Courses

3 Credits
Prerequisites: General knowledge of how to use a computer, common software such as Microsoft Office, a web browser, and R programming language
Course Instructors: Subha Madhavan, Ph.D., & Samir Gupta, Ph.D.

This basic course is a broad overview of how data analytics is used in healthcare and medical research settings. Here, we primarily focus on the use of a data analytic framework to advance the goals of healthcare, research and product development organizations. We attempt to cover the people, process, infrastructure needs, tools, skills, organization and governance to effectively perform this work in a healthcare organization or an organization that is in the ecosystem of a healthcare system. We do not focus on any particular quantitative methodologies. Here we provide a broad overview of the field with exposure to several common tools, software packages, use cases with a focus on hands-on problem solving with real world datasets.

3 Credits
Prerequisites: HIDS required core courses
Course Instructors: Hank Rappaport, M.D., & Nathan Cobb, M.D.

The scientific utility of EHR derived “routine” clinical data generated as a byproduct of care delivery has been previously demonstrated, and there are no insurmountable technical challenges or barriers to achieving the effective use of this type of data for a national cohort of millions of individuals.

This course is designed to introduce students to one of the major components of data collection, analysis and clinical decision support systems at the point of care: The electronic health record systems (EHR) and their use in care management, research, and analytics. The course is intended to familiarize students with data access across multiple data sources. This is a practical, hands on experience course that will introduce students to the variety of systems used in the clinic and the complex interactions and features that make up a modern clinical information system.

Students will learn different aspects of EHR systems in both inpatient and outpatient clinical environments, and will focus on data extraction, exploring analytics and visualization capabilities and generating insights from the perspective of EHR.

This is an introduction‐level course, designed to offer the most benefit to Health Data Scientists and offers a deep learning experience of EHR functionality.

3 Credits
Prerequisites: Basic programming knowledge, preferably in R
Course Instructors: Lauren Kunz, Ph.D., & Yili Zhang, Ph.D.

The class will focus on key concepts about evidence-based approaches in data science, including the analysis of population health datasets and the discussion of important case studies. Students will learn important principles for organizing and analyzing data, along with concepts and tools for reproducible research, for which they will gain hands-on experience with the R programming language. Students will then expand their understanding of study design and evidence-based medicine, discussing important literature in this area and where the field is going regarding innovative designs. Finally, there will be an emphasis on the understanding of “meta-research” or “research on research,” focusing on the understanding of reproducibility and replicability.

4 Credits
Prerequisites: General knowledge of how to use a computer, common software such as Microsoft Office, and a web browser and basic knowledge in biology and mathematics/computer technologies
Course Instructors: Yuriy Gusev, Ph.D., & Krithika Bhuvaneshwar, M.S.

This course is designed to provide fundamentals of current computational approaches to support and enable Precision Medicine through data management, analysis and visualization. The successful implementation of precision medicine initiatives requires professionals who are well-versed in informatics. According to recent publications a closer look at strategic plans focused on research and clinical implementation of precision medicine efforts revealed significant gaps in expertise that impacts the ability of a health system to relate genomic data to better health outcomes. Clinical and biomedical informatics, with their focus on how information is collected, stored, analyzed and disseminated, are two important areas for the advancement of precision medicine. Providing data that has clinical relevance on a patient specific level as well as presenting that data in a way that makes it easier for physicians to interpret and use it in their decision-making process, informatics is essentially the engine that will power precision medicine into widespread adoption. This course covers the two broad focus areas: Precision Medicine in the context of Molecular Medicine and genomic technologies, and Precision Medicine in the context of data analytics integrating clinical data with genotype or other biomarker type data. Course details:  this course will consist of 4 modules, quizzes and final exam. Modules include: Precision Medicine fundamentals: concepts and approaches; Molecular Diagnostics: major technologies, platforms, resources and providers; Data Commons: on-line resources and tools for large scale Big Biomedical data; Data Analytics solutions for Precision Medicine.

