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 introduces students to the fundamentals of health informatics, including policy related to health informatics and other levers that influence the field. It prepares learners for success in a new or expanded career in health informatics building skills in provide management, critiquing research, and developing understanding of health information technology infrastructure, data standards, cybersecurity, data governance, policy, and ethical considerations in healthcare.
HIF 603. Introduction to Analytics in Health Informatics. 3 credits.
This course focuses on descriptive and diagnostic analytics, utilizing SQL. It introduces the fundamental concepts and techniques essential for descriptive and diagnostic analytics within the field of health informatics. Student will learn how to effectively collect, clean, and analyze healthcare data.
HIF 605. Advanced Analytics in Health Informatics. 3 credits.
This course offers an in-depth exploration of predictive and prescriptive analytics tailored for the health informatics field. Student will learn how to use predictive analytics to identify future health trends and outcomes from historical data, which helps in better planning and preventive care. The course also covers prescriptive analytics, focusing on developing effective strategies and recommendations to improve health outcomes based on the predictions made.
HIF 635. Data Governance. 3 credits.
In informatics, the flow and protection of health data is critically important. HIPAA compliance, risk-benefit analysis, regulations, clinical workflow, and legal policies must all be considered. In this course, students will explore real-life scenarios and case studies to equip students to build their own data governance team, define and communicate clear processes for receiving and evaluating data requests, recognize potential ethical or legal issues, and create external partnerships for the public good.
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.
This course introduces the basics of supervised learning, unsupervised learning, reinforcement learning, and deep learning, providing a foundational understanding of each approach and their application in health informatics. We will also explore the ethical considerations and potential biases in machine learning algorithms, emphasizing their impact on healthcare outcomes.
HIF 710. Systems Analysis & Design. 3 credits.
Without focusing on human factors, data is just data. The goal of the health informaticist is to move from raw data to wisdom. This course focuses on building skills in project management practices, examining and improving clinical workflow, using clinical decision support tools, and understanding that the system life cycle and its impact on health informatics are crucial to continually improving systems and implementation.
HIF 755. Generative AI in Health Informatics. 3 credits.
This course introduces Generative AI - focusing on Large Language Models, Generative Adversarial Networks, and Natural Language Processing - to improve data processing, patient care, and decision-making in health informatics. We will explore how these technologies are applied in healthcare environments and discuss the importance of using AI responsibly, addressing ethical considerations and potential biases. 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.
The capstone course will allow students to apply the data skills and expertise learned throughout the curriculum to a real-world data project. The intent is that students will work with collaborative partners setting forth on a planned date project in the field of health informatics. Students must complete a presentation of their lessons learned and a final data product provided to the partner.