An in silico Approach to Understanding Pain Associated with the Chest Tube; The Deadly Impacts of Privatized Pharmaceutical Data
Miller, Charlotte Anne, School of Engineering and Applied Science, University of Virginia
Guilford, William, EN-Biomed Engr Dept, University of Virginia
Ferguson, Sean, EN-Engineering and Society, University of Virginia
The sensation of pain perturbs both psychological and physiological processes that comprise human existence. While both acute and chronic pain cause emotional distress, pain also interferes with biological homeostasis, quality of life, and disease recovery. Over the past few decades, medical practice has devoted more attention towards researching and addressing pain. However, the opioid epidemic has revealed the difficulty in striking a balance between successful and ethical pain management. In 2019, the National Institutes of Health launched the HEAL Initiative to focus specifically on creating and researching non-opioid-based therapies. Although the federal government has directed more funding towards alternative pain management techniques, multi-billion-dollar pharmaceutical companies have such a tight grasp on the market that it remains virtually impossible for new competitors – with effective products – to break into the field.
The following technical thesis seeks to deepen the understanding of pain associated with the chest tube and to create a virtual model of the thoracic cavity for in silico modeling. Since the difficulty of studying pain lies in its highly variable nature, the first part of the project revealed underlying consensuses about pain from the healthcare provider’s (HCP) view. In the second part of the thesis, we conducted finite element analysis on a computer model of the chest in order to simulate stress concentrations and strain deformations under external loads. The simulation results – coupled with our survey finding that 85 percent of HCPs believed that the chest tube needs improvement – indicate that a true clinical need exists and must be addressed.
The Science, Technology, and Society (STS) thesis focuses largely on the ethical and societal implications of privatizing prescription data and using it to inform pharmaceutical marketing decisions. The process of prescription “datafication” has successfully increased the revenues of brand-name pharmaceuticals at the expense of doctor’s privacy and patient’s livelihoods. When this controversial practice came before the Supreme Court, the Court held that exchanging prescriber-identifiable pharmaceutical data was tantamount to corporate expression and must be protected under the First Amendment. The STS research analyzes the prescription data mining system as one manifestation of neoliberalism within biopower, where a free-market mentality drives nation-state practices towards prioritizing the business, rather than the individual.
The technical research revealed that that medical field has neither sufficiently nor successfully addressed pain management, while the STS research focuses on how one highly-defended business practice creates a barrier to pharmaceutical market entry. Although the technical project was unable to provide a chest tube dressing that reduced pain, the creation of an anatomically-correct thoracic cavity model has wide-reaching applications for in silico modeling. The STS thesis successfully analyzed the prescription data mining system as an engrained corporate practice, leading to the rise of the corporate Frankenstein monster.
Lastly, I wish to express my profound gratitude for William Guilford, Ph.D., and Sean Ferguson, Ph.D., for guiding my technical project and STS thesis. I would also like to thank Maeve Isabella Coleman, my technical project partner, for her countless hours of work dedicated to this research.
BS (Bachelor of Science)
Computer-aided design, Finite element methods, Thoracic cavity, Neoliberalism, Biopower, Sorrell v. IMS Health Inc.
School of Engineering and Applied Science
Bachelor of Science in Biomedical Engineering
Technical Advisor: William Guilford, Ph.D.,
STS Advisor: Sean Ferguson, Ph.D.,
Technical Team Member(s): Maeve Isabella Coleman
All rights reserved (no additional license for public reuse)