Online Master of Science in Health Informatics

• HI 2250 - Foundations of Health Informatics (3 credits)
• HI 2454 - Data Science in Health Informatics (3 credits)
• HI 2453 - Data Analytics and Machine Learning in Health Science (3 credits)
• HI 2999 - Independent Study (1 credit)

• HI 2450 - Security, Privacy, Legal & Ethical Issues in Health IT (3 credits)
• HI 2210 - Health Information and the Health Care System (3 credits)
• HI 2452 - Digital Health (3 credits)
• HI 2999 - Independent Study (1 credit)

• HI 2210 - Health Information and the Health Care System (3 credits)
• HI 2632 - Leadership and Project Management (3 credits)
• HI 2231 - Talent Management and Human Resources (3 credits)
• HI 2999 - Independent Study (1 credit)

• HI 2210 - Health Information and the Health Care System (3 credits)
• HI 2410 - Health Vocabulary, Terminology, and Classification Systems (3 credits)
• HI 2230 - Financial Management and Health Care Reimbursement (3 credits)
• HI 2999 - Independent Study (1 credit)

This course covers multiple topics, including an introduction to the structure of human cells, tissues, organs and organ systems, and functions associated with them; a general overview of the predisposing factors and direct causes of disease, as well as their effects on the human body; an introduction to pharmacology; and word construction, pronunciation, spelling, definition and use of terms related to all areas of medical science, hospital service, and health related professions.

This course provides an introduction to statistical methods and computer programming that are essential for data scientists. This course covers fundamental statistics from the perspective of data science in health care and how to apply various statistical methods to data science. This course provides introduction to two of the most widely used statistical programming tools in data science: R and Python. Prior experience in R and Python is not expected in this course, however, familiarity with basic statistical concepts and modern programming language will be very useful. The approach of this course is practical, hands-on, and project oriented. The goal of the course is to provide students with skills for solving various problems in health informatics using R and Python.

This course aims to evaluate the reference terminologies that are currently used in health care settings (SNOMED-CT, LOINC) as well as applications of data capture technologies (such as natural language processing, voice recognition, and document imaging). Further, the course intends to introduce students to computer-assisted coding technology applications and evaluate the use of health care terminologies, vocabularies, and classification systems found both nationally and internationally. A more focused review on format, guidelines, and application for ICD-10-CM, ICD-10-PCS, and HCPCS/CPT will take place. Students will utilize online coding tools to obtain hands-on practice with each of these three systems.

This course will introduce the student to the health care system and health information management as well as the integrity of the health data that is used by health care organizations. It will also introduce the student to the importance of the quality of health care and health information and the need for risk management and patient safety in health care organizations.

This course introduces health informatics, providing an overview of the important topics and a foundation for other courses in the master’s and certificate programs in health informatics. Students will learn what health informatics is, the challenges in health informatics, and the careers in the profession of health informatics. Students will be briefly introduced to important concepts in health informatics including computing and networking in health informatics, digital health, data analytics, machine learning, data science, health technology intervention, electronic health record, health care standards, privacy and security, bioinformatics, and digital imaging.

This course is designed to introduce basic knowledge of security, privacy, legal and ethical issues in health information systems. In the first half of the course, the major focus will be on security techniques, including basic concepts, encryption and decryption methods, program operating systems, databases, and network security. In the second half of the course, we will shift our attention to the application of the concepts of privacy and security in the health care industry. Legal and ethical issues around the privacy and security of health information will also be discussed.

This course is a practical guide to human resource management in health care and covers topics such as recruitment, compensation and benefits, training, discipline, termination, legal issues, labor unions, and multi-human interactions that emerge in the workplace. It is also an overview of the laws and regulations that guide every decision related to managing people in the complex society in which we exist today.

Leadership and Project Management is a course designed to introduce and develop leadership behaviors as well as manage the project management process. The goal of this course is to advance the capabilities in meeting current health care organizations’ challenges through both leader and leadership development. It is not intended to provide a background on the evolution of thought on leadership or leadership theory. The course takes a developmental – not training – approach by providing a leadership development framework that helps guide one in acquiring the characteristics and behaviors associated with good leadership. This course will study how technology, people, and economics of software projects interact and the impact these elements have on managing software projects.

