CAROLINA HEALTH INFORMATICS PROGRAM (CHIP)

In this course, students will be introduced to patient engagement, population health, digital therapies; learn about interoperability standards driving data sharing; review the regulatory bodies defining standards of care, along with understanding the privacy and security laws governing the use of health care data. The course includes a project prototyping and pitching a digital health solution. We will hear from industry experts who will participate as guest lecturers with opportunities for students to ask questions.

Exploration of an introductory-level special topic not otherwise covered in the curriculum. Previous offerings of these courses do not predict their future availability; new courses may replace these.

Introduction to the systems approach to the design and development of information systems. Methods and tools for the analysis and modeling of system functionality (e.g., structured analysis) and data represented in the system (e.g., object-oriented analysis) are studied.

Exploration of a special topic not otherwise covered in the curriculum, at an intermediate level. Previous offering of this course does not predict future availability; new courses may replace these. Topic varies by instructor.

Study by an individual student on a special topic under the direction of a specific faculty member. Six credits maximum for master's students. Graduate faculty. Permission of the instructor.

Students will learn the basics of programming and collaborative coding in a health informatics environment. The Python programming language will be primarily used, though the basics of programming is transferable to many other languages. We will learn by doing. Expect to read and write code in every class, and to have applicable exercises and small projects working with synthetic and real-world data.

This course is focused on Descriptive analytics primarily. Descriptive analytics is the most basic type of analytics organizations use to measure performance year over year by monitoring their KPIs (Key performance indicators). In this class the focus will be on data profiling, querying data using Google's Big Query, Data analysis, and reporting in excel, testing, and validation, project life cycle, data visualization in Google Studio, etc. This course will provide an overview of Data Analysis and reporting, via Big Query and Excel. The course will also review several use cases in healthcare.

With the advent of Electronic Health Records (EHR) there are more opportunities than ever to make a real impact on patient health and the science of medicine using data. However, clinical data has unique characteristics and structures that can make it both challenging and rewarding to use in analytics. In this course, students will gain understanding of clinical data collection, models, context, and caveats through lectures and hands-on activities. Students will then apply that knowledge to real clinical data and use Python and other tools to perform analyses and replicate findings from literature. Knowledge of SQL and Python are required.

Introduction to the systems approach to the design and development of health information systems. Understanding systems analysis in healthcare. Methods and techniques for the analysis and modelling of system functionality (e.g., structured analysis) and data represented in the system (e.g., object-oriented analysis) are studied.

This course explores the critical intersection of human factors and ergonomics (HFE) and artificial intelligence (AI) within the healthcare domain. It emphasizes the importance of a human-centered approach to designing, developing, and implementing AI solutions in healthcare settings. The course will delve into key HFE principles and methods, including cognitive workload management, usability, human-AI teaming, explainable AI (XAI), safety, ethics, and patient acceptance, and their application to a wide array of healthcare scenarios. Students will gain a comprehensive understanding of the challenges and opportunities presented by AI in healthcare and learn how to leverage HFE to create AI systems.

This course leverages the Lean Six Sigma framework to analyze and solve problems as related to quality improvement projects. Students in this course will apply the Lean Six Sigma philosophy and goals to build problem-solving, analytical and technical skills while implementing successful change management techniques. Projects will be completed in the healthcare context.

In this course, students will get hands on experience using data visualization tools with the focus on Tableau software. Students will gain an understanding of Tableau's fundamental concepts and features: how to connect to data sources, use Tableau's drag-and-drop interface, and create compelling visualizations. The course includes a project prototyping using Tableau software for data visualization. We will also hear from data visualization experts who will participate as guest lecturers with opportunities for students to ask questions. The course also includes basic concepts of quality improvement with special emphasis on healthcare applications.

This course introduces students to the breadth and complexity of the US health care system. We will look at the functioning parts of how and who delivers health care. We will also examine the emergence, response, and outcomes of the COVID-19 pandemic in the US.

Focuses on EHR data standards with emphasis on data management requirements, applications, and services. Course includes HL7, CCHIT, and CDISC standards. For data management specialists, administrators, and health data analysts.

Students will learn the basics of setup, administration, and querying relational databases using SQL to perform CRUD operations: Create read Update Delete. We will also cover some basic information about indexing and normalization to improve performance, efficiency and stability.

Basic programming concepts are introduced using JavaScript, a client-side language that runs in browsers to facilitate user interaction and dynamic real-time updates. A crucial component of the modern web design and workflow, it can be used to enhance user experience on a website, display complex visualizations like charts and maps, and communicate with servers in the background while navigating a website. This course will start with basics like variables, functions, classes, loops, conditionals, and Ajax. It will then will proceed to work with tools like charting and mapping libraries, as well as jQuery, React, Vue and other frameworks.

An introductory course in statistics intended for students interested in healthcare research. Topics discussed include displaying and describing data, the normal curve, regression, probability, statistical inference, confidence intervals, and hypothesis tests with applications in the real world. This course prepares students to (a) better comprehend results presented in journal articles and research reports and (b) conduct statistical analyses for methodology courses as well as independent research (e.g., theses). This course covers the analysis of data from experimental and nonexperimental research in healthcare and allied fields, emphasizing the application and logical nature of statistical reasoning.

This advanced course in inferential statistics covers the practical application of statistical analysis with emphasis on healthcare. Instruction includes an examination of the role of statistics in healthcare research; understanding statistical terminology; use of appropriate statistical techniques; and interpretation of findings. Analytic procedures covered will include regression diagnostics, mediation, moderation, generalized linear models, and factor analysis. The focus will be on applications of advanced data analytic techniques and developing skills at using software. This course is recommended for students who have taken an introductory course to statistics or have knowledge of basic statistical analysis techniques.

The course focuses on developing an understanding of current and future directions for the use of information technology to improve the health and health care of patients in the U.S. health system and beyond. This series explores key areas in Health Informatics and includes research results, overview of programs of research, and evaluative projects. Speakers with extensive informatics experiences and knowledge from both academia and industry are invited to present.

The health informatics internship is a course designed to expand classroom learning to include "hands-on" experience with an industry partner in health care or health information technology. The main aim of the course is to provide the students a practical learning opportunity in Health IT deployment, data collection and management, and data analysis.

Exploration of an advanced special topic not otherwise covered in the curriculum. Previous offering of these courses does not predict their future availability; new courses may replace these.

Individual work on the doctoral dissertation under the guidance of the student's dissertation advisor. Graduate students must have completed their pillar and elective coursework requirements. A doctoral degree check with program coordinator must be completed before a student can be enrolled in CHIP 994. Doctoral student standing and permission of instructor required.