Collaboration Intake Form

The UM-IHC leverages cutting-edge data science to optimize health and mitigate human disease via a Learning Health System model of continuous innovation. The UM-IHC receives significant funding from Montgomery County and the State of Maryland’s MPower Strategic Partnership. The Institute unites world-class computational and clinical expertise from the University of Maryland, Baltimore (Schools of Medicine, Nursing, Social Work, Dentistry, Pharmacy, and Law) and the University of Maryland, College Park (College of Computer, Mathematical and Natural Sciences and School of Public Health, among others) campuses with the University of Maryland Medical System (UMMS), a statewide network of hospitals and outpatient clinical operations that serves 2 million patients.

The UM-IHC comprises 6 Centers that serve as the foundation for complementary and collaborative research in support of the Learning Health System:

1. Artificial Intelligence (AI): Develop novel models to elucidate patterns in biological and clinical datasets as well as program digital health technologies that optimize mechanistic drug target discovery, phenotyping, prognostication, clinical trial enrollment, and implementation sciences.
2. Bioinformatics: Innovate approaches to reducing big data (i.e., genomic, transcriptomic, proteomic, and metabolic assets) to functionally important elements that provide critical information needed to clarify pathogenetic disease mechanisms.
3. Augmented and Virtual Reality: Use high-resolution, three-dimensional holographic and virtual graphical technology to visualize complex biological or clinical datasets, venues, or scenarios that are otherwise challenging or impossible to represent in two-dimensional space.
4. Therapeutic Target Discovery: Use real-time algorithms and software systems to analyze multiple interacting data streams to enable drug discovery. This includes the health equity-drug discovery initiative, which focuses on the effect of environmentally-determined molecular modifications of drug targets (i.e., stress-catecholamine; pollution-redox stress, others) to improve treatment responses in patient subgroups.
5. Population Health: Leverage real-world data, lived experiences, and advanced computational methods to improve health and healthcare quality through rigorous, interdisciplinary research on the multi-level drivers of health, healthcare delivery and utilization, health outcomes, and costs of care.
6. Real World Evidence and Pragmatic Trials: Interface data science with the electronic health record as well as administrative claims and publicly available data on social and structural determinants of health to improve clinical research methodology and facilitate large-scale trials.

These Centers are integrated and enable comprehensive research capacity that spans theory to practice to implementation for all health and human disease concepts.