DRAFT - Artificial Intelligence Pathway

Course Director: Yunyu Xiao, PhD
4 credits

Health informatics is the body of knowledge that concerns the acquisition, storage, management and use of information in, about and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. The discipline of health informatics sits at the intersection of several fields of research – including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. In recent years we have witnessed how the collection, storage and usage of digital health data has exponentially grown. Increases in the complexity of health information systems have driven growth in demand for a specialized workforce. This course introduces the field of health informatics and provides students with the basic knowledge and skills to pursue a professional career in this field and apply informatics methods and tools in their health professional practice.

Course Director: Yunyu Xiao, PhD
3 credits

Informatics innovations have their desired impact only when they have high quality, are highly usable, are integrated into their organizational setting, and are widely adopted and used. That makes it critical for informatics students to understand not only how informatics innovations work, but also the users and settings in which they are used. Students will learn methods and models for: measuring data and system quality; assessing and predicting technology adoption (what makes technology sticky?); improving humancomputer interaction via human factors engineering; understanding organizational and systemic challenges in the real world; influencing patients’ health behavior and decisions; and assessing quality, safety, and cost outcomes using health services research study designs. In this mixed methods course, students will gain experience using both quantitative and qualitative methods.

Course Director: Faculty
2 credit

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of 12 the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates

Course Director: Chang Su, PhD
3 credits

Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.

Course Director: Chang Su, PhD
3 credits

In addition to technical, programming and analytical skills, healthcare informaticians and data scientists need clinical domain expertise to understand and interpret real world data and analytical findings and to communicate effectively with healthcare practitioners and investigators.  This course is designed to equip informaticians with a foundational understanding of key concepts in clinical medicine, especially as they relate to the collection, application and interpretation of real world data toward clinical phenotypes and predictive analytics.   Students will learn the fundamentals of the cardiovascular, gastrointestinal, respiratory, hematological, endocrine, neurological, musculoskeletal, psychiatric, and renal systems and how diseases in these body systems are reflected in subjective and objective measures collected through patient reports, clinical observations, laboratory tests and ancillary studies.  Students will understand the clinicians approach to ordering tests to evaluate for the presence of disease.  They will also learn about the variety and classification of pharmacological therapies, the context and rationale for starting and stopping medications, and their intended and unintended effects on body systems.  Students will also learn how the physical and social environment in which patients live may impact the recognition and severity of illness, as well as  the timing, approach and outcomes of care. Students will be introduced to differentiated care in the management of different patient specialties, including pediatrics and geriatrics.

Course Director: Marianne Sharko MD, MS
3 credits

Prerequisite: Introduction to Health Informatics
Clinical information systems such as electronic health records are central to modern healthcare. This course introduces students to the complex infrastructure of clinical information systems, technologies used to improve healthcare quality and safety (including clinical decision support and electronic ordering), and policies surrounding health information technology.

Course Director: Yiye Zhang, PhD
3 credits

This course covers tools that students will need to create, manage and maximize value from big databases. The emphasis is on design and implementation of relational databases and the use of Structured Query Language (SQL). At the end of this course, students will be able to explain the requirements for handling large and complex datasets; be able to design, build, and query a relational database; and understand how relational databases and big-data targeted tools complement one another.

Course Director: Faculty
3 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation.

Course Director: Fei Wang, PhD
3 credits

Prerequisite: Artificial Intelligence in Medicine I
This class will teach students more advanced topics on AI in medicine. It requires students to have taken the AI in medicine I class. The contents of the class cover generalizability of AI models, computational fairness, model interpretation and explanation, privacy and security, federated learning, multi-modal learning, generative AI, causal inference, target trial emulation. The students will be asked to do a final project with teams based on the contents taught in the class, and python programming will be needed for doing the project.

Course Director: Yifan Peng, PhD
3 credits

This course introduces students to the field of natural language processing (NLP), applied to the health domain. NLP focuses on text data, which lacks the structure of conventional tabular data. In the health domain text is abundant in electronic health records, the medical literature and on the Web. Important applications of NLP include information extraction (pulling facts out of text) and information retrieval (searching through a collection of texts). The course presents methods for working with text: identifying the elements (words and symbols), recognizing sentence boundaries, parsing syntactic structures, assigning meaning, and establishing the structure of the discourse as a whole. The students build skills with these methods through laboratory work.

Course Director: Faculty
3 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Implementation Science and Ethics
Course Director: Chang Su, PhD
3 credits

Python for Health AI
Course Director: Chengxi Zang, PhD
3 credits

Course Director: Faculty
3 credits

Consumer health informatics (CHI) is the study of consumer information needs and technologies that provide consumers with the information they need to be more engaged in self-care and healthcare. This introductory CHI course will present an overview of theories of health and information behavior; key concepts and terminology; and main application domains. We will explore how health behavior theories 8 provide a framework for explaining consumers’ health behaviors and how CHI tools that are built with a theoretical foundation can promote health behavior change. The course will cover CHI applications in major application domains including electronic patient portals, mobile health (mHealth), and telehealth. Students will learn how to assess end-user needs and technological practices of potential users who experience health information and technological disparities. Students will also learn how to design for endusers, evaluate CHI applications and research.