According to the , strokes are the fifth leading cause of death in the U.S., and rates are substantially higher in compared to white people.
But, a shows that Black Americans are not getting assessed properly for stroke risk because the right questions are not being asked by current artificial intelligence models used by health professionals. Researchers evaluated different algorithms used in these prediction models to look at several risk factors, like age, blood pressure and cholesterol, and then created a probability of what types of people would be at risk over the next 10 years.
is a professor of Biostatistics and Bioinformatics at and the director of. He said the data collected needs to take into account the patient's situation, like where they鈥檙e born, live, and work, because it can cause unfair treatment.
鈥淪o I think adding the patient voice into the process of risk assessment, into preventive strategies, into engagement and awareness is critical,鈥� said Pencina. 鈥淲e need to work with these patients and engage.鈥�
Pencina said updating current medical standards is important. That includes improving data collection procedures and expanding the pool of risk factors for strokes.
鈥淲e can design preventive strategies that are not as onerous as having to have the patient go see the doctor, get laboratory measurements, and just do an interview," he said. 鈥淎sk a patient a number of questions, calculate the risk, and then decide those are the higher risks.鈥�
Pencina said he hopes to collaborate with community-based organizations to collect unbiased data to make treatment decisions more equitable.
