The Evolve to Next-Gen Accrual to Clinical Trials (ENACT) (previously known as ACT) network was established in 2015 with funding from the NCATS. ENACT is a large federated network of EHR data repositories at 57 CTSA hubs that serves as an information superhighway for querying EHR data on >142M patients and providing data access to all CTSA hub investigators. As a substantial portion of vital information resides within clinical texts, the utilization of Natural Language Processing (NLP) techniques is critical to fully leverage EHRs for clinical and translational research. However, to date, no large EHR network has implemented NLP pipelines and systems to fully utilize the text data. The ENACT NLP working group was established with the primary goal of ensuring that NLP pipeline will be deployed network wise and NLP-derived concepts become accessible and searchable across the entire ENACT network. The working group consisted of ten participating ENACT sites, which were then split into several focus groups to pilot a few specific projects in different disease conditions. During this panel, we will introduce the current state of the ENACT NLP Working Group and share practical strategies we made and learned during the process. We will share the updates and lessons learned from three pilot projects, including housing status identification, delirium phenotype identification, and opioid disorder identification, with the AMIA community. This work will also benefit other large EHR networks, such as the PCORnet and OHDSI network, which are considering deploying NLP pipelines to unlock the potential of clinical texts.
Learning Objectives
- Describe the structure, goals, and current status of the ENACT NLP Working Group and its role in leveraging Natural Language Processing (NLP) to enhance the utility of clinical text data across the ENACT network.
- Explain the strategies and challenges associated with deploying NLP pipelines across a federated EHR network, including lessons learned from pilot projects.
- Analyze the outcomes of three pilot projects (housing status identification, delirium phenotype identification, and opioid disorder identification) and their implications for scaling NLP efforts in other large EHR networks.
- Identify opportunities for applying NLP techniques in clinical and translational research within other large-scale EHR networks, such as PCORnet and OHDSI.
Moderator
- Yanshan Wang, PhD, University of Pittsburgh
Speaker
- Yanshan Wang, PhD (University of Pittsburgh)
- Sunyang Fu, PhD, MHI (UTHealth)
- Paul Heider, PhD (Medical University of South Carolina)
- Daniel Harris (University of Kentucky)
- Michele Morris, BA (University of Pittsburgh)
Continuing Education Credit
Physicians
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this online enduring material for 1.25 AMA PRA Category 1™ credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Claim credit no later than March 10, 2028 or within two years of your purchase date, whichever is sooner. No credit will be issued after March 10, 2028.
ACHIPsTM
AMIA Health Informatics Certified ProfessionalsTM (ACHIPsTM) can earn 1 professional development unit (PDU) per contact hour.
ACHIPsTM may use CME/CNE certificates or the ACHIPsTM Recertification Log to report 2025 Summit sessions attended for ACHIPsTM Recertification.
Claim credit no later than within three years of the release date or within two years of your purchase date, whichever is sooner.