Using Data Science to Confront Healthcare Inequality
It is not news that disparities exist within healthcare. Many professionals within the healthcare industry have been trying to find ways to lessen and ultimately remove those disparities, to varying success. Data scientists are now turning to machine learning and other technological advancements to reduce health disparities.
Kai Ruggeri, a behavior scientist at Columbia University’s School of Public Health, is using data science to reduce healthcare inequalities and increase healthcare access.
One project, Nudging New York, had Ruggeri collaborating with Community Healthcare Network (CHN), a federally qualified health clinic that provides care to disadvantaged residents of New York City, to determine why patients missed appointments.
For his projects, Ruggeri “uses big data and Bayesian machine learning techniques to understand what prevents many of the 80,000 CHN patients from making their medical appointments,” writes Emily Henderson for News-Medical.
Ruggeri’s research found that when patients don’t make their appointments, the community where they live ends up with a higher health burden. This is because patients with conditions that could have been treated and managed at health clinics end up in emergency rooms or hospitalized.
Using machine learning to explore the barriers to keeping an appointment, Ruggeri and his team can implement behavioral techniques, known as “nudges,” to encourage better patient choices.
“By implementing Bayesian machine learning methods to better understand patterns of behavior in these groups, we will design nudges that increase health care access for the most vulnerable New Yorkers,” says Ruggeri. “If we do it right, the methods we create can be used at community clinics across the U.S. to radically improve health care while significantly reducing cost.”
Ruggeri has made a point to ensure he is knowledgeable of financial and business analytics on top of health analytics. He notes that in the past, many data scientists were only able to focus on one or two aspects of population make-up, such as education, financial status, and health status. However, with the advent of new technology, like machine learning, a more robust picture can be created to show how various elements interact.
“We can make use of all the data and new technology to drive better outcomes for those who need it most,” says Ruggeri.
Students at Capitol Tech studying analytics and data science are well positioned to help improve AI and ML systems by studying text mining and natural language processing, advanced machine learning, data visualization, and data identification and collection strategies. With multiple large health systems and government health agencies in the vicinity, Capitol Tech is the perfect place for any student interested in pursuing health care data science to get their education.
Want to learn about analytics? Capitol Tech offers bachelor’s, master’s and doctorate degrees in analytics and data science. Many courses are available both on campus and online. To learn more about Capitol’s degree programs, contact email@example.com.