What's Happening

3/24/2025

To better support researchers and institutions using the ENACT Network, we introduced our Monthly Informatics Office Hours series. These sessions, held on the second Monday of each month from 3 – 4 pm ET, provide an opportunity to discuss technical challenges, share best practices, and engage directly with our informatics experts. We look forward to continuing these conversations and helping our community make the most of ENACT’s resources.

3/1/2025

ENACT was excited to participate in the 2025 American Medical Informatics Association (AMIA) Conference, where we showcased our latest innovations in data harmonization and real-world evidence generation. Our team presented on key topics, including scalable approaches to integrating EHR data across institutions and strategies for ensuring reproducibility in multi-site research. If you attended AMIA, we hope you connected with us—we enjoyed discussing how ENACT could support your research!

11/13/2024

The ENACT Network made a strong impact at the 2024 Clinical and Translational Science Award (CTSA) Program Meeting, where Dr. Steve Reis presented insights on leveraging multi-institutional electronic health record (EHR) data to advance clinical research. Our presentation highlighted real-world use cases demonstrating how ENACT’s federated data network accelerated discovery while maintaining patient privacy. Attendees engaged in discussions about optimizing data-sharing strategies and fostering cross-institutional collaboration. Thank you to everyone who joined us—we looked forward to continuing the conversation!

2024-10-03

Mora N, Mehall M, Lennox LA, Pincus HA, Charron D, Morrato EH.

Journal of Clinical and Translational Science.

Introduction: The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).

Methods: We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group.

Results: Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers’ unmet needs.

Conclusions: Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science....

2023-09-29

Elaine H Morrato et al.

Journal of Clinical and Translational Science

The ACT Network was funded by NIH to provide investigators from across the Clinical and Translational Science Award (CTSA) Consortium the ability to directly query national federated electronic health record (EHR) data for cohort discovery and feasibility assessment of multi-site studies. NIH refunded the program for expanded research application to become “Evolve to Next-Gen ACT” (ENACT). In parallel, the US Food and Drug Administration has been evaluating the use of real-world data (RWD), including EHR data, as sources of real-world evidence (RWE) for its regulatory decisions involving drug and biological products. Using insights from implementation science, six lessons learned from ACT for developing and sustaining RWD/RWE infrastructures and networks across the CTSA Consortium are presented in order to inform ENACT’s development from the outset. Lessons include intentional institutional relationship management, end-user engagement, beta-testing, and customer-driven adaptation. The ENACT team is also conducting customer discovery interviews with CTSA hub and investigators using Innovation-Corps@NCATS (I-Corps™) methodology for biomedical entrepreneurs to uncover unmet RWD needs. Possible ENACT value proposition hypotheses are presented by stage of research. Developing evidence about methods for sustaining academically derived data infrastructures and support can advance the science of translation and support our nation’s RWD/RWE research capacity....

2022-07-01

Leslie A Lenert et al.

JAMIA open

Opioid Overdose Network is an effort to generalize and adapt an existing research data network, the Accrual to Clinical Trials (ACT) Network, to support design of trials for survivors of opioid overdoses presenting to emergency departments (ED). Four institutions (Medical University of South Carolina [MUSC], Dartmouth Medical School [DMS], University of Kentucky [UK], and University of California San Diego [UCSD]) worked to adapt the ACT network. The approach that was taken to enhance the ACT network focused on 4 activities: cloning and extending the ACT infrastructure, developing an e-phenotype and corresponding registry, developing portable natural language processing tools to enhance data capture, and developing automated documentation templates to enhance extended data capture. Overall, initial results suggest that tailoring of existing multipurpose federated research networks to specific tasks is feasible; however, substantial efforts are required for coordination of the subnetwork and development of new tools for extension of available data. The initial output of the project was a new approach to decision support for the prescription of naloxone for home use in the ED, which is under further study within the network....

The inaugural ENACT Grand Rounds seminar on March 24, 2025, will feature Shyam Visweswaran, MD, PhD, Professor and Vice Chair of Clinical Informatics in the Department of Biomedical Informatics at the University of Pittsburgh, and Olga V. Kravchenko, MS, PhD, Assistant Professor in the Department of Family Medicine at the University of Pittsburgh. Dr. Visweswaran and Dr. Kravchenko will present ENACT’s pioneering postpartum hemorrhage (PPH) study, which exemplifies ENACT’s ability to generate insights from multi-site data, showcasing a stepwise journey from initial hypotheses to advanced predictive modeling. By analyzing data from 22 ENACT sites and over 1.2 million unique delivery hospitalizations (2005–2022), researchers uncovered troubling trends in PPH incidence and comorbidity burdens. Using ENACT enclaves and synthetic datasets, the team developed a machine learning (XGBoost) model incorporating 15 risk factors to predict PPH, demonstrating improved performance when combining data across multiple sites. Attendees will learn how ENACT’s secure, collaborative tools can be applied to solve complex healthcare challenges, advancing clinical decision-making.

Precision Phenotyping in ENACT for Curated Cohorts of Unexplained Chronic Conditions With advances in AI and increasing computational capabilities, electronic phenotyping is evolving into precision phenotyping -- where multi-modal frameworks define phenotypes in a more personalized manner using electronic health record (EHR) data. The ENACT network supports this shift by providing interoperable informatics infrastructure for distributed learning and algorithm deployment. This presentation explores how precision phenotyping algorithms within ENACT can be applied to curate highly specific research cohorts for complex, unexplained chronic conditions. By integrating algorithmic precision and collaborative infrastructure, this approach offers new avenues for targeted clinical studies and advances in translational research. Presenter: Hossein Estiri, PhD, is an Associate Professor of Medicine at Harvard Medical School and an Investigator at Massachusetts General Hospital, where he leads the Clinical Augmented Intelligence Group (CLAI). He also serves as the Head of AI Research at the Center for AI and Biomedical Informatics of the Learning Healthcare System (CAIBILS) at Mass General Brigham. Dr. Estiri’s research is at the intersection of biomedical informatics, machine learning, and population health, with a particular focus on modeling complex phenotypes from electronic health records (EHRs). His work aims to advance precision medicine by developing computational frameworks that uncover nuanced patterns in clinical data, enabling more accurate phenotyping and improved cohort discovery for translational research and clinical trials.