Can Improving Data Analysis Decrease Health Disparities?

by Sathvik Charugundla
3 mins read

While trying to develop a basic understanding of applying Big Data, artificial intelligence (AI) and machine learning (ML), I also am exploring how these novel tools can be used to better understand the social determinants of health, with a goal to reduce health disparities. Through my research I am learning how the National Academies of Sciences, Engineering, and Medicine is working with the National Cancer Institute’s Center for Biomedical Informatics and Information Technology to help develop and implement digital capabilities (biomedical informatics, scientific management information systems and computing resources) to identify and help resolve some health disparities.74, 74);” In researching efforts being undertaken by the National Cancer Institute (NCI) I am learning how health disparities are a critical community issue, with lack of equal access to services for all diseases and disorders threaten public health. NCI is working to narrow the gap between individuals who need treatment and individuals who receive treatment to identify underserved populations that can be included in clinical trials for cancer research. This effort will hopefully generate more accurate data to help all people afflicted with cancer.  Additionally, by increasing the size and diversity of clinical trials, scientists will be better able to identify barriers to care, including mistrust, stigma, transportation, or technology such as lack of internet access. As a budding data scientist, I am always interested in exploring different forums to learn how to collect quality data from multiple sources. Also, by linking quality data with demographic information, we can learn more about why some populations are at greater risk for disease and if health disparities increase such risks. Interestingly, the National Association of Engineers Grand Challenges include three medical-related challenges and goals that in turn relate to advancing the field of precision medicine care based on genetics and clinical characteristics. It is exciting to learn how AI and ML models may propel improved platforms to reduce health disparities. Yet, researchers and data scientists, individually and collectively, recognize that in order to use these new tools effectively, such data must be recognized that it “can carry bias [if’ participants are selected that don’t represent a diverse population … If an algorithm is too narrow or too broad it can unintentionally lead to false conclusions].” Moving forward, to improve data for identifying and solving health disparities, data users need to determine whether there are any within the data that could inappropriately impact the model itself, handle missing values, and be conservative to not over-filter data. As a future student majoring in computer science with interest in data science, AI & ML, I am committed to vet diverse ethical challenges and social impacts of modern technologies and how emerging tools are appropriately used to address current and emerging challenges.


Chen, I.Y., Joshi, S. & Ghassemi, M. Treating health disparities with artificial intelligence. Nat Med 26, 16–17 (2020).

National Academies of Sciences, Engineering, and Medicine 2020. Applying Big Data to Address the Social Determinants of Health in Oncology: Proceedings of a Workshop. Washington, DC: The National Academies Press.

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