Finding the continuum of risk
Holly Ende, MD, an obstetric anesthesiologist at Vanderbilt University Medical Center, Nashville, Tenn., said approaches that leverage electronic health records to identify women at risk for hemorrhage have many advantages over currently used tools.
“Machine learning models or statistical models are able to take into account many more risk factors and weigh each of those risk factors based on how much they contribute to risk,” Dr. Ende, who was not involved in the new studies, told this news organization. “We can stratify women more on a continuum.”
But digital approaches have potential downsides.
“Machine learning algorithms can be developed in such a way that perpetuates racial bias, and it’s important to be aware of potential biases in coded algorithms,” Dr. Li said. To help reduce such bias, they used a database that included a racially and ethnically diverse patient population, but she acknowledged that additional research is needed.
Dr. Ende, the coauthor of a commentary in Obstetrics & Gynecology on risk assessment for postpartum hemorrhage, said algorithm developers must be sensitive to pre-existing disparities in health care that may affect the data they use to build the software.
She pointed to uterine atony – a known risk factor for hemorrhage – as an example. In her own research, she and her colleagues identified women with atony by searching their medical records for medications used to treat the condition. But when they ran their model, Black women were less likely to develop uterine atony, which the team knew wasn’t true in the real world. They traced the problem to an existing disparity in obstetric care: Black women with uterine atony were less likely than women of other races to receive medications for the condition.
“People need to be cognizant as they are developing these types of prediction models and be extremely careful to avoid perpetuating any disparities in care,” Dr. Ende cautioned. On the other hand, if carefully developed, these tools might also help reduce disparities in health care by standardizing risk stratification and clinical practices, she said.
In addition to independent validation of data-based risk prediction tools, Dr. Ende said ensuring that clinicians are properly trained to use these tools is crucial.
“Implementation may be the biggest limitation,” she said.
Dr. Ende and Dr. Li have disclosed no relevant financial relationships.
A version of this article first appeared on Medscape.com.