As a new clinician, Stanford’s Jonathan Chen, MD, PhD, struggled to treat patients with unfamiliar conditions. He yearned to ask one or, even better, dozens of more experienced physicians for advice.
For most people, that would be a passing wish. But not for Chen, who has a PhD in computer science and experience working as a software developer. (Oh yeah, he also started college when he was 13).
“I thought about how the Amazon product-recommender algorithm works and thought, `Can we do this for medical decision-making?’” said the 34-year-old Chen, a VA Medical Informatics Fellow at Stanford Health Policy.
So instead of, other people who bought this book also liked this book, how about: Other doctors who ordered this CT scan also ordered this medication.
“What if there was that kind of algorithm available to me at the point of care?” he asked. “It doesn’t tell me the right or wrong answer, but I bet this would be really informative and help me make better decisions for my patients.”
Chen’s idea differs from the Green Button concept, which draws on thousands of medical records to search for patients with similar conditions. Instead, Chen is trying to capture doctor’s decision-making process by developing a digital platform to mine electronic medical records; he calls his project OrderRex.
It “looks for ‘doctors like me,’ and anticipates what the doctor wants before they ask for it,” Chen explains in the article.
Previously: Push-button personalized treatment guidance for patients not covered by clinical-trial results, Big Data in Biomedicine panelists: Genomics’ future is bright, thanks to data-science tools and Euan Ashley discusses harnessing big data to drive innovation for a healthier world
Photo by Joseph Matthews/VA Palo Alto