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Gina’s professional journey began at CS Mott Children’s Hospital at the University of Michigan, where she worked as a clinical research coordinator specializing in infants and toddlers with congenital heart defects. During her time there, she played an integral role in several major research projects, including the NIH-sponsored TECS study in partnership with Boston Children’s Hospital. This study focused on managing blood glucose levels with insulin to improve recovery outcomes for babies undergoing open-heart surgery. Gina also coordinated novel therapeutic trials for cardiomyopathy, overseeing everything from patient enrollment to follow-up visits.

Beyond her work in clinical trials, Gina helped establish a national registry for pediatric cardiothoracic ICUs, including sites in Canada. This registry was ahead of its time, tracking over 200 data points and offering physicians a unique opportunity to compare their hospital’s performance and patient outcomes with others in real-time. It encouraged collaboration in an environment that is typically very competitive, helping to improve care across the board.

After her time in pediatric research, Gina transitioned into the legal field as an Associate Director for a team focused on applying artificial intelligence (AI) to improve workflows and identify relevant data in complex legal investigations. The AI-driven tools they developed helped streamline data retrieval and analysis, making legal research faster and more accurate and ultimately providing the perfect segue into Hope’swork, where she is assisting in the exploration of how AI can enhance clinical research, improve data collection, and ultimately help advance care for families impacted by HIE.

One of the primary roles at Hope is to form relationships with researchers and clinicians from around the world who are investigating new treatments and therapies for HIE. By establishing these connections, she ensures that the work aligns with the needs of Hope families.

Her goal is to educate researchers about the unique benefits of the Hope for HIE “Halo of Support” model, which focuses on providing families with comprehensive support services, including clear communication, dedicated spaces for families during research studies, and access to peer support networks. In turn, this model benefits researchers by improving retention rates, ensuring more consistent data, and providing a deeper understanding of the real-world impact of their work. By addressing families’ emotional and practical needs—such as through access to child life specialists, peer support, and social workers—researchers can reduce barriers that might otherwise hinder family participation.

As part of her work, Gina coordinates family input for studies like the COOL PRIME & STAR studies and upcoming trials. She helps facilitate family advocacy panels that work directly with researchers and clinicians to inform trial protocols. This means that Hope families have a voice in shaping the design of studies, ensuring that protocols are not too burdensome and more aligned with what is manageable for families. By keeping the focus on outcomes that matter most to parents—such as the creation of long-term developmental outcomes for children with HIE—Gina ensures that research stays family-centered.

One of the key initiatives Gina is leading at Hope for HIE is the development of the HIE Registry, a vital tool designed to collect data that will improve long-term outcomes for children with HIE. This registry will support research and deepen our understanding of the broader implications of HIE diagnoses and treatments. A key priority for Gina is ensuring that the data collected reflects a global perspective, benefiting the entire HIE community. To achieve this, she is focused on carefully considering the methods and data points to ensure consistency, accuracy, and relevance.

One of the most exciting aspects of the HIE Registry is its potential for data harmonization. Gina is working to establish common data elements that will allow registries from various institutions to communicate with each other, making it easier to share and analyze data across different systems, creating a larger, more comprehensive cohort to drive research forward. This collaboration will help reduce the bias in data collection and will ensure that future studies have the statistical power needed to make meaningful conclusions.