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.
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.
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 Report documents the accomplishments of an extraordinary group of students, faculty, and staff at the Arizona state University, who participated in a year-long, multidisciplinary, first-of-its-kind academic endeavor entitled “The Making of a COVID Lab.” The lab that is the focus of this project is the ASU Biodesign Clinical Testing Laboratory, known simply as the ABCTL.
Under the direction of Dr. Carolyn Compton, a group of seven Barrett honors students have embarked on a truly unique team thesis project to create a documentary on the process of creating a COVID-19 testing laboratory. This documentary tells the story of the ASU Biodesign Clinical Testing Laboratory (ABCTL), the first lab in the western United States to offer public saliva testing to identify the presence of COVID-19.
In the first chapter, a supply chain operating model that breaks away from the traditional healthcare supply chain structures is examined. Consolidated Service Centers (CSCs) embody a shared services strategy, consolidating supply chain functions across multiple hospitals (i.e. horizontal integration) and disintermediating several key roles in healthcare supply chains such as the group purchasing organizations and national distributors. Through case studies, key characteristics of CSCs that enable them to reduce the level of supply chain complexity are examined.
The second chapter investigates buyer-supplier relationships in healthcare (i.e. supplier integration), where a high level of distrust exists between hospitals and their suppliers. This context is leveraged to study both enablers and barriers to buyer-supplier trust. The results suggest that contracting counteracts the negative effects of dependence on trust. Furthermore, the study reveals that hospital buyers may, in some situations, perceive dedicated resource investments made by suppliers as trust barriers, associating such investments with supplier upselling and entrenchment tactics. This runs contrary to how dedicated investments are perceived in most other industries.
In the third chapter, the triadic relationship between the hospital, supplier, and physician is taken into consideration. Given their professional autonomy and power, physicians commonly undermine hospital efforts in supply base rationalization and standardization. This study examines whether physician-hospital integration (i.e. customer integration) can drive physicians towards supply selection practices that align with the hospital’s sourcing strategies and ultimately result in better supply chain performance. This study utilizes theory on agency triads and professionalism and tests hypotheses through a random effects regression model applied to data about hospital financial performance and physician-hospital arrangements.