Visualizing and Communicating COVID-19 Data Effectively; Doctors and Digitization: Artificial Intelligence’s Evolving Role in Healthcare

Hoffman, Matthew, School of Engineering and Applied Science, University of Virginia
Qi, Yanjun, EN-Comp Science Dept, University of Virginia
Baritaud, Catherine, EN-Engineering and Society, University of Virginia

The intersection of computer science and healthcare promises a new software-driven era of disease prevention, identification, and treatment, while also raising ethical questions regarding job loss and quality of care. The technical report questions how emerging technologies may impact public health and outlines which qualities make web applications effective in the medical field. The sociotechnical thesis explores the introduction of revolutionary software within hospitals, with emphasis on artificial intelligence. The tightly coupled reports analyze the advantages and obstacles facing governments and medical systems as they incorporate new software.
The COVID-19 pandemic exposed faults in current healthcare technology systems, especially those which communicate valuable information and instructions to citizens. The increasing scope and accessibility of the internet present computer scientists with an opportunity to supplement these flawed systems. The technical project reframes data distribution applications with the design of a personalized notification system and aggregator of relevant statistics and government announcements.
The interactivity and customizability of the technical project promote active engagement with pandemic data. Therefore, the team concludes that simple, engaging tools are imperative for the success of web-based public health measures. These specifications seek to improve the public’s access and comprehension of epidemic information and serve to curb the spread of future infections.
As artificial intelligence becomes increasingly prevalent in hospitals, the sociotechnical research questions the potential effects this technology may have on doctors’ roles in delivering care and how the healthcare market should respond. Primarily, the investigation seeks to answer whether artificial intelligence should replace doctors, or if the retention of human providers should be prioritized over technical advancement. The exploration posits that the adoption of artificial programs should occur in the form of instruments working alongside physicians, instead of replacing them. This thesis is developed via a Social Construction of Technology analysis of doctor and patient perceptions of the technology. The testimonies of physicians, objectives of current artificial intelligence developers, and marketing surveys of patient preferences are highlighted in support of the thesis.
Although artificial intelligence may surpass doctors in diagnostic speed, accuracy, and cost when faced with common diseases, lack of training data makes algorithmic outcomes suffer against unique conditions. A lack of data also impacts quality of care gaps for minority communities, where algorithms cannot be properly trained to compete with doctors. From a patient’s perspective, software lacks the empathy and personal connection of traditional physicians, leading many to side with human providers or avoid artificial care. Consequently, the sociotechnical report advises limiting the application of artificial intelligence to education, communication, result validation, and administrative roles, while patient-facing responsibilities are left to doctors.
Together, the technical research and sociotechnical report demonstrate that new software is not inherently valuable. Complex or impersonal web applications do not serve populations well during pandemics, and the unmitigated use of artificial intelligence does not necessarily improve patient care. For future developments to achieve success, it is imperative to understand and serve the needs and perceptions of target demographics.

BS (Bachelor of Science)
COVID, Artificial Intelligence, Healthcare, Social Construction of Technology

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Yanjun Qi
STS Advisor: Catherine Baritaud
Technical Team Members: Evan Bernard, Edward Moder

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