Jake Luo, an associate professor at UWM, uses AI to analyze massive health data sets, revealing disparities in healthcare access and outcomes. He collaborates with clinical investigators to identify gaps in treatment responses across different populations. His work aims not just to highlight issues but also to improve patient experiences through innovative reporting methods, such as AI-assisted interaction systems.
Jake Luo is on a mission to dive into the vast ocean of data surrounding health care disparities. Wearing multiple hats as an associate professor at UWM’s Zilber College of Public Health and the College of Engineering & Applied Science, he zeroes in on electronic health records—specifically, the National Inpatient Sample, which encompasses countless records from 7 million patients.
With computers struggling to keep up with the towering piles of data, Luo leans heavily on artificial intelligence. Ready to handle immense information loads and pinpoint patterns, AI aids Luo tremendously, especially at UWM’s High Performance Computing Center. It’s all about efficiently sifting through mountains of datasets to better understand healthcare service access and the health outcomes linked to various populations.
Access to health care in the U.S. isn’t all sunshine and rainbows. Factors like income, race, sex, and location can drastically change the healthcare experience for individuals. To create a more equitable landscape, Luo knows that identifying and addressing disparities is crucial. As the head of the UWM Center for Health Systems Solutions, he dives deep into electronic medical records, revealing just how far apart various patient journeys can be.
In many of his projects, Luo teams up with clinical investigators—those on the front lines of patient care—to identify and clarify these gaps. Sometimes it starts with a physician observing that a specific group responds poorly to certain treatments. “They might notice certain nuances, and we follow up by analyzing the data to see if these hypotheses hold water,” Luo explains. It’s a collaborative digging into the data.
Sometimes it’s about detection over support. Luo’s team takes on big datasets and uses machine learning to uncover unseen patterns. A recent endeavor focused on telemedicine during the COVID-19 pandemic, revealing stark variances in who used virtual care. “We processed millions of interactions, and what we found was astounding,” he said, indicating that some findings both confirmed and surprised—like how women tended to engage with healthcare professionals online more than men.
In partnership with the Medical College of Wisconsin, Luo also spearheads OTO Clinomics, a detailed study aimed at deciphering risk factors linked to ENT diseases. An earlier report he contributed to showed that patients suffering from chronic rhinosinusitis were predominantly older, more educated, white, and female—validating national healthcare access trends, but also pointing to significant gaps for others, like Black patients and those with lower incomes.
Luo doesn’t stop at just identifying problems; he’s seeking tangible improvements for patients, too. Teaming up with the National Institutes of Health, he’s part of an initiative that uses AI to engage patients in reporting their health data—think an Alexa device that nudges users at critical moments. “We wanted to create a better interface for tracking health data, something that feels natural,” he said. It’s about removing barriers and reminding us that addressing disparities isn’t just about data; it’s personal.
Luo’s work harnesses the power of AI to uncover and tackle healthcare disparities, revealing the enormous impact factors like race and location have on health outcomes. By merging his expertise in bioinformatics with real-world clinical observations, he’s not only identifying critical gaps in care but also pioneering efforts to enhance the patient experience. Through collaborations and innovative solutions, Luo sheds light on a path toward a more equitable healthcare system.
Original Source: uwm.edu