FHIR® for Researchers

Overview

The goal of this FHIR® for Research site is to equip NIH research scientists and program officers to better leverage FHIR® in their own research by deepening their understanding of where FHIR® data comes from and the FHIR® standard more generally. For a general review of the FHIR® ecosystem and potential applications, please review the Introduction to FHIR® for Research webinar. To complete a series of hands-on exercises on working with FHIR® for research, please review the hands on content below. To obtain a broad view of Electronic Health Records (EHR), terminology systems, and how United States Core Data for Interoperability (USCDI) and Implementation Guides like US Core enable remote access and interoperability, please continue with Exercise 4.

To view recordings of Exercises 0-3, please visit the following:

• Workshop in Python
• Workshop in R

Exercise 0

Exercise 0 introduces learners to the basic mechanisms for working with FHIR® by completing the following steps: establishing a connection to the client server, formatting and submitting a query to the server, processing response data from the FHIR® server, and viewing resulting data to confirm it was successfully pulled from the remote server. It includes hands on exercises in both R and Python.

• Exercise 0 Python
• Exercise 0 R

Exercise 1

Exercise 1 steps learners through a use case for patients prescribed opioids to teach the following concepts: understanding a FHIR® server’s capabilities, reading FHIR® specifications, understanding and searching for FHIR® Resources, processing paginated responses, and integrating with other, non-FHIR® Application Programming Interfaces (APIs). It includes hands on exercises in both R and Python.

• Exercise 1 Python
• Exercise 1 R

Exercise 2

Exercise 2 steps through a use case leveraging Kids First data to enable learners to better understand how to query FHIR® resources in various ways to enable visualizing and analyzing data. It includes hands on exercises in both R and Python.

• Exercise 2 Python
• Exercise 2 R

Exercise 3

Exercise 3 applies knowledge gained in the previous exercises to identify drug-on-drug interactions using FHIR® data and NLM APIs. It includes hands on exercises in both R and Python.

• Exercise 3 Python
• Exercise 3 R

Exercise 4

Exercise 4 is a self-paced web-based training that provides learners with a broad view of Electronic Health Records (EHR), terminology systems, and how United States Core Data for Interoperability (USCDI) and Implementation Guides like US Core enable remote access and interoperability. This exercise is meant to augment the materials in Exercises 0-3.