The generation and application of evidence is fundamental to the practice of medicine in the modern era. Methodologies like randomized controlled trials (RCT) and meta-analyses have produced a body of evidence that have demonstrably improved care for a multitude of common diseases. However, for many rare outcomes or conditions there is often a striking lack of data in which to drive evidence-based clinical decisions. With the recent expansion of multi-model datasets across EHRs, genomics and digital health, there exists an opportunity to fill in gaps of knowledge to improve recognition, diagnosis, prognostic models, and treatment of numerous, often rare outcomes.
The Center for Digital Genomic Medicine (DGM) seeks to advance our understanding by building integrated data resources and develop novel methodologies to directly inform and improve diagnostics and treatment decisions.
In summary, DGM seeks to:
- Establish infrastructure to collect, extract, curate and derive clinically relevant information that is currently not captured during routine clinical care (e.g. behavior)
- Create scalable diagnostics and prognostic modeling, including patient identification for targeted therapeutics
- Leverage EHR, genetic and environmental data to improve understanding of risk factors and inform clinical decision making