3 Credits
Prerequisites: Ability to install new software application and local web server and configure a personal computer with administrative rights
Course Instructor: Adil Alaoui, M.S., MBA

This course provides a broad overview of Health Informatics and is designed for the health data science program.  Given the generally high level of Health Informatics field and its important role in the healthcare delivery, it is likely that each of you will have (or may already have had) some notion about informatics, information technologies in healthcare at some point in your careers.  This course will provide you with a framework to help you understand the informatics environment in healthcare, to assess different components of the ecosystem, to develop understanding about each of the major building blocks, and to be able to think about the specific technologies enabling health information access, sharing and analysis to improve performance, efficiency and enable a better patient outcome.  This course also provides an understanding of the leadership role of the health data scientist, implementation and management of technologies including computer based decision models to structure information and analyze complex organizational problems. Current and future IT applications such as the electronic health record (EHR), mobile health, and telemedicine will be analyzed for their influence on cost, quality and access to care; the legal, ethical and regulatory ramifications of these technological advances will also be explored.

3 Credits
Prerequisites: Proficiency in Python and R scripting languages in a high-performance computing environment
Course Instructors: Samir Gupta, Ph.D., & Matthew McCoy, Ph.D.

This course will provide a hands-on opportunity to explore the current applications of artificial intelligence and machine learning in biomedicine and healthcare. Focused around case studies representing the successful examples of these algorithms, students will learn the mathematical formulations underlying the methods, understand the computational requirements and limitations of their application, and explore their use in healthcare research applications. In general, a single ML/AI method will be addressed each week, and consist of lecture on the underlying algorithm, discussion of applications from the literature, and an interactive workshop illustrating its application to a test example dataset. Homework will test the implementation of the given algorithms on a curated dataset and explore the unique attributes.

4 Credits
Course Instructors: Nathan Cobb, M.D., Kristen Miller, DrPH, CPPS, & Anand Basu, M.S., MBA

This course will teach students the fundamentals of digital health application design and programming from both a theoretical and technical standpoint.  This course will start with a JavaScript/HTML bootcamp which will provide the foundations for building modern digital health applications, including accessing data over both FHIR and REST APIs. Class lectures will cover the design paradigms for both consumer and health IT, using real world examples to examine and learn key concepts and approaches. As part of the course students will work in teams of 4 to build their own digital health application using HTML, Javascript, FHIR, external APIs and TextIt to address the COVID pandemic. Usability and human factors labs will be interwoven into the syllabus to help students develop an understanding of these critical factors while developing and deploying digital health tools for patients, physicians and other users. As healthcare work is increasingly computerized, there is increasing discussion around human factors and usability. Recent initiatives, such as the ones launched by the Office of the National Coordinator, have put significant focus on the role of health information technology (health IT), especially electronic health records (EHRs) in clinical settings. However, positive effects notwithstanding, the use of health IT in clinical settings can lead to unanticipated consequences such as errors and adverse events. Human factors is a scientific discipline that provides insights into design (or redesign) of healthcare systems and processes impacting patient safety and quality of care. The focus of human factors is on improving human performance accounting for their cognitive and physical limitations. This course provides a broad overview of the role of health IT and informatics in healthcare through a human factors lens.

3 Credits
Prerequisites: Students should have general knowledge of how to use a computer, common software such as Microsoft Office and a web browser, be familiar with Python and R, and basic knowledge in biology and mathematics/computer technologies. Students must bring their own computers. The GU Canvas system will be used.
Course Instructor: Yuriy Gusev, Ph.D.

The objective of this course is to develop an understanding of Biomedical Imaging Informatics, its goals, data formats, data standards, methods and applications.  This course will enable students to identify various imaging modalities and associated data types, understand imaging data standards, navigate through on-line resources for medical imaging, and become familiar with tools for imaging information processing and analysis for major practical applications of biomedical imaging in research and clinical practice.