Master of Science in Health Informatics: Curriculum

This course provides an introduction to the health informatics discipline, as the foundation for further study in this inter-professional /multidisciplinary field. This course traces the history of health data management and the role of the Electronic Health Record (EHR) and other clinical informatics applications in healthcare organizations. This course provides knowledge essential for self-selection of subspecialty or pursuit of general practice within the health informatics field. Emphasis is given to clinically transformative technologies, communication processes and information practices in health care.

This course discusses healthcare knowledge essential for practice in the health informatics field. The structure and function of healthcare systems in the United States and abroad are analyzed. Concepts related to management and leadership in healthcare are described. Emphasis is given to current trends in health care.

This course introduces students to the principles of computer science and software development as a foundation of health informatics. Topics include computer architecture and organization, programming languages, computer programming, data structures and algorithm design, software life cycle, and software development cycle.

This course studies the application of today’s information technology in health information management. Topics include: computer information technology infrastructure and architecture, systems interoperability, interface and integration, information assurance, knowledge management and decision support systems, and technology for communication.

Prerequisite: HINF 535

This course introduces students to database principles and database applications in health informatics. Main topics include entity relationship data model, relational model, relational database design, database queries using SQL, and database recovery and security. The course also covers emerging technologies and issues relevant to health informatics such as NoSQL, data warehousing, and data mining.

Prerequisite: HINF 540

This course explores population health from a systems and organizational perspective with an emphasis on health information technology. Research in health care is analyzed in relation to evidence-based practice, use of large databases, data mining, consumer information, health promotion and maintenance, and quality assessment. The management of health data in the achievement of healthcare organization objectives is emphasized.

Prerequisite: HINF 545

This course examines business management principles and practices essential to the health informatics field. Concepts focus on what it takes to effectively manage, budget, govern, and evaluate information technology services in a health care organization. Topics include market analysis, the budget planning process, construction and evaluation of the RPF process, financial management, project management, and communication strategies.

Prerequisite: HINF 545

This course focuses on the relationships between information and federal, state, and enterprise policy, governance, compliance, and usage. Topics such as data provenance, integrity, warehousing, and quality are explored. Emphasis is also placed on interdisciplinary management processes and organizational planning and decision-making in relation to health informatics.

Prerequisite: HINF 545

This course will examine trends impacting the health informatics field and their impact on the structure, behavior, and interactions of natural and artificial systems that store, process and communicate information. Emphasis is given to prediction of clinically transformative technologies, communication processes and information practices in health care.

Prerequisite: HINF 545

This course studies fundamental principles, concepts, and approaches regarding health care operations management, quality management, and process improvement. The systematic approach to quality includes patient safety, clinical process improvement, and credentialing.

Prerequisites: Permission of instructor, or HAD 500 or MGT 505, HAD, 501, HAD 519 and HAD 521

(Prerequisites MBA 501A, MBA 501B, and MBA 501C) Business analytics is widely recognized as a strategic weapon in today’s competitive business environment as being merely a supporting tool. As the gateway to the MBA specialization in Business Analytics, the goal of this introductory course is to provide an overview and exposure to the areas of descriptive, predictive, and prescriptive analytics. It will combine the study of key analytics concepts with hands-on exercises in data visualization and mining, statistical and predictive modeling, optimization and simulation.

Data mining refers to an analytic process designed to explore “big data” in search of consistent patterns and/or systematic relationships between variables, and to validate the findings by applying the detected patterns involved in a variety of phases that will involve data preparation, modeling, evaluation, and application. The instructor will provide hands-on demonstrations using a variety of data mining techniques (e.g. classification, association analysis, clustering, text mining, anomaly detection, feature selections) using widely adopted data mining software tools. (Prerequisites MBA 501A and MBA 501C)

This course focuses on the use of data visualization within data analysis. Students will learn what data visualization is, storytelling within data visualization and best practices using data visualization. Students will gain hands on experience using Tableau software which is a top visualization tool used in the business world today as well as a top software skill that employers in numerous fields seek. (Prerequisite MBA 501C)

This course module is intended to develop the statistical concepts and techniques that are needed to make business decisions. Topics to be covered include detailed coverage of descriptive statistics, probability theory (including Bayes’ Theorem), and discrete and continuous probability distributions with an emphasis on business applications. A survey of modern statistical methods covering sampling distributions, interval estimation, hypothesis testing, and regression and correlation analysis will be discussed.

An introduction to the quantitative approaches used in today’s businesses to solve decision problems. Topics will include overviews of linear programming, spreadsheet modeling, project scheduling, waiting line systems, and simulation.

Provides an understanding of systematic application of healthcare informatics. Integrates health informatics knowledge, principles, theories, concepts, methods, techniques, skills, competencies, values, and professionalism developed throughout the curriculum. Students work with faculty and capstone site preceptors to plan, and initiate participation in a health informatics project in a real-world setting.

Prerequisite: Completion of 30 credits in the Health Informatics Curriculum and any required specialty track courses or approval of the Health Informatics Program Director.

Provides an understanding of systematic application of healthcare informatics. Integrates health informatics knowledge, principles, theories, concepts, methods, techniques, skills, competencies, values, and professionalism developed throughout the curriculum. Students work with faculty and capstone site preceptors to participate in, summarize and evaluate a health informatics project in a real-world setting.

Prerequisite: HINF 591