REDCap to OMOP: Making NIH HEAL Data More FAIR

As part of the NIH HEAL IMPOWR research network, the Wake Forest IDEA-CC is building connections for chronic pain (CP) and opioid use disorder (OUD) using advanced data solutions. The HEAL Data Ecosystem is working to transform data across its projects and networks to meet FAIR (Findable, Accessible, Interoperable, Reusable) data standards. Our supplement supports the development of tools that will automate a process and remove barriers for combining data across different HEAL projects. This supplemental proposal will amplify the objective of IDEA-CC to build data infrastructure tools that will convert existing data into format that will allow for improved FAIR. To accomplish this goal, this proposal responds to the request for strategies to make data harmonize across different research networks. Our team is experienced working in the OMOP data model and will create data pipelines for HEAL clinical data.The focus of the parent grant is to create a research framework for the HEAL IMPOWR network and larger scientific community to harmonize combined chronic pain (CP) and opioid use disorder (OUD) data. This administrative supplement expands this mission beyond the scope of the NIH HEAL IMPOWR network to existing and future CP and OUD clinical trials. The proposed work will significantly deepen and augment approaches to FAIR principles in CP and OUD data for the HEAL Data Ecosystem, connecting with both RENCI and Vanderbilt University’s REDCap program. It enhances the rigor of the parent grant by improving the larger data relevance of what we are doing beyond the NIH HEAL IMPOWR network. The long-term goal is to build a HEAL Data Ecosystem that supports a connected data network of CP and OUD clinical trial networks. Building on our prior work, the overall objective of this project is to move CP and OUD data one step closer to FAIR by building tools to automate process that would normally require both extensive domain knowledge and informatics expertise. We hypothesize that developing systems for automating conversion of CP and OUD data elements will allow HEAL research teams without specific technical expertise in this area to convert their data. The general hypothesis will be tested by the following specific aim: (1) Transform existing HEAL CDE into OMOP data model via automated tool development. First, we will adapt our mappings for OMOP and HEAL CDE to develop a working use case. Then we will, test and refine our automated tool for the HEAL CDE data. We hypothesize that these tools will provide infrastructure necessary to successfully develop FAIR data. In aim 1, we believe that transforming existing HEAL CDE into the OMOP data model will accelerate the harmonization of existing and prospective data for a future HEAL Data Commons. The expected outcome of this project is data optimization pipelines and tools to support the goal of FAIR data across the HEAL ecosystem. The results will provide a strong basis for further development of the HEAL Data Ecosystem and extend the impact of the IDEA-CC beyond the NIH HEAL IMPOWR network.