Guiding Research on Heart Health, Cancer Care and Pediatrics
This is the first part of a two-part series on data science, adapted from a feature from the Northwestern University News Center and edited for the Breakthroughs in Care audience.
Data science is transforming biomedical research at Northwestern University Feinberg School of Medicine, propelling important discoveries in rare and common diseases and translating those findings into new treatments and individualized patient care at an accelerated pace.
Much of this research is possible because of the depth of data housed in the Northwestern Medicine Enterprise Data Warehouse (NMEDW). That includes 95 million inpatient admissions and outpatient visits and 101 billion data elements (a patient lab test, for example) – a number updated by 14 million new data elements every night
“The depth of the data on those individuals now allows us to drill down in a way that’s never been possible before and really understand individual responses to treatments,” said Donald Lloyd-Jones, MD, chair of preventive medicine and director of Northwestern University Clinical and Translational Sciences Institute. “It’s allowing us to develop true precision medicine so we can better tailor treatments to the people who are most likely to respond and least likely to have adverse effects. That’s the end game of this. That’s the future of medicine.”
Findings from deep dives into data are already informing research in such fields as cardiovascular disease, cancer and critical pediatric care.
Data Science and Heart Health
A perfect example of how data science is shaping care at Northwestern Medicine is the research of Sanjiv Shah, MD, associate professor of Medicine in the Division of Cardiology. He uses electronic health records to identify patients for enrollment in a specialized heart failure clinical program and clinical trial. Then, he uses a combination of deep phenotyping and machine learning to discover new ways to understand the disease process and ultimately improve treatment.
“We view this as a paradigm for how we want to help a number of clinical programs evolve,” Lloyd-Jones said. “We are helping them align their clinical and research missions by harnessing the analytical power of data science.”
Data Science and Critical Pediatric Care
Mark Wainwright, MD, PhD, professor of Pediatrics and Neurology, and his team are looking for the signals from data science to improve the outcomes of critically ill children at Anne & Robert H. Lurie Children’s Hospital of Chicago. This group is developing tools to integrate and analyze data from all the different monitoring devices attached to a critically ill child in order to provide earlier warning of changes in a patient’s condition that require intervention by the medical team. By analyzing the trajectory of thousands of pediatric patients in intensive care, they will have computers develop an algorithm of signals to warn of an unstable situation that needs immediate attention.
“This would be invaluable, allowing us to catch much earlier the subtle signals that a child is getting worse,” Dr. Wainwright said. “We could then intervene and prevent a cardiac arrest or other serious complications.”
Data Science and Cancer Care
Data science also is on the cusp of transforming cancer care as scientists analyze volumes of critical genetic information to develop more personalized and effective treatment for individual patients. Ramana Davuluri, PhD, director of the Informatics Cancer Core at the Robert H. Lurie Comprehensive Cancer Center of Northwestern University, is developing methods for analyses of data sets in multiple fields of study from patients with glioblastoma, the most common and aggressive malignant brain tumor as well as prostate, breast and ovarian cancers. The goal is to parse the genetic differences between groups of patients within each cancer to determine which treatments will best help them.
To learn about how scientists extract insight from vast volumes of data and electronic medical records, read part two.
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