Dr. Kiran C. Patel College of Osteopathic Medicine—Health Informatics Program 97 MI 6416—Lean Six Sigma Green Belt for Health Care Lean Six Sigma for Health Care (Green Belt) participants will learn intermediate level tools, and techniques to deliver breakthrough business improvements that will reduce waiting times, improve quality, and reduce costs in a health care environment. More specifically, they will learn to apply a comprehensive set of 15-20 Lean Six Sigma process improvement tools by using the DMAIC (Define, Measure, Analyze, Improve, and Control) problem solving model. They will learn techniques for both quantitative and qualitative analysis, as well as methods and tools for workflow enhancement and acceleration. The course also covers how to map out processes and identify sources of variation, as well as to gain a basic understanding of inferential statistical analysis. Finally, they will learn how to perform how to implement lean management tools and philosophy, to improve and stabilize processes. Candidates work on either an integrated health care case study or on an actual business project and will apply class techniques to the project. There will be additional practice with basic tools to help promote mastery. (3 credit hours) MI 6417—Meaningful Use of Electronic Health Record Systems—A NextGen Approach This course will provide students with the opportunity to learn the fundamentals of set-up and using the applications of one of the most commonly used electronic health record systems in the United States, NextGen, in clinical settings. Students will be required to complete the NextGen elearning modules before the on-campus, hands-on training sessions. This course is required for the competitive internship opportunity in the NSU clinics. (3 credit hours) MI 6421—Geographical Information Systems: Fundamentals for Health Care This course will introduce students to geographic information systems (GIS) to map and spatially analyze public health and demographic data. Students will learn the fundamentals of the ArcMap software system and ways to integrate cartography into health informatics practice. Beyond use of GIS for cartography, this course will also examine ethical issues and methods of analyzing demographic and spatial health patterns using GIS and demography analysis methods. The versatility of GIS in a public health setting will be examined and will include exercises involving GIS applications in health marketing, demography, epidemiology, and health care systems. For example, the course will look at how different socioeconomic groups use urban spaces differently in terms of transportation and how these differences in navigation impact contact points for health marketing. Other issues covered in the class will be the ethics of GIS, manipulation of data, sources of data, and understanding some commonly used public health datasets such as the YRBS, BRFSS, and U.S. Census. (3 credit hours) MI 6422—Workflows and Process Improvement in Health Care Settings The course will introduce the clinical workflow analysis as a method of choice to improve clinical processes in health care delivery systems. Students will review the primary objectives for process improvement in clinical health care: outcome quality (including patient safety) and the development of health information technology (HIT) to support the Electronic Health Record (EHR) with initiatives showing a significant impact on clinical workflows, such as meaningful use. Students will define the functional components of the health care activities and learn to map on a flowchart the standard symbols used to represent all tasks and steps, decision points, resources, and outcomes of the clinical workflow. Students will apply the tools of workflow analysis by assessing a workflow in a health care setting using graphical representations of the workflow phases (current state, desired state), and process defects identification and classification. The course will introduce the quantitative measures of workflow improvement used in Lean Six Sigma. Students will formalize a proposal for an intervention aimed at the modification and optimization of a clinical workflow. (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 system designs, such as Hadoop ecosystems and Hadoop-based tools. Students will be exposed to the application of predictive analytics specific to health care, so they 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 realtime 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 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
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