Designing Therapeutics for Equity in Healthcare

Introduction
The main theme of my STS research paper was to ensure equity in access to healthcare. People who live in low-income areas are at a higher risk of developing cardiovascular diseases and are less likely to be able to get the healthcare they need because of healthcare deserts and medical price gouging. Heart disease is a global killer that affects millions of people across the world and my technical project aimed at finding more effective drug treatments for heart disease.
Project Summaries
In my STS research paper, I studied the role of bias and inequalities in healthcare. I discovered that low-income areas overlap with increased rates of death from heart disease, that racist equations are still used today for medical diagnosis and decision making, that patient care deprioritizes minorities, that bias influences healthcare at every level, and that the price of brand-name medication rises faster than the rate of inflation. I conclude that to fix these issues, there should be incentives for pharmaceutical companies and hospitals to prioritize helping marginalized communities. Further, I believe that there needs to be recurrent bias training every 3 months at minimum, harsher punishments for acting in a biased manner that causes harm, and automated methodologies in hospitals need to be re-examined thoroughly in order to move towards an equitable healthcare system. The technical portion of my thesis identified mechanisms driving heart disease and found promising treatments that may be better than the current ones. I did this by understanding the cellular behavior of neutrophils, the first responders of the immune system. To understand neutrophils, I created a computational model that computed protein interactions inside of a neutrophil. Understanding how these cells can cause cardiac inflammation advances the field of immunology and cardiovascular medicine. Using a computational model allowed me to find a large number of drug treatments that will work to treat heart disease and allowed me to prioritize treatments that are more accessible to everybody.
Conclusion
Both projects heavily influenced each other. For example, in my technical capstone project, to avoid bias, we prioritized data from mice to avoid sampling bias that would ignore large swaths of demographics. This is because, scientifically, the immune system is known to be quite different due to many factors and if I had prioritized data from white men this would not be representative of the general population. My technical project also influenced my societal research. I knew what to look for and this scientific knowledge enriched my understanding of the severity of adding coefficients to account for race to avoid giving care to specific groups of people. In terms of my technical project, the ethical significance is that I prioritized medications that are more accessible to the public and in terms of my STS project it compiled and pinpointed a wide range of issues as well as explaining how they arose, allowing for a better understanding of where to start problem solving. Together, both projects changed the way I will engineer as I will look through scientific methodologies and findings through a lens of ethical responsibility and curiosity.

Acknowledgements
I want to acknowledge my STS professor for both semesters, Richard (Doc.) Jacques, for his insightful knowledge in this subject area that helped guide my research. I would like to also acknowledge my capstone advisor, Jeffrey Saucerman, and my graduate student mentor, Mukti Chowkwale, for their sustained and enthusiastic guidance throughout my project as well as both of their commitments to my professional development after graduation.