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Cardiac intelligence uses artificial intelligence to monitor patients for cardiac disease and progression. The Centers for Disease Control and Prevention acknowledges that social determinants of health — such as race, finances, housing and employment — can lead to disparities in health outcomes.
Two faculty members at the University of Alabama at Birmingham Marnix E. Heersink School of Medicine’s Department of Medicine, who also serve as physicians at the UAB Medicine Cardiovascular Institute, are leveraging the power of natural language processing and artificial intelligence to help address those inequities.
“There are gaps in cardiac care that sometimes occur because of a patient’s clinical or social status,” said Julian Booker, M.D., associate professor in the Division of Cardiovascular Disease. “We saw an opportunity to close those gaps and ensure that all patients receive the highest level of care in a timely fashion.”
From Concept to Implementation
In September 2019, Booker and Efstathia Andrikopoulou, M.D., assistant professor of cardiovascular disease and radiology, developed a software algorithm that provides clinical decision support to help identify patients at risk for heart valve disease who otherwise might be overlooked.
“We focused on supporting echocardiography in Cardiovascular Imaging Services, cardiovascular surgeons, and the Structural Heart and Valve Clinic,” Booker said.
After defining and validating the algorithm and analyzing preliminary data, the clinical decision support system went live in February 2020. Now, the group is working on better identifying patients who have heart failure.
“It’s a complex disease, and we want to connect patients with our heart failure specialists and electrophysiologists in a timely manner, so that their need for defibrillators can be evaluated,” Andrikopoulou said.
Also, the team is in the final stages of developing an algorithm that helps identify cancer patients and cancer survivors who need cardiology services.
“We want to make it easier for oncologists to easily identify these patients and refer them to a cardiologist,” Booker said. “This is an exciting collaboration that’s generated a lot of interest.”
Central to the group’s work is studying the socio-demographics of UAB Medicine’s service area. After obtaining approval from the UAB Institutional Review Board, the team examined patients’ ZIP codes.
“Characteristics of certain neighborhoods correlate with poorer outcomes in patients with valvular heart disease,” Andrikopoulou said. “Among people living in neighborhoods where the average income is less than $60,000 per year, we found that more than 20 percent of the residents are Black, and residents who have inadequate access to transportation are at higher risk of experiencing faster worsening of their heart valve disease.”
Booker and Andrikopoulou extracted data based on ZIP codes. Their next step is to integrate the data with personalized patient data from UAB Medicine’s electronic health records.
“Combining both personalized and aggregate data will pave the way to understanding and providing equitable care to our patients,” Andrikopoulou said. “The decision to integrate EHR data makes the possibility of using technology to support clinicians a reality.”
Recognizing that cardiovascular issues arise across the spectrum of care, Booker and Andrikopoulou anticipate future partnerships with other UAB Medicine areas, such as surgery and obstetrics.
“When we think about cardiovascular-related conditions like atherosclerotic disease, diabetes and hyperlipidemia, we realize that we have the opportunity to optimize clinical care both in the aggregate and in individuals,” Andrikopoulou said.
A Scalable System
The team sees its work as both a proof of concept and the tip of the iceberg.
“The technology and approach we used is entirely scalable across all facets of the delivery of medical care,” Booker said. “There’s no limit to the services that can be provided across UAB Health System. It requires only patience and algorithm development.”
Andrikopoulou connects the potential of machine learning and artificial intelligence with the need for clinicians to expand their toolkit.
“This program is a prime example of how physicians cannot do it all,” she said. “We have to recognize that we can no longer be the sole agents responsible for providing high-quality care.”
According to Booker, technology has enabled quick and accurate review of charts to identify patients who may qualify for certain types of care.
“Health care delivery is a winding path, and patients occasionally slip through the cracks, either at the patient end or at the health care end,” he said. “What drives our effort is a desire to ensure that all our patients receive equitable, high-value care. If we allow the system the time it needs to understand patients, there’s no ceiling. We’re limited only by the guardrails of our imaginations.”