There’s a small cadre of highly skilled big data professionals and doctors who are leveraging technology to help you live a longer, healthier life. Armed with mountains of government-funded genomic data sets along with mature and easily accessible analytics tools, these technicians and doctors are building apps, tools, and systems which can help you diagnose and treat illnesses ranging from common to catastrophic.
Leading that charge is Dexter Hadley, unique in that he is both an engineer and a doctor. Dexter runs the Hadley Lab – a big data laboratory at UCSF Health which develops tech to fight Disease and promote health. The Hadley Lab has a mandate to derive value from the mountains of clinical data that UCSF continually generates. With a research background in genomics and clinical training in pathology, Dexter likes to quip that he “uses big data to practice medicine.”
We got a chance to ask Dexter about the innovations that are born at the intersection of technology and medicine and tell us about how the democratization of technology is really impacting people’s lives.
So first off, people are probably wondering why and how you became both a doctor AND an engineer?
I have always wanted to be a doctor, but my trajectory changed dramatically when I taught myself to program computers at the age of 10 years old. Since then, I have been obsessed with how to leverage computation to better facilitate medicine. That journey took me from an undergraduate education focused on computer programming to medical school at the University of Pennsylvania where I earned a master’s degree in engineering, a Ph.D. in genomics, and an MD for good measure.
Through stints practicing medicine in an internship in general surgery at Penn, and then later residency in pathology at Stanford, I developed a passion as a physician/scientist to integrate medicine and software engineering in order to improve the delivery of healthcare for doctors and their patients.
So, what does the Hadley Lab do and how do you contribute?
The Hadley Laboratory leverages big data to improve the practice of medicine and the delivery of healthcare. Our work generates, annotates, and ultimately reasons over large and diverse data stores to better characterize diseases. We develop state-of-the-art data-driven models of clinical intelligence that drive clinical applications to more precisely screen, diagnose, and manage diseases. We integrate multiple large data stores to identify novel biomarkers and potential therapeutics for Disease.
The end point of our work is rapid proofs of concept clinical trials in humans that translate into better patient outcomes and reduced morbidity and mortality across the disease spectrum. “I’m an equal opportunity scientist. I care less about the best disease I can study, but more about what disease I can study best– it’s all driven by the data.
And what would you say is the present, future, and ideal state of R&D in this area?
At present, I think we are experiencing a continued renaissance of medicine that started with the initial sequencing of the human genome well over a decade ago. Now, we are finally in a position to actually quantify human health and disease in “precision medicine,” a fundamentally different approach to healthcare research and its delivery where our focus is on identifying and correcting individual patient differences rather than making broader generalizations.
While genomics allows us to quantify our molecular self, I think the future is in leveraging all the technology at our fingertips today to better quantify our physical self. As the power of genomics lies in its objective ability to correlate with physical manifestations in the patient, the ideal state of R&D must involve data collection and analysis at both the molecular genotypic level and the more clinical phenotypic level of the patient.
