# Wake Forest IMPOWR Dissemination Education and Coordination Center (IDEA-CC)

> **NIH NIH R24** · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2022 · $309,729

## Abstract

PROJECT SUMMARY
The HEAL Data Ecosystem is working to collect data across its projects and networks to meet FAIR (Findable,
Accessible, Interoperable, Reusable) data standards. Bringing diverse data sources together will require
complex data solutions to have a highly successful and accessible HEAL data commons. Meeting these data
goals brings two major challenges. The first is there are existing siloed datasets that are not yet able to be
combined with other data limiting the findability and accessibility. The second is collecting and organizing
prospective data so that one could assure data quality and integrity that allows for interoperability and reuse of
the data. To accomplish this goal, this proposal responds to the request for strategies to make data more
machine learning/artificial intelligence (ML/AI ready). 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. The proposed work will
significantly deepen and augment approaches to FAIR principles in CP and OUD data for both the HEAL
network and larger NIH research community. 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 incorporates existing data and supports the integration of prospective CP and OUD
data collection. Building on our prior work, the overall objective of this project is to move CP and OUD data one
step closer to FAIR by leveraging existing datasets and developing tools for new projects. The general
hypothesis of the project is that leveraging existing CP & OUD data and collecting new data using ML/AI data
quality standards will accelerate the impact of the HEAL Data Ecosystem. The general hypothesis will be
tested by the following specific aims: (1) Transform existing dataset by mapping chronic pain/OUD CDE to
demonstrate use case for making existing siloed data into a ML/AI ready format by automatically suggesting
HEAL CDE annotations for already collected data based on semantic and syntactic analysis. (2) Adapt tools to
support ML/AI readiness for existing and prospectively collected HEAL CDE. First, we will adapt our previously
developed tools to measure and assess the semantic distance for pain/OUD CDE. This will support the
development of federated transfer learning by assessing the quantitative distances using SHAP modeling of
previously collected data. We hypothesize that these tools will provide infrastructure necessary to successfully
develop ML/AI ready data. In aim 1, we believe that transforming existing datasets to be ML/AI ready will
accelerate the harmonization of existing and prospective data for a future HEAL Data Commons. In aim 2, ...

## Key facts

- **NIH application ID:** 10593312
- **Project number:** 3R24DA055306-02S1
- **Recipient organization:** WAKE FOREST UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** MEREDITH C. B. ADAMS
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $309,729
- **Award type:** 3
- **Project period:** 2021-09-30 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10593312

## Citation

> US National Institutes of Health, RePORTER application 10593312, Wake Forest IMPOWR Dissemination Education and Coordination Center (IDEA-CC) (3R24DA055306-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10593312. Licensed CC0.

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