This course offers the fundamental principles, concepts, and technological elements that constitute the segments of health informatics and how technology can be applied in health care delivery. This course allows students to gain general perspectives from interdisciplinary and the competencies for graduate education in informatics. The topics covered in this course will include the definitions of informatics and related terms, computational systems thinking, information science and effective use of biomedical data, application of health information technology to healthcare, understanding of technological standards and informatics terminology, current issues in informatics, approach in problem-solving, and decision-making strategies. The knowledge and skills presented in this course will prepare students to progress to more advanced courses throughout the program curriculum. Students will engage in activities to explore the techniques in assessing current information practices, determining the information needs of health care professionals and patients, and effectively managing information using computer technology.

Timely sharing of accurate health information plays a key role in continuation of patient care and informed decision making in clinical settings. This course provides an in-depth overview of health information technology standards and terminologies that are essential for clinical data interoperability and exchange across diverse systems. Details of clinical data structure, relevant standards, terminologies, nomenclatures and ontologies, and clinical data interchange will be explored. Students will be also exposed to current and emergent standards such as  FHIR or HL7 and how these standards are applied to achieve integrated health data interoperability and information exchange in healthcare settings. An overview of healthcare information network architecture and security as it applies to HIPAA regulation will also be provided in this course.

This course aims to impart a critical perspective of, and an empirical familiarity with, the range of methods available to researchers in the field of Health Informatics. Beginning with a review of basic issues regarding the formulation of research questions, research design, data collection and analysis, this course will provide an opportunity for you to advance your understanding of research through critical exploration of research language, ethics, and approaches. The course introduces the language of research, ethical principles and challenges, and the elements of the research process within quantitative, qualitative, and mixed methods approaches. Three themes will be explored: 1) the various advantages and disadvantages of each method, 2) when the use of one method is appropriate or inappropriate for the research question, and 3) how to evaluate researchers’ claims on the basis of the evidence they present. These themes will be explored by reading examples of each of these methods as you gain experience using them on a paper that explores a topic of their choice. Emphasis is placed on identification of the core validities relating to experimental design, use of control techniques for relevant (confounding) variables and the relation between design and statistical power. The purpose of this course is to build on basic research skills and concepts.

This course provides conceptual and practical knowledge of the healthcare information systems (HCIS), emphasizing the implementation of electronic health record systems (EHRs), infrastructures, evaluation methods, and maintenance approaches in diverse healthcare delivery settings. The breadth of material covered in this course includes reviewing and analyzing the EHRs project planning, vendor selection, design, implementation, maintenance, associated clinical information system components, health information exchange and regulatory provisions, application of decision support systems, and interface methods standards. Also, students will engage in practical clinical workflow analysis, evaluation methods, and provision of different healthcare users’ roles in the EHR implementation. The course will highlight leadership concepts and communication models in EHR systems operations.

Due to advancement and wide adoption of health information technologies, there is a proliferation of health-related data generated by various sources. Successfully capturing and storing these data in electronic format play a critical role in further health related application development and decision making. This course is an overview of the fundamentals of relational database design and management. This course covers topics in entity-relationship (ER) modeling, database architecture, functional dependencies and relational design, relational algebra for basic query structure, and query design. Special emphasis is on Structured Query Language (SQL), query processing and basic database application development using Oracle relational database management systems (RDBMS).

The role of the patient and consumer as a full partner in health promotion and disease management has never been of greater importance in the wake of the pandemic. Consumer health informatics (CHI) is the branch of medical informatics that: a) analyses consumer needs for information, b) studies of making information accessible to consumers and c) models and integrates consumers’ preferences into health information systems and a broad-range of patient applications. A central tenet of consumer health informatics is that although not all patients will participate, patients participating in their health care leads to higher quality care than that which is achievable without patient participation. In this course, students will become familiar with a range of CHI applications across platforms. The course emphasizes a multi-disciplinary and user-centered approach for designing eHealth interventions using theories and principles from communication, information science, human-computer interaction, medicine, psychology, and public health. Students will also learn to assess the needs and technology practices of potential users, with a particular focus on groups that experience health disparities.

Due to advancement and wide adoption of health information technologies, there is a proliferation of sources of biomedical and health related large data in electronic format. Timely and efficient processing of these data, and accurate interpretation and representation of analysis results play critical roles in health related decision making. The purpose of this course is to prepare students in identifying, collecting and analyzing big data in healthcare setting.  The course will explore the role of analysts and analytics in healthcare organizations.  The course is designed to provide students hands-on use of analytical tools for data extraction and analysis. This course will also introduce students to the various data collection and processing methods in the delivery of meaningful data from patients to healthcare providers, administrators, etc. The course will begin with introduction to “big data” and then focus on how big data helps in informing healthcare analytics in ways not possible before.  The course will also emphasize on diverse sources and uses of health related big data, various data types, classification procedures, and basic applied statistical methods and procedures commonly used in health related data analysis. Students will use software such as SAS, Tableau, or R to gain hands on experience in data acquisition, analysis, and reporting.

This course focuses on the science of data visualization with the art of graphic design using Tableau to help communicate complex information effectively. It gives students the ability to articulate the theory, principles, methods, and techniques in data visualization while acquiring the visualization knowledge needed to solve real-world health problems. Students will be exposed to best practices on transforming data sets into visual graphics such as charts, bar graphs, scatter plots, and heatmaps and learning storytelling in presenting complex data to different audiences, which empowers decision-making. Through several hands-on activities, students will explore the different types of data presentations that provide insights to the users. Industry-level data visualization software such as Tableau will be used throughout the semester for the hands-on exercises.

GIS provides an effective way to visualize, organize and manage a wide variety of information including administrative and medical data, social services, and patient data as it relates to population health. Public health agencies are also using GIS to map health-related events, identify disease clusters, investigate environmental health problems, and understand the spread of communicable and infectious disease. This course focuses on Geographic Information Systems (GIS) and its various applications in public health domain. This course uses a hands-on approach for teaching GIS in health care by focusing on how to use GIS software in the context of carrying out projects for visualizing and analyzing population health related data. Each week’s activities include a lecture and hands-on tutorial that focuses on a health care issue which uses the software to analyze data or solve a problem. Through weekly assignments, project, and case studies, students will not only learn how to use the software but will also learn the many distinctive advantages of using GIS for public health related decision making and planning.

This course provides a timely way to explore cutting edge topics of interest to students and faculty. The varying content may include topics such as: artificial intelligence, advanced data analytics, data mining and machine learning, cyber security, emerging topics in public health surveillance systems, imaging informatics, telehealth, mHealth, or any other informatics-related emerging topics. This course will have an in-depth exploration of at least one selected topic and a team-based project. Topics may vary from year to year course offering depending on availability of instructor and/or required resources.