Online Master of Science in Health Informatics

The Data Science track provides students with the tools to analyze, extract and present data to the health care community. With these skills, graduates can help health care organizations make well informed data-driven decisions.

The Data Science Track will:

• Teach you how to extract information from large and complex datasets, visualize it and communicate it to C-suite executives and health care providers alike.
• Enable you to help health care systems and organizations with performance improvement, especially in relation to quality measurement, patient outcomes and financial performance.

The Data Science Track is for:

• Professionals who want to learn data analysis (pattern identification, hypothesis testing, risk assessment) and prediction (machine learning models that predict the likelihood of an event occurring in the future, based on known variables).
• Those who want to manage large databases with multiple types of data from different sources.
• Those who want to use data from multiple sources to guide their decision-making.

Potential Career Paths through the Data Science Track:

• Chief Information Officers, Chief Data Officers, Chief Analytics Officers, Chief Medical Information Officers, * Chief Medical Officers, Chief Research Officers, Chief Quality Officers, Chief Actuaries
• Directors and Managers of Data and Analytics, Data Warehousing, Data Governance, Business Intelligence, Population Health, Clinical Research, Clinical Informatics, Clinical Decision Support, Financial Analytics, Process Improvement, Operations, Precision Medicine
• Data Analysts, Data Architects, Data Scientists, Data Stewards, Data Integration Architects
• Statistical Analysts, Medical Informaticists, Actuaries
• Database and Data Warehouse Developers, Administrators, Architects
• Health IT System Developers

The Health Informatics (HI) track prepares students for the evaluation and management of health information technologies and systems. Graduates of this track analyze, design and evaluate health information systems. They may also perform data analytics projects to improve the quality of health care and reduce the cost of health care services.

Health Care Supervision and Management (HSM), a track in the Master of Science in Health Informatics program, provides students an opportunity to acquire advanced skills in administration and supervisory management in health care, long-term care, and rehabilitation. Students in this track develop the combined skills of health care management and organizational leadership. With these skills, graduates can implement their leadership and business acumen to enact change in a complex and dynamic health care landscape.

The Registered Health Care Information Administrator (RHIA) track gives students the academic accreditation necessary to sit for the RHIA exam, which will allow students to serve as an integral link between care providers, payers and patients. With these skills, graduates can help manage protected health information, including its input, security, transmission and storage.

The RHIA track:

• Centers around health information management – including revenue cycle, privacy and security and clinical documentation requirements (content that goes into electronic health records).
• Provides graduates with the skills to create and manage databases, oversee organization of data and ultimately act as the business intelligence component of a health care organization.
• Helps professionals in any health-related area become directors, managers or team leaders in various health care settings.
• Enables graduates to develop the interpersonal and interdisciplinary skills needed to work alongside multiple people and teams throughout the healthcare organization, communicate with C-suite executives and ultimately take on leadership roles.

The RHIA Track is for:

• Professionals looking to gain specialized experience and be promoted in their field.
• Anyone whose goals include leading and supervising teams or departments, from any background (related or not).
• Those who want to work as a critical link between care providers, payers and patients, specifically:
  • Becoming an expert in managing patient health information and medical records, administering computer information systems, collecting and analyzing patient data and using classification systems and medical terminologies.
  • Possessing comprehensive knowledge of medical, administrative, ethical and legal requirements and standards related to health care delivery and the privacy of protected patient information.
  • Managing people and operational units, participating in administrative committees and preparing budgets.
  • Interacting with all levels of an organization – clinical, financial, administrative and information systems – that employ patient data in decision-making and everyday operations.

Potential Career Paths:

Job opportunities for RHIAs exist in multiple settings throughout the health care industry. These include the continuum of care delivery organizations, including hospitals, multispecialty clinics and physician practices, long-term care, mental health and other ambulatory care settings. The profession has seen significant expansion in nonpatient care settings, with careers in managed care and insurance companies, software vendors, consulting services, government agencies, education and pharmaceutical companies.

• Health administrators
• Project managers
• Systems analysts
• EHR implementation specialists
• Consultants
• Product managers
• Auditors (abuse and fraud)
• Quality control specialists
• App developer
• Executive level or leadership roles

• HI 2020 – Practical Statistics and Programming Using Python & R (3 credits – required)
• HI 2451 – Database Design and Big Data Analytics (3 credits – required)
• HI 2454 – Data Science in Health Informatics (3 credits – required)
• HI 2453 – Data Analytics and Machine Learning in Health Science (3 credits – required)

• HI 2450 – Security, Privacy, Legal & Ethical Issues in Health IT (3 credits – required)
• HI 2210 – Health Information and the Health Care System (3 credits – required)
• HI 2452 – Digital Health (3 credits – required)
• HI 2020 – Practical Statistics and Programming Using Python & R (3 credits – recommended elective) or
• HI 2650 – Practical Research and Evaluation Methods (3 credits – recommended elective)

• HI 2210 – Health Information and the Health Care System (3 credits – required)
• HI 2632 – Leadership and Project Management (3 credits – required)
• HI 2231 – Talent Management and Human Resources (3 credits – required)
• HI 2230 – Financial Management and Health Care Reimbursement (3 credits – recommended elective)

• HI 2210 – Health Information and the Health Care System (3 credits – required)
• HI 2410 – Health Vocabulary, Terminology & Classification Systems (3 credits – required)
• HI 2230 – Financial Management and Health Care Reimbursement (3 credits – required)
• HI 2452 – Digital Health (3 credits – recommended elective)

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 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.