This is how data scientists can help doctors improve patient care
By analyzing electronic data from many doctors' experiences with many patients, we can move ever closer to discovering what is truly best for each patient.
Leo moved to the US from the Philippines after medical school to pursue specialty training in internal medicine (Cleveland Clinic), infectious diseases (Harvard) and critical care medicine (Stanford). He has practiced medicine in three continents (Philippines, US and New Zealand) and has worked in both industry (Philips Visicu) and academe (faculty positions at Harvard, MIT, Stanford and University of Otago), rendering him with broad perspectives in healthcare delivery. He has a strong interest in systems re-design for quality improvement, and became the New Zealand representative to the Quality and Safety Committee of the Australia New Zealand Intensive Care Society in 2006. Feeling he needed more skills to tackle the healthcare inefficiencies he faced wherever he practiced, he went back to the US to pursue graduate studies in biomedical informatics at MIT and public health at Harvard. While attending both schools and working part-time as an emergency department physician, he co-founded Sana, personally recruiting most of the current members, and was instrumental in shaping the mission and vision of the young organization.
His other research interest is in data mining and the application of machine learning on large databases. As a research scientist at the Laboratory of Computational Physiology at MIT, he works with MIMIC, a publicly-available de-identified ICU database from BIDMC. He is working on a data-driven decision support system known as Collective Experience that (1) allows a clinician to draw on the experience of other clinicians who have taken care of similar patients as recorded in a clinical database, and (2) uses models performed on relatively homogeneous patient subsets.