William & Mary data scientist Haipeng Chen believes in AI for social good. So, he is using his expertise to help deliver personalized and more accessible health care to patients with chronic conditions.

Chen, an assistant professor of data science, is leading a partnership between William & Mary and the health care technology company Generated Health. He and his team will develop synthetic patient data to help train a more autonomous version of the company’s digital nurse, “Florence.” 

“If we can have an AI system that can deliver automated, personalized management of patients, then we will relieve some of the growing pressures created by the accelerating prevalence of chronic conditions and workforce shortages,” said Chen.

The contract with Generated Health, starting July 1 this year, will also cover the stipend of a graduate research assistant from Chen’s lab.

This partnership is part of a growing portfolio of externally funded data science research at William & Mary. The data science program has attracted over $2 million in research funding last year and is now extending its scope with projects supported by federal agencies and the private sector – as well as pursuing technology transfer opportunities.

“As the disciplinary home of AI on campus, the data science unit is particularly interested in studying AI solutions as they impact the world,” said Professor Anthony Stefanidis, data science program director. He described the research program as particularly focused on the intersections of data science and AI with location, health, information generation and dissemination, and large-scale experiments and simulations.

The data science program will be part of a proposed new school at William & Mary, which will expand among other things the university’s focus on data fluency and data-intensive research by building on the strengths of existing programs.

AI for the benefit of patients

According to a Generated Health press release, the digital nurse Florence has already managed over 25 million clinical conversations with 200,000 patients in three countries, delivering a better patient experience and improved clinical outcomes.

Assistant professor of data science Haipeng Chen, looking at the camera with his arms crossed.
Data Science Assistant Professor Haipeng Chen.

Chen said that Florence has been used to help chronic disease patients monitor and control their conditions.

“In many cases, patients can’t get an appointment soon enough to get to know their condition better,” said Chen. “Using AI, we can have an automated way to accelerate and augment the current health care system.”

Chen and his team will be developing an AI diffusion model simulating real patient behavior, which will be used to train the nurse model combining generative AI and reinforcement learning. 

The goal is developing a next-generation digital nurse with the ability to take effective decisions – learning from its interaction with an environment – within a set of clinical rules and protocols that eliminate the risk for hallucination – that is, incorrect information presented as factual.

Chen’s interest in health care is not new. While a postdoctoral fellow at Harvard, he started working on AI in the public health domain. At William & Mary, he and Associate Professor of Kinesiology Carrie Dolan are developing a project using data science to get timely vaccinations to rural communities in Kenya.

“I believe that AI should be used for the good: It’s a kind of philosophical belief,” said Chen. “Many people mostly care about the fancy techniques, but then at the end of the day what really makes AI useful is its application to domains related to society.”

According to Chen, one advantage of applying AI to the medical domain is freeing up clinicians’ time, helping alleviate the impact of workforce shortages in health care across the nation – currently estimated at 200,000 among nurses and 124,000 among physicians by the 2030s. Also, he sees AI as a support tool for auxiliary health care workers, helping remove barriers and create job opportunities.

“This collaboration is a very important piece of my general vision,” he said. “I would be excited to see this system benefiting tens of thousands or even millions of patients around the world – because that’s one of the end goals for researchers in AI for social good.”

, Senior Research Writer