Youyou Tao, Ph.D.
Associate Professor of Information Systems and Business Analytics, College of Business Administration | Loyola Marymount University
Los Angeles, CA, UNITED STATES
Biography
Contact Youyou Tao at Youyou.Tao@lmu.edu.
Youyou Tao is an associate professor in the Department of Information Systems and Business Analytics in the College of Business Administration. She received her M.S. degree in information systems from the University of Washington, Seattle, and her Ph.D. in computer information systems from Georgia State University. Her research focus is on healthcare analytics and informatics. In particular, she applies the lenses of IT complementarity, business value, causal reasoning, and predictive analytics to examine the myriad intriguing issues in the healthcare industry. She has developed skills in a variety of advanced research methods including econometrics, latent growth modeling, structural equation modeling, Bayesian modeling, and social network analysis.
Education (3)
Georgia State University: Ph.D., Computer Information Systems 2018
University of Washington, Seattle: M.S. 2012
Guangdong University of Tech: B.Admin 2011
Areas of Expertise (4)
Healthcare Analytics and Informatics
Econometrics
Latent Growth Modeling
Bayesian Modeling
Courses (4)
BSAN 6030: Programming for Data Management
This course introduces learners to Python programming for data analytics. It introduces the basics of programming (algorithms, variables and data types, operators, looping and branching) and provides a working knowledge of Python libraries to process data. It includes how to retrieve, clean, manipulate, and analyze structured and unstructured data. Students will also be introduced to the basics of data management architecture such as relational databases and data warehouses, as well as use of SQL within Python for querying and interacting with such data architectures.
BSAN 6040: Data, Models and Decisions for Analytics
The course introduces students to the process of understanding, displaying, visualizing and transforming data into insight in order to help managerial decision makers make better, more informed, data-driven decisions. The course provides a basic introduction to cleaning data as well as exploring data with descriptive analytics and visualization techniques. It also provides an introduction to predictive analytics (forecasting and regression), and prescriptive analytics (simulation and optimization). The course will require the use of Excel, Tableau, and other specialized analytics and decision-making software.
AIMS 3730: Programming for Business Applications
This course is an introduction to programming with an emphasis on its business application capability. Students will learn the basic techniques of programming from concepts to code. The objectives of this course are: making students comfortable with fundamental programming terminology and concepts, including data type, input/output, control statements methods, arrays, strings and files; giving students hands-on practical experience with modeling and problem solving; and illustrating to students how such models are translated into working business applications.
AIMS 2710: Management Information Systems
This course is designed to introduce students to the key concepts in MIS (Management Information Systems) and to enhance understanding of the issues that business organizations face when developing and managing information systems. The course will examine the fundamental principles associated with IT development and management and the increasing impact of information technology in business organizations. The field is in a state of flux, so the course will also examine emerging technologies and IT trends. By completing the course, students should be better equipped to make IT decisions, to participate in IT projects, and to communicate more knowledgeably with IT experts.
Articles (10)
Impact of Telehealth on Health Disparities Associated with Travel Time to Hospitals for Patients with Recurrent Admissions: A 4-Year Panel Data Analysis
Journal of Medical Internet Research2024-11-25
This study aimed to explore the impact of telehealth in addressing health disparities associated with travel time to hospitals for patients with recurrent hospital admissions. It specifically examined the role of telehealth in reducing in-hospital length of stay (LOS) for patients living farther from the hospital.
Disclosure Patterns of Opioid Use Disorders in Perinatal Care During the Opioid Epidemic on X From 2019 to 2021: Thematic Analysis
JMIR Pediatrics and Parenting2024-10-07
The objective is 3-fold: first, we aim to identify key themes and trends in perinatal OUD discussions on platform X. Second, we explore user engagement patterns, including replying and retweeting behaviors. Third, we investigate computational methods that could potentially streamline and scale the labor-intensive manual annotation effort.
Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study
JMIR Formative Research2024-10-01
Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE.
Development of a Cohort Analytics Tool for Monitoring Progression Patterns in Cardiovascular Diseases: Advanced Stochastic Modeling Approach
JMIR Medical Informatics2024-09-24
This study aims to apply advanced stochastic modeling methods to uncover the transition probabilities and progression patterns from longitudinal episodic data of patient cohorts with CVD and thereafter use the computational model to build a digital clinical cohort analytics artifact demonstrating the actionability of such models.
Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study
Journal of Medical Internet Research2024-09-20
This paper employed a mixed-methods approach to identify underexplored, emerging, and important topics related to perinatal polysubstance use, with significant stigmas and legal ramifications being discussed on X (formerly known as Twitter).
The Association Between Social Determinants of Health and Population Health Outcomes: Ecological Analysis
JMIR Public Health and Surveillance Journal2023-03-29
This study aimed to investigate the ecological association between SDOH factors and population health outcomes at the census tract level and the city level. The findings of this study can be applied to support local policy makers in efforts to improve population health, enhance the quality of care, and reduce health inequity.
Patient Trust in Physicians Matters—Understanding the Role of a Mobile Patient Education System and Patient-Physician Communication in Improving Patient Adherence Behavior: Field Study
Journal of Medical Internet Research2022-12-20
The ultimate goal of any prescribed medical therapy is to achieve desired outcomes of patient care. However, patient nonadherence has long been a major problem detrimental to patient health, and thus is a concern for all health care providers. Moreover, nonadherence is extremely costly for global medical systems because of unnecessary complications and expenses. Traditional patient education programs often serve as an intervention tool to increase patients’ self-care awareness, disease knowledge, and motivation to change patient behaviors for better adherence. Patient trust in physicians, patient-physician relationships, and quality of communication have also been identified as critical factors influencing patient adherence. However, little is known about how mobile patient education technologies help foster patient adherence.
Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals
Journal of Medical Internet Research2022-05-26
Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals.
Functional IT Complementarity and Hospital Performance in the U.S.: A Longitudinal Investigation
Information Systems Research2021-08-15
This paper examines complementarity between clinical health information technology (HIT) applications and their effects on three hospital-level performance measures: clinical quality, experiential quality, and healthcare cost.
Addressing Change Trajectories and Reciprocal Relationships: A Longitudinal Method for Information Systems Research
Communications of the Association for Information Systems2021-08-01
This paper makes a focused methodological contribution to the information systems (IS) literature by introducing a bivariate dynamic latent difference score model (BDLDSM) to simultaneously model change trajectories, dynamic relationships, and potential feedback loops between predictor and outcome variables for longitudinal data analysis.