This class will explore the concepts of process improvement methodologies including Six Sigma and Lean/Toyota Production Systems and their application to health care. The student will gain a foundational understanding, and with additional study and requirements, may be prepared for certification in Six Sigma.

This is an introductory course to data science. Data sources, types, collection methods, processing and analysis methods, and analysis result interpretation will be covered in this course. Students will learn these skills by finishing several assigned projects. By finishing the course, students will have the skills to analyze various types of data sets using different methods and have the ability to interpret the meaning of the results.

This course is designed for students who want to learn data analytics and machine learning, as well as their applications in health science. Data analytics tools and methods, machine learning procedures and tools, and their applications in health science will be discussed in this course. Students who finish this course should be able to use statistical and machine learning methods and tools to analyze various types of data sets in health science, as well as make predictions and recommendations according to the obtained results.

This course introduces key principles of financial management and reimbursement in the health care industry. The aim is to provide an understanding of financial and reimbursement language, concepts, and processes to enhance the daily management performance of current and future leaders in health care.

Students will be offered tools to develop a working knowledge of financial statements, dashboard metrics, and industry financial issues. Students will learn reimbursement methodologies, health care compliance regulations, and tools for managing all aspects of the revenue cycle. Course modules and exercises offer insight into the development of operational budgets, understanding financial trends and variances, developing financial negotiating strategy, quantifying productivity, interpreting reimbursement analytics, ensuring compliance, understanding health care reimbursement, and managing the revenue cycle.

The ability to capture, organize, and analyze data is critical to health care organizations and to biomedical research. Database is arguably the most important tool in supporting data capture, organization, and analysis of outcomes, both for regular and transactional data as well as big data. This course will provide students with opportunities to learn data and big data management skills, including principles in database and approaches to manage data, as well as offer a place to discuss and analyze key issues in data management. The course will have two sections. The first section will emphasize the theoretical and technical aspects of database, specifically in data modeling, design of database, as well as learning the “language” to manage database. The second section will address advanced topics in database and big data, including transforming data, data warehousing and data mining, and analysis and visualization of data.

The advancement of informatics in health has allowed a convergence of digital technologies with health and health care, with the goal of improving the efficiency and effectiveness of health services as well as making medicines more personalized and precise. These technologies include solutions for both health care providers and patients, ranging from Electronic Health Records, Telemedicine, Mobile Health, Wearable Devices, as well as many emerging Internet-of-Things devices and sensors. Interventions through digital health require a multidisciplinary domain team involving both informatics and social sciences. This course will provide students with opportunities to explore; analyze, and discuss key issues, principles (both from technical and social science perspectives), approaches, and policies surrounding digital health. The final assignment of this course will task students to work with key health service stakeholders (who will share their knowledge throughout the course as well) to investigate and propose a practical digital health solution to tackle a specific health issue.

The ability to measure and describe issues in health informatics is vital in the process of decision-making in health care. The process of evaluation itself depends on the questions and the contexts in which the issue arises. This course will study the methods of evaluation in health informatics, specifically the practical application of well-established research and evaluation techniques to problems in health informatics. Different situations in health care will necessitate the use of different evaluation methods, therefore in this course, students will be exposed to approaches to develop measurement instruments, design studies, analyze results, as well as present the results of the study in health informatics evaluations. Case studies will be used to facilitate active learning, including conducting usability evaluation and cost evaluation of health informatics resources. By the end of the course, students will be able to develop a working evaluation “contract,” which can be used to guide the evaluation process as well as to validate the result of the evaluation process itself (that the result is indeed what the stakeholders want and need to know to make a decision).

Supervised practical experience or capstone project providing an opportunity for students to apply previously learned skills and theories in the field of Health Informatics.

Independent study provides students an opportunity to explore, in depth, an area of particular interest to them. It is the student’s responsibility to find a faculty member willing to undertake such a tutorial.