According to the American Medical Informatics Association, health informatics is “the science of how to use data, information and knowledge to improve human health and the delivery of healthcare services.” The Creighton Health Informatics Program develops a health data informed workforce focused on the utility of data to improve health care delivery. With the proliferation of value-based payments, population health and social determinants of health, the health informatics program will collaborate with industry partners to ensure a cutting-edge program that not only prepares graduates for an innovative environment but for themselves to innovate to meet industry demands and address societal needs to improve health care.
Master of Science, Health Informatics (36 Credits)
The Master of Science degree in Health Informatics program is designed to offer students a broad, interdisciplinary, and critical understanding of informatics and analytics within the healthcare context. Health informatics is an interdisciplinary field that analyzes biomedical data for improved decision making in healthcare. Health informatics brings together the fields of computer science and data analytics to study the processes for the generation, storage, retrieval, use, and management of health data. The program highlights the social and ethical as well as the technological dimensions of this rapidly evolving professional field. The program will be of interest to students intending to pursue careers at the intersection of healthcare and technology, it will also appeal to any graduate student interested in examining larger questions about healthcare analytics from an interdisciplinary perspective, including the role of data to understand social and cultural dimensions of health care.
Degree Requirements (36 Credits):
| Code | Title | Credits |
|---|---|---|
| Required courses: | ||
| HIF 601 | Introduction to Health Informatics | 3 |
| HIF 603 | Introduction to Analytics in Health Informatics | 3 |
| HIF 605 | Advanced Analytics in Health Informatics | 3 |
| HIF 635 | Data Governance | 3 |
| HIF 640 | Data Management | 3 |
| HIF 710 | Systems Analysis & Design | 3 |
| BIA 770 | Cybersecurity | 3 |
| HIF 785 | Leadership and Business Principles for Health Informatics | 3 |
| HIF 790 | Health Informatics Capstone | 3 |
| Electives - Choose three courses: | 9 | |
| Predictive Analytics | ||
| Applications of Optimization Modeling | ||
| Data Visual Analysis and Visualization | ||
| Machine Learning in Health Informatics | ||
| Generative AI in Health Informatics | ||
| Organization and Management of Public Health Services | ||
| Epidemiology | ||
| Health Communication and Informatics | ||
| Health Economics and Finance | ||
| Introduction to Mixed Methods | ||
| Failing and Failure in Leadership | ||
| Total Credits | 36 | |
Graduate Certificate in Health Informatics (12 Credits)
Certificate Requirements (12 Credits)
| Code | Title | Credits |
|---|---|---|
| Required courses: | ||
| HIF 601 | Introduction to Health Informatics | 3 |
| HIF 710 | Systems Analysis & Design | 3 |
| HIF 785 | Leadership and Business Principles for Health Informatics | 3 |
| Elective (choose one from below): | 3 | |
| Predictive Analytics | ||
| Applications of Optimization Modeling | ||
| Data Visual Analysis and Visualization | ||
| Machine Learning in Health Informatics | ||
| Generative AI in Health Informatics | ||
| Organization and Management of Public Health Services | ||
| Epidemiology | ||
| Health Communication and Informatics | ||
| Health Economics and Finance | ||
| Introduction to Mixed Methods | ||
| Failing and Failure in Leadership | ||
| Total Credits | 12 | |
Courses
HIF 601. Introduction to Health Informatics. 3 credits.
This course offers a thorough introduction to information processing, searching & organization, and analysis of information. This survey course will provide an overview of Health IT Infrastructure, Internet based access, ethics & economics of information, and change management in a healthcare setting.
HIF 603. Introduction to Analytics in Health Informatics. 3 credits.
Introduces the fundamentals of statistical inference as used in health informatics. Discussion topics will include probabilistic models, random variables, and the central limit theorem. Further the statistical inference will be discussed from point and confidence interval estimation, within the context of hypothesis tests, and using linear regression models.
HIF 605. Advanced Analytics in Health Informatics. 3 credits.
A course in computer programming and problem solving, with an emphasis on designing and developing solutions to real-world problems (such as system modeling, data analysis, and multimedia processing). Specific topics include data standards, algorithm development, basic control structures, simple data types and data structures.
HIF 635. Data Governance. 3 credits.
ln this survey style course, students will discuss both historical and current healthcare IT law and policy to include regulatory frameworks, system evaluation, the impact of the internet on healthcare data, data request processes, and data governance issues within the healthcare field.
HIF 640. Data Management. 3 credits.
The course provides an in-depth look at IT systems architecture, security, and networking within current health information systems and applications to include management and mining of data, data extraction, and security issues.
HIF 655. Machine Learning in Health Informatics. 3 credits.
The course covers basic statistical principles of supervised machine learning, as well as some common algorithmic paradigms to improve health care decision making, clinical decision support and care process improvements. Ethics of Artificial Intelligence including risk of bias will also be discussed.
HIF 710. Systems Analysis & Design. 3 credits.
This course focuses on the human computer interaction and human factors engineering. A specific focus is how Interface design impacts usability. The class will investigate workflow analysis and redesign, healthcare decision support and clinical guidelines along with distributed health care information technologies.
HIF 755. Generative AI in Health Informatics. 3 credits.
This course covers key AI concepts with an emphasis on ethical considerations. Students will learn deep neural network algorithms, practical AI healthcare applications, and efficient problem-solving methods, while consistently addressing ethical implications to ensure responsible and sustainable solutions. The course introduces students to the core concepts of generative AI, focusing on generative adversarial networks, autoencoders, recurrent neural networks, reinforcement learning, and large language models. TensorFlow will be used to build hands-on applications. It covers the mathematical principles essential for understanding natural language processing models. Students will create predictive models using reinforcement learning and deep Q-learning and evaluate large language models. The course equips students with the necessary skills to navigate the field of AI, emphasizing practical experience in model evaluation and the development of robust AI systems using Python. P: HIF 655.
HIF 785. Leadership and Business Principles for Health Informatics. 3 credits.
This course will explore contemporary trends in today's volatile and complex healthcare/higher education organizations (micro, meso, macros levels) with an analysis of theories and executive/manager competencies that contribute to positive outcomes. Students examine innovation, change, psychological safety, and communication strategies that maximize human potential and organizational success.
HIF 790. Health Informatics Capstone. 3 credits.
Health informaticists need to be able to support the utility of health data to improve healthcare. In the capstone, students will work with a health-related dataset to design a project with the intent of application of skills from the didactic curriculum to prepare for a career as a health informaticist. Students are expected to design and implement the project with mentorship and present their final product to gain skills in ensuring data has meaningful impact.