In the United States, clinical testing is monitored by the federal and state governments, held to standards to ensure the safety and efficacy of these tests, as well as maintaining privacy for patients receiving a test. In order for the ABCTL to lawfully operate in the state of Arizona, it had to meet various legal criteria. These major legal considerations, in no particular order, are: Clinical Laboratory Improvement Amendments compliance; FDA Emergency Use Authorization (EUA); Health Insurance Portability and Accountability Act compliance; state licensure; patient, state, and federal result reporting; and liability. <br/>In this paper, the EUA pathway will be examined and contextualized in relation to the ABCTL. This will include an examination of the FDA regulations and policies that affect the laboratory during its operations, as well as a look at the different authorization pathways for diagnostic tests present during the COVID-19 pandemic.

As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets<br/>identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL)<br/>among business, communications, management/training, law, and clinical analysis. The first<br/>chapter of this manuscript covers the background of clinical laboratory automation and details<br/>the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The<br/>second chapter discusses the usability and efficiency of key information technology systems of<br/>the ABCTL. The third chapter explains the role of quality control and data management within<br/>ABCTL’s use of information technology. The fourth chapter highlights the importance of data<br/>modeling and 10 best practices when responding to future public health emergencies.

For our project, we explored the growth of the ASU BioDesign Clinical Testing Laboratory (ABCTL) from a standard university research lab to a COVID-19 testing facility through a business lens. The lab has pioneered the saliva-test in the Western United States. This thesis analyzes the laboratory from various business concepts and aspects. The business agility of the lab and it’s quickness to innovation has allowed the lab to enjoy great success. Looking into the future, the laboratory has a promising future and will need to answer many questions to remain the premier COVID-19 testing institution in Arizona.

For our project, we explored the growth of the ASU BioDesign Clinical Testing Laboratory (ABCTL) from a standard university research lab to a COVID-19 testing facility through a business lens. The lab has pioneered the saliva-test in the Western United States. This thesis analyzes the laboratory from various business concepts and aspects. The business agility of the lab and it’s quickness to innovation has allowed the lab to enjoy great success. Looking into the future, the laboratory has a promising future and will need to answer many questions to remain the premier COVID-19 testing institution in Arizona.

In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput biological sequencing methodologies providing snapshots of an organism’s molecular profile, biological data is rapidly increasing in its prevalence. As more biological data continues to be harvested, artificial intelligence and machine learning are well positioned to aid in leveraging this big data for breakthrough scientific outcomes and revolutionized medical care. <br/><br/>The coming decade’s intersection between biology and computational science will be ripe with opportunities to utilize biological big data to advance human health and mitigate disease. Standardization, aggregation and centralization of this biological data will be critical to drawing novel scientific insights that will lead to a more robust understanding of disease etiology and therapeutic avenues. Future development of cheaper, more accessible molecular sensing technology, in conjunction with the emergence of more precise wearables, will pave the road to a truly personalized and preventative healthcare system. However, with these vast opportunities come significant threats. As biological big data advances, privacy and security concerns may hinder society's adoption of these technologies and subsequently dampen the positive impacts this information can have on society. Moreover, the openness of biological data serves as a national security threat given that this data can be used to identify medical vulnerabilities in a population, highlighting the dual-use implications of biological big data. <br/><br/>Additional factors to be considered by academia, private industry, and defense include the ongoing relationship between science and society at-large, as well as the political and social dimensions surrounding the public’s trust in science. Organizations that seek to contribute to the future of biological big data must also remain vigilant to equity, representation and bias in their data sets and data processing techniques. Finally, the positive impacts of biological big data lie on the foundation of responsible innovation, as these emerging technologies do not operate in standalone fashion but rather form a complex ecosystem.

As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020, this virus's deadly nature has required clinical testing to meet 2020's demands of higher throughput, higher accuracy and higher efficiency. Information technology has allowed institutions, like Arizona State University (ASU), to make strategic and operational changes to combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL) among business, communications, management/training, law, and clinical analysis. The first chapter of this manuscript covers the background of clinical laboratory automation and details the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The second chapter discusses the usability and efficiency of key information technology systems of the ABCTL. The third chapter explains the role of quality control and data management within ABCTL’s use of information technology. The fourth chapter highlights the importance of data modeling and 10 best practices when responding to future public health emergencies.

This project is designed as part of the multi-student ASU Biodesign Clinical Testing Laboratory (ABCTL) thesis project sponsored and organized by Dr. Carolyn Compton, professor of Life Sciences at ASU and medical director with the ABCTL. This project divides students into teams with Business, Law, Laboratory, IT, and Documentary focused groups, with the goal of providing a comprehensive overview of the operations of the ABCTL as a reference for other institutions and to produce a documentary film about the laboratory. As a member of the IT team, this writeup will focus on quality control throughout the transfer of data in the testing process, security and privacy of data, HIPAA and regulatory compliance, and accessibility of data while maintaining such restrictions.