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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

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.

ContributorsWoo, Sabrina (Co-author) / Leung, Michael (Co-author) / Kandan, Mani (Co-author) / Knox, Garrett (Co-author) / Compton, Carolyn (Thesis director) / Dudley, Sean (Committee member) / School of Life Sciences (Contributor) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

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

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.

ContributorsLeung, Michael (Co-author) / Kandan, Mani (Co-author) / Knox, Garrett (Co-author) / Woo, Sabrina (Co-author) / Compton, Carolyn (Thesis director) / Dudley, Sean (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

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

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.

ContributorsKnox, Garrett (Co-author) / Leung, Michael (Co-author) / Kandan, Mani (Co-author) / Woo, Sabrinia (Co-author) / Compton, Carolyn (Thesis director) / Dudley, Sean (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

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

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.

ContributorsKandan, Mani (Co-author) / Leung, Michael (Co-author) / Woo, Sabrina (Co-author) / Knox, Garrett (Co-author) / Compton, Carolyn (Thesis director) / Dudley, Sean (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.

Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
ContributorsMonroe, Zachary Lynn (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2020