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Sathvik Charugundla

Sathvik Charugundla

EducationFeatured

Combat Disparities Through Improved AI Tools

by Sathvik Charugundla July 30, 2022
3 mins read

By researching health disparities, I am learning that there are disparities in access to health insurance and healthcare, quality of care received based on geographic regions, and real biological differences. For example, University of Michigan Medicine researchers have been studying racial disparities in the healthcare system to gain a better understanding of how complex factors create differing health outcomes for Black and white Americans. With this research I am learning how the use of artificial intelligence (AI ) in healthcare helps combat some of these disparities.

 

Researchers note that Black patients tend to report suffering more intense pain than white patients but are less likely to undergo knee replacement surgery. Interestingly, U-Michigan researchers are training a new algorithm to read knee X-rays for patients with arthritis found that their AI program performed more efficiently in diagnosing Black patients’ reported pain than human radiologists. 

 

Yet, my research revealed that some AI tools have increased disparities rather than combating them. For example, an algorithm previously used by a health insurance company to predict future healthcare costs and recommend patients for care actually reduced care provided for Black patients from 50% to 20%. As AI and ML algorithms scale, they take more repetitive tasks, so inequities included in these algorithms can increase as well. As these algorithms used cost as an endpoint, the algorithm was faulty, even though it was intended to be theoretically race-blind.  In contrast, the algorithm researchers trained on knee X-rays was “trained on patient reports of pain” so as Black patients reported higher levels of pain with clinically equivalent images, AI-assisted Xray readings were improved and resulted in more accurate diagnosis of disease patterns and in turn, led to better care for Black patients.

 

 

References:

Adnan Asar “AI Could Reduce Racial Disparities In Healthcare” Forbes Technology Council 1 Oct 2021 online.


Jordyn Imhoff “Health Inequality Actually Is a “Black and White Issue” University of Michigan Health 3 June 2020 online.

Wired “New Algorithms Could Reduce Racial Disparities in Health Care: Machine learning programs trained with patients’ own reports find problems that doctors miss—especially in Black people” 25 Jan 2021 online.

July 30, 2022 0 comment
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Featured

Can Improving Data Analysis Decrease Health Disparities?

by Sathvik Charugundla May 14, 2022
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.

References:

Chen, I.Y., Joshi, S. & Ghassemi, M. Treating health disparities with artificial intelligence. Nat Med 26, 16–17 (2020). https://doi.org/10.1038/s41591-019-0649-2

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. https://doi.org/10.17226/25835.

May 14, 2022 0 comment
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Featured

CARE FOR OUR FRONTLINE VETERANS

by Sathvik Charugundla April 16, 2022
3 mins read

As a US Air Force Civil Air Patrol cadet, I periodically volunteer at veteran events. Recently, I became aware that New York has the fifth-largest veteran population in the US (840,000+ veterans), of which 7% are women at 50% are over age 65, and the majority of veterans in New York prefer to receive their health care from outside of the VA.

 

When researching this issue further, I learned that as our veterans are a vulnerable and growing population, the National Academies of Sciences, Engineering, and Medicine; Health’s Board on Population Health and Public Health is now focusing on several efforts to promote health equity for our military members and our veterans who often experience chronic trauma from their service as well as socioeconomic disadvantages post-deployment that influence their physical and mental well-being – conditions resulting in healthcare disparities.

 

Several factors contribute to veterans’ poor health and mortality, including higher rates of suicide, homelessness, and mental health issues.  The VA recently examined suicide rates among VA-enrolled veterans from all states (in 2014) and found VA-enrolled veterans account for approximately 18% of suicide deaths among US adults, with higher suicide risk noted among younger veterans (57% higher than rate among active-duty military personnel). Likewise, mental illnesses, including PTSD, depression, substance abuse, and sexual trauma, are more prevalent among the veteran population.

 

Per the National Health and Resilience in Veterans Study (2013), disparities related to access to and use of healthcare and prevalence of chronic diseases are also present in the veteran population. A review of studies examining racial and ethnic health care disparities in the VA found that relative to white veterans, African American veterans experience lower levels of arthritis and cardiovascular disease management, lower levels of surgery related to cancer and cardiovascular disease, and lower quality of diabetes-related care. Similarly, veteran homelessness is a staggering issue affecting veteran health.

 

 

References:

NYS Health-produced snapshot, see New York State Health Foundation at nyshealthfoundation.org.

National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Population Health and Public Health Practice; Committee on Community-Based Solutions to Promote Health Equity in the United States; Baciu A, Negussie Y, Geller A, Communities in Action: Pathways to Health Equity. Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The State of Health Disparities in the United States. Available from: https://www.ncbi.nlm.nih.gov/books/NBK425844/.

April 16, 2022 0 comment
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