A large northern Virginia hospital and a Massachusetts analytics firm are partnering see if data mined from EMRs can better predict the risk of preterm live birth.
The Inova Translational Medicine Institute at Virginia’s Inova Fairfax Hospital is working with Cambridge, MA-based analytics firm GNS Healthcare to create and commercialize computer models to predict the risk of preterm birth, reports Healthcare IT News. The two are using next-generation genomic sequencing technology and EMR data to build the models.
The models will be built using ITMI’s large database, which is stocked with data on both normal and preterm family cohorts. GNS will then attempt to link genetic and molecular factors with clinical data and health outcomes, Healthcare IT News said.
Once created, GNS and ITMI will license the models and software — as well as optional access to underlying ITMI data — to academic researchers, health systems and pharma/biotech companies. The ITMI database includes whole genome sequencing (SNP, CNV, SV), RNAseq expression, CpG methylation, proteomic, metabolomic, imaging, EMR, clinical phenotypes and patient survey data for over 2,400 individuals, Healthcare IT News reports.
The two partners are attacking a large problem. As Healthcare IT News notes, 12 percent of babies born in the U.S. are delivered at less than 37 weeks gestation, which causes nearly 10,000 deaths per year and costs $28 billion annually. Researchers suspect that genetic factors help to prompt preterm birth, though no specific genes have been identified to date.
But there’s many more problems to take on using this approach, and translational medicine projects of this kind are popping up nationally. For example, recently New York’s Mount Sinai Medical Center launched a new program designed to link information stored in the EMR with genetic information provided by patients. As of May, 25,000 patients had signed up for the biobanking program.
I believe this is just the tip of the iceburg. Using EMR data with genomic information is a very logical way to move further in the direction of personalized medicine. I’m eager to see other academic medical centers and hospitals jump in!