The Healthcare Analytics specialized studies program is ideal for professionals who want to pursue or advance their career. The program is designed for both healthcare and information technology professionals looking to learn research and analytical skills to collect, organize, and visualize data in order to change the healthcare landscape. Using data analytics in a healthcare setting can improve patient outcomes, lower costs, improve the quality of care, enhance health delivery system performance, and optimize business operations.
Learning Format Online
Duration 6-12 months
*Based on student course load.
Total Units 10 Units
Course Schedules are Subject to Change
Healthcare AnalyticsThis course will introduce students to health informatics and advanced analytics through core technologies, data analytics (computational and analytical methods), and health information technology to improve patient care outcomes and enhance the health delivery system’s performance. The course will focus on health informatics applications within the healthcare and public health landscape. Specific topics will include an overview of the health informatics concept and related terminologies, data standards, security and confidentiality, health information exchanges, population health management, health data analytics, consumer health informatics, emerging health informatics innovations, and other topics related to health informatics. Learning objectives will be achieved using a variety of learning methods, including (lectures, discussion questions, participation, assignments, experiential projects, selected readings from the textbook, peer-reviewed articles, and industry reports) for each learning objective to develop critical core competency skills and to ascertain real-world applications.
Health Data Acquisition, Analysis and ManagementThis course will provide students with an understanding of information generated in the research, delivery, and management of public and private healthcare services and how the information can be used to generate better healthcare outcomes while respecting privacy, regulation, and ethical practices. Additionally, this course will expose students to some of the challenges in connecting disparate data sources for rigorous analysis using best practices and toolsets. The experience of working through these challenges with real data will provide a deeper understanding of our healthcare systems and insights into building confidence in the analysis that can lead to improved public health.Specific topics will include an overview of the healthcare information landscape, how information is used in different healthcare settings, healthcare information standards (such as the ICD standard and its history), healthcare interoperability standards, which enable the sharing of critical healthcare information across providers and settings, ethical considerations in the use and sharing of healthcare information, governance of healthcare information, the architecture of modern healthcare systems, and the practice of obtaining, standardizing, and connecting various and disparate data sources.
Public Health InformaticsThis course will aim to provide an overview of the definition and importance of public health informatics and its role in public health services. It will address topics related to the use of public health informatics as a structured and deliberate approach to using available organizational data and information for furthering research and knowledge and have an influence in the planning and delivery of key public health services for the population at large.
Data Visualization & AI Machine LearningThis course introduces topics on visualization techniques and AI Machine learning tools that are necessary to facilitate the decision-making process to improve healthcare outcomes and support strategic and operational decisions. Students review data basic concepts, principles, methods, design and implementation techniques, and applications of data visualization models and AI applications. Students gain core competency skills in using processed datasets, compatible with creating visualization and AI machine learning tools such as dashboards, executive summaries, and clinical and operational applications to optimize clinical and business outcomes.