectReference, $piece(sqliknowparser(DescriptionText),'|',2) as entities
FROM AA.Goal

Result :

ID DescriptionText Key LifecycleStatus SubjectReference entities
178 Maintain blood pressure below 140/90 mm[Hg] Goal/178 accepted Patient/172 blood pressure
179 Reduce sodium intake to no more than 2,400 mg/day Goal/179 accepted Patient/172 sodium intake
178 Maintain blood pressure below 140/90 mm[Hg] Goal/178 accepted Patient/172 140/90 mm[hg]
179 Reduce sodium intake to no more than 2,400 mg/day Goal/179 accepted Patient/172 no more than 2,400 mg/day

Tableau

To make a connection with Tableau, you need to install the JDBC driver.

Copy the JDBC driver in the drivers folder of Tableau.

JDBC driver : https://github.com/intersystems-community/iris-driver-distribution/blob/main/JDBC/JDK18/intersystems-jdbc-3.6.1.jar

drivers folder :

  • Windows : C:\Program Files\Tableau\Drivers
  • Mac : ~/Library/Tableau/Drivers
  • Linux : /opt/tableau/tableau_driver/jdbc

Just open Tableau and connect to IRIS with the JDBC driver.

Tableau file : TableauDemo.twb in misc folder.

Conclusion

We have seen how to use the FHIR SQL Build to create a SQL database.

Made with
Version
1.0.002 Feb, 2023
ObjectScript quality test
Category
Technology Example
Works with
InterSystems IRIS for Health
First published
02 Feb, 2023
Last checked by moderator
27 Jun, 2023Works