The field of biomedical informatics is rapidly emerging and holds promise to transform the way physicians prevent, diagnose and treat disease. By employing biostatistical methods, researchers can examine and interpret large datasets, including electronic medical records (EMR) that are integral in ensuring quality patient care. These datasets include information from thousands of patients, allowing researchers to identify patterns and gaps in current medical care. 

At the forefront of the field is Laura Wiley, PhD, an associate professor in the CU Department of Biomedical Informatics and Ludeman Center researcher. Dr. Wiley has dedicated her career to harnessing the power of EMRs for precision medicine. She uses computational phenotyping, which is a process where you apply computer algorithms to clinical data to find groups of patients with a specific disease. This information can be used for research and to determine optimal treatment paths for patients. 

As a trained chemist, programmer, data scientist, and self-titled ‘data detective,’ Dr. Wiley studies patterns in EMRs. This is especially significant for women, who historically have been neglected in research studies despite their biological differences. It is also important for women because, in too many instances, they are underdiagnosed and are not receiving optimal care.   

“By identifying women during research, we are learning from all the patients, not only a subset,” she says. “I want to use technology and innovation in medical practice to ensure that women receive the best therapies and care for all conditions.” 

In 2022, Dr. Wiley received the Ludeman Center Early-Career Faculty Research Development Award for her project, "EmPoW-HER: Equitable Phenotyping for Women’s Health Research.” Historically, women have been left out of heart disease research even though women comprise 51% of heart failure patients nationally. Now that EMRs are being used to identify patients eligible for clinical research studies, it is critical that these algorithms do not have a similar bias. Dr. Wiley conducted a systematic review of the literature and found that nearly all computational phenotyping algorithms in scientific publications identified more men than women with heart failure — an alarming national trend. This left out data specific to women, hindering efforts to use informatics to improve care for women with heart failure. Without having the proper information about women with heart failure, they are receiving suboptimal care. 

Her path forward was clear.  

“How do we consider existing health disparities in algorithms to make sure that every patient, regardless of gender, gets equal benefit from EMR’s?” Dr. Wiley said. She is now focused on creating new algorithms that correctly identify patients, so the data is useful in improving the quality of care for all patients.  

Dr. Wiley is now applying for grant funding from the National Institutes of Health to further her research regarding the different subtypes of heart attacks and how women present with different symptoms. This will help her develop impactful solutions to improve women’s health outcomes. 

“I am excited by the idea of using data to understand the optimal choices for treatments,” says Dr. Wiley. “We have the responsibility of learning from every patient to inform the care of the next patient while considering their biological differences each step of the way.”