Online Master of Science in Health Informatics

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 in R essential for data scientists. It covers practical statistics from the perspective of data science in health care and how to apply various statistical methods to data analytics projects. In addition, this course introduces the most widely used statistical programming tools in data science: R. Prior experience in R is not expected in this course; however, familiarity with basic statistics concepts and modern programming language will be very useful. The approach of this course is practical, hands-on and project-oriented.

This course is an introduction to Python for students in health informatics. It aims to provide an understanding of modern computer programming and to help students learn how to use computer programs to solve real-world problems in health sciences. Students will be guided to write Python programs to process multiple types of data in health informatics, such as electronic health records, clinical data, and clinical texts. In addition, this course practices basic concepts and applications of artificial intelligence, machine learning, and natural language processing in health care using Python. The approach of this course is practical, hands-on and project-oriented.

This course introduces students to the health care system and health information management as well as the integrity of health data used by health care organizations. It also introduces 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 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 understand 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.

This course presents a practical guide to human resource management in health care, covering topics that include 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 today’s complex society.

In this course, you will examine health care’s quality movement over the last 20 to 25 years and survey the major methodologies for performance improvement. These methodologies include: the IHI Improvement Cycle, Lean (the Toyota Production System), Six Sigma, High Reliability, Positive Deviance, and the Baldrige and Shingo frameworks. In the second part of the course, you will take a deeper dive into the philosophy and tools of Lean Health care. This online course includes workshop exercises that allow you to practice core improvement skills like problem definition, process mapping, root cause analysis, setting target conditions, selecting appropriate metrics, and driving rapid cycles of experimentation. The course culminates in a final project in which you will document an actual improvement project of your choice. You can also earn a Green Belt in Lean Health care if you earn at least a B grade in the course and pass an optional final lean competency exam.

This course introduces health informatics, providing an overview of its central topics and a foundation for other courses in the master’s and certificate programs in health informatics. Students will learn the definition of health informatics, 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.

Healthcare data is generated constantly from various sources, including government offices, hospitals, research organizations, and patients themselves. This course explores tool-based data analytics and visualization to teach students how to analyze and visualize large data using software such as Excel, SPSS, Tableau, and Orange. These tools allow us to conduct desired data analysis and machine learning without programming. This course will provide an introduction to descriptive and predictive data analytics and visualization methods using those tools, along with a number of solid examples in healthcare.

This course aims to evaluate the reference terminologies 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 introduces students to computer-assisted coding technology applications and evaluates 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 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. The second half of the course shifts 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.

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, and 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, and wearable devices to 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.

This course is designed for students who want to learn data analytics tools and methods, machine learning procedures and tools, and their applications in health science. 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 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 develop the skills to analyze various types of data sets using different methods and have the ability to interpret the meaning of the results by finishing several assigned projects.

This course discusses current trends in modern health care as well as innovations that can be used in providing better patient treatments, medical research, and outcome analysis. The introduction of innovative new health care technologies will have a major impact on the industry. AI, Big Data, Novel Medicines, and Cloud infrastructure will provide environments in which both clinical medicine and research can reside and flourish. With emerging technologies, data management needs will grow. We will discuss keeping data safe, available, and usable for analytics to facilitate innovation and improve health care and patient outcomes.

This course aims to equip students with the knowledge, skills, and mindset to apply design thinking methodologies to solve real-world health informatics challenges. By the end of the course, students will have a deep understanding of the principles and process of design thinking and how it can be applied to health informatics. They will develop skills in user-centered design, including techniques for conducting user research, prototyping, and user testing. Students will apply design thinking methodologies to real-world health informatics challenges and develop innovative solutions that meet the needs of stakeholders and end-users.

Leadership and Project Management is a course designed to introduce and develop leadership behaviors as well as mastery in 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.

The ability to measure and describe issues in health informatics is vital to 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, and 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.