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 present students with an introduction to the field of health informatics and advanced analytics through the use of core technologies and data analytics (computational and analytical methods) and the use of health information technology to improve patient care outcomes and to enhance health delivery system performance. The course will focus on health informatics applications within the healthcare landscape. Specific topics will include: overview of the health informatics concept and related terminologies, data standards; security and confidentiality, health information exchanges, population health management and 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 and participations, quizzes, projects, and selected readings from the textbook, peer-reviewed articles, industry reports, etc.,) for each learning objective to develop critical core competency skills and to ascertain real- world applications.
Data Assets and Data StrategyData underlies all of our best efforts to evolve health care practices. Data, and lots of it, now come in many forms and from many sources. A workable data strategy has to account for the variety of data forms and sources. But more importantly, a good data strategy should bake in empathy for the sensitive nature of the data representing each individual. It’s a gray and messy area. This course is designed to give you the tools necessary to understand an organization’s strategy, identify gaps that may exist in the strategy, define the various roles that influence data strategy, and adapt health data strategies to evolving health care practices. Specific topics include emerging trends in data governance and regulation, roles of data scientist, chief information officer, chief data officer, chief analytic officer, and chief technology officer, various ways that analytic teams are organized, connecting data strategy and governance to improvement in patient outcomes, and data as the key catalyst for the transition from volume-based, episodic care to value-based, personalized care. This is the foundation for improving the delivery and outcomes of our healthcare experience.
Health Data Acquisition and ManagementData and analytics professionals are continually rethinking ways to achieve meaningful data acquisition and management of data that can address the rapid increase in demand and complexity of data. This course will focus on healthcare data acquisition and management including balancing, collecting, and connecting to modern data management solutions. The course will begin by addressing the more traditional forms of data management followed by the collection and management of metadata. Topics include the processes used to describe, organize, integrate, share, and govern data in the healthcare operational and analytics framework. The course will introduce participants to new framework and trends in modern data management used in multi cloud settings with service-oriented architecture principles, API integration, edge computing and the overall governance of data for healthcare settings.
Data Mining, Visualization and Decisioning for HealthThis course introduces topics on data mining and visualization tools that are necessary to facilitate the decision-making process to improve healthcare outcomes and support strategic and operational decisions. Students review basic concepts, principles, methods, design and implementation techniques, and applications of data mining and data visualization models and applications. Students gain core competency skills in data mining to access and create processed datasets, compatible for creating visualization tools such as dashboards, executive summaries, and clinical and operational reports to optimize clinical and business outcomes.
Introduction to Artificial Intelligence (AI) in HealthThis course provides an introduction to artificial intelligence (AI) and its healthcare applications in machine learning, precision medicine, and robotics. Students learn the core skills needed to assess clinical and business information data sets and apply these skills to enhance evidence-based healthcare and business outcomes. In this course, students apply AI knowledge and skills to promote effective disease management and patient engagement models, enhance clinical practice, adopt innovative clinical interventions in the value-based care model, and appraise the ethical implications of deploying and integrating of AI in healthcare. Students have the opportunity to describe how AI assists healthcare leaders in making strategic decisions as well as gain the knowledge needed to transform their organizations into innovative, efficient, and sustainable entities of the future. This course expands upon the learning topics presented in the Healthcare Analytics program by taking a deeper dive into artificial intelligence, as applied to various medical settings.
Precision MedicineThis course will allow students to obtain an understanding of precision medicine theory and its sub-field, its impact in the public health and healthcare industry, and the imminent role health analytics plays in this emerging healthcare field. In addition, this course will relate how concepts in public health, health management and policy, big data and health informatics, real world data sets are impacted by the precision medicine field; further, students will also get a chance to demonstrate knowledge in the precision medicine arena and translating its multiple type’s approaches. Topics include functional applications of precision medicine, the relationship to health analytics and its consequences in value based clinical care, public health impact on health departments and population health initiatives. Learn about analytic methodologies to apply precision medicine, as well as current and innovative health informatics arenas that align with the precision medicine field.