NSU HPD Catalog 2021-2022

Dr. Kiran C. Patel College of Osteopathic Medicine—Health Informatics Program 107 roles. The course will produce a personal understanding of individual, as well as group personality/strengths and how these evolve and affect performance in individuals. Students will develop a better self-awareness of what strengths they possess and how this affects personal and work performance. It demonstrates how leaders continue to grow, if this is a chosen career path, and how they develop each of the group’s talents to maximize the performance of the team and organization. The Affordable Care Act will be incorporated and students will discover what individual and organizational talents must be used to improve patient care in the future when utilizing technology. (3 credit hours) MI 6424—Health Care Analytics and Data Visualization I The course will expose students to health care “big data” focused on current needs—such as population health, outcome reporting, clinical decision support, physician quality measurement, and various other measures (including CMS initiatives like meaningful use and Medicare and payer-quality reporting requirements). The course will use current real-world problem scenarios where data analytics and visualization can be applied to successfully report on and solve various problems prevalent in today’s value-based payer model. Students will learn how to do large scale data mining and the infrastructures needed to support the various systemdesigns, such as Hadoop ecosystems and Hadoop-based tools. The student will be exposed to the application of predictive analytics specific to health care so he or she will understand the use of data to help deliver quality and safe patient care, as well as data-driven methods of improving care. The course will expose students to real-time data analytics where data is collected and reported on around the clock. It will also expose student to mobile data acquisition and analysis coming from various local and remote devices and will introduce students to data visualization methods that will teach them how to communicate analytical insights to both technical and nontechnical audiences. (3 credit hours) MI 6426—Health Care Analytics and Data Visualization II This course is a continuation of MI 6424 (Health Care Analytics and Data Visualization I). The course will expose students to health care “big” data focused on current needs such as population health, outcome reporting, clinical decision support, physician quality measurement, and various other measures (including CMS initiatives like meaningful use and Medicare and payer-quality reporting requirements). The course will use current, real-world problem scenarios where data analytics and visualization can be applied to successfully report on and solve various problem prevalent in today’s value-based payer model. The student will learn how to do large-scale data mining and the infrastructures needed to support the various system designs such as Hadoop ecosystems and Hadoop-based tools. The student will be exposed to the application of predictive analytics specific to health care with an understanding of using data to help deliver quality and safe patient care and providing data-drivenmethods of improving care. The course will expose students to real-time data analytics where data is collected and reported on around the clock and to mobile data acquisition and analysis coming from various local and remote devices. It will also introduce students to data visualization methods that will teach themhow to communicate analytical insights to both technical and nontechnical audiences. (3 credit hours) MI 6428—Artificial Intelligence for Health Care This advanced cognitive engineering systems course will expand upon introductory topics presented as part of the clinical decision support, database management, and analytics courses to take a deeper dive into data science and artificial intelligence algorithms, with specific application to such medical specialties as oncology, cardiology, pulmonology, radiology, neurology, and psychology. It will provide students with skills necessary to undertake programmatic statistical analysis of complex patient information data sets; to apply unsupervised learning techniques that will enhance outcomes of the predictive and prescriptive analytics methods; to use supervised learning methods that represent evidence-based guidelines and detect medical fraud; to process and exchange structured and unstructured clinical data; to compare and analyze graphs (i.e., ECHO) and images (i.e., MRI/X-Ray); and to apply natural language processing techniques to ingest and analyze clinical data. Students will learn how to choose among various AI methods; integrate clinical data and algorithms; translate research applications into clinical practice; and perform longitudinal data analysis using primary sources of clinical data, such as electronic medical records, lab information systems, and imaging databases. Participants will combine research methods with real-world evidence to discover new ways of approaching drug performance and pharmacological surveillance through real-time aggregation and monitoring of health care provider databases. (3 credit hours) MI 6430—Methods of Health Care Analytics This course will introduce students to a variety of mathematical techniques that are commonly used in health care analytics and health informatics. The emphasis will be on developing an understanding of the methods, their uses, and their limitations. Mathematical rigor would not be emphasized, but instead, an understanding of the meaning and uses of the techniques. The instruction would also include teaching a mathematical mindset to the students that will allow them to extend their knowledge and understanding to further areas as needed in their future endeavors. (3 credit hours) MI 6432—Big Data Analysis in Health Care This course provides a comprehensive and rigorous introduction to big data analytics in health care. It will describe the hardware/software infrastructures that are used today for big data (e.g., Hadoop, Hive) and the implications of these

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