Jhinuk Barman advances cancer research through data science

In the mission to treat and cure cancer, data is plentiful—x-rays, appointment notes, trial results, and more. But when that data exists as billions of pieces of paper scattered across offices and filing cabinets, progress slows to the pace of manual labor. Critical information falls through the cracks, directly impacting patients' access to care.

Jhinuk Barman, B.A. ‘19, is tackling that problem. As a data scientist at the UC Davis Comprehensive Cancer Center, Barman is automating the management of data to support researchers, doctors, and patients. A key focus of her work is improving the success of clinical trials by optimizing patient enrollment and retention.

“Cancer research is critical to developing better treatments, so I use data to ensure we’re enrolling the right number and type of patients in clinical trials,” says Barman. “I analyze patient enrollment across different illness types–like brain or breast cancer. I also track where patients are coming from to ensure those in underserved communities have access, especially since some of these trials could potentially save lives.”

The interdisciplinary approach of UC Berkeley's data science B.A., one of the degrees available through the College of Computing, Data Science, and Society, prepares graduates to address pressing challenges across industries. But while data science and AI are poised to transform healthcare, Barman sees firsthand the need for more professionals with data and tech expertise in the field. As one of the only data scientists in her organization, she takes on a wide range of data management and analysis tasks. Her role is dynamic and rewarding, yet she recognizes how much more could be achieved.

“Right now, a lot of data entry is still done manually, which slows things down. With data engineering, we can introduce databases, automate analysis, and store information in the cloud—making it much easier for healthcare employees to access and use that data to improve patient care,” says Barman.

With understaffed hospitals, integrating data science with biomedical tools could bring significant improvements to patient care. For instance, natural language processing can automate clinical notes, which can then be analyzed against vast datasets from clinical trials and medical records. These models could predict diagnoses and suggest treatment options, allowing doctors to make informed decisions more efficiently.

Barman’s perspective on healthcare data transformation was shaped by her early experiences in the field—particularly during the 2020 pandemic. She saw firsthand the challenges created by outdated technology. As hospitals filled up, real-time information was desperately needed. But in an environment that still relied heavily on fax machines, crucial data was slow to reach the people who needed it most. 

While the pull of private-sector tech and finance roles is strong, many alumni, like Barman, are choosing to apply their skills where they can make the greatest impact. After spending a few years in the tech industry, Barman felt a calling to return to healthcare.

“In my first role after undergrad at UC San Francisco, we'd get patient feedback and I could see how my work was impacting real people,” says Barman. “When people think about making a difference in patient care, they often consider becoming doctors. But data scientists have a unique ability to transform healthcare in ways that go beyond one-on-one interactions. I wanted to go back to where I was really needed.”

Interested in hearing from more UC Berkeley alumni using data science in the healthcare space? Listen to our podcast episode, Data Science Graduates in Healthcare on Spotify, Apple Podcasts, or wherever you listen!