As an ob-gyn specializing in infertility, Mylene Yao, MD, has devoted her career to helping women try to conceive. But IVF treatments can be stressful and expensive, with many women needing more than one round of treatment to have a baby. Yao wanted to help make IVF more accessible and successful for as many families as possible.
In 2005, while working at Stanford University, Yao and colleague Wing H. Wong, PhD, began developing a prediction model that uses historical data analytics from more than 150,000 IVF cycles combined with a couple’s personal information (like age, body mass index, ovarian reserve test results and semen analysis) to craft personalized fertility prognosis for patients and their doctors.
The result? Mapping a couple’s fertility odds that are 1,000 times more accurate than going off of age-based estimates alone. After a few years developing their technology, a business grant from the university gave them the push they needed to “get on the startup track,” and Univfy was founded in 2009.
The company now offers an “end-to-end” solution by giving couples a personalized probability of their chance to have a baby from IVF and how to do it quickly to lessen the financial cost. “That’s what people see as the hurdle, and we think with advanced analytics that can be overcome,” she says.
“In our first phase, our test provided information about the probability of success for that patient. But through feedback from patients and doctors, we realized that to truly meet their needs, we needed to figure out how to make IVF financially feasible for patients. We started working on that concept in the beginning of 2015 and launched in January 2016. Our program allows fertility clinics to structure their pricing and offer a refund program to more than 50 percent of patients in the event that they don’t have a baby.”
Changing insurance coverage
“We need a range of solutions for how different groups or companies could help more patients get fertility treatments. They traditionally haven’t been well covered by insurance because there’s been little information for a provider to go on. But validated analytics that can predict outcomes strengthen the argument that they should be covered. We’re exploring ways our technology can be used to help companies interested in expanding fertility benefits to their employees.”
The personalization model
“Over the last few years, the idea of using data analytics to help make personalized decisions (like with online shopping) has become more widely accepted and less of a foreign concept, which has helped us a lot. If you can harness more info from a patient that has been validated to impact her probability of IVF success, why wouldn’t you do that to help give her the best information possible?”
Problem-solving for the patient
“A lot of my job involves figuring how we can make the most sense of data to solve a problem: helping more patients be able to get IVF treatment. Univfy isn’t any one thing—it’s not just a research project, it’s not just a business idea, it’s not just a website—it’s all of these things coming together to give a very practical solution both patients and doctors can use.”