Curriculum

This is an intensive course designed for students who have little to no background in statistics. The faculty director recommends this boot camp for students that have not recently taken a statistics course or that do not regularly use statistics in their everyday work or activities. The course will focus on understanding the concepts and will include a reasonable level of theory and applications and will strive for balance between these two. The objective of the course is to bring all students to the same level of statistical understanding and to ensure that they have the ability to apply these concepts using real data.

Master of Science in Biomedical Informatics students need to have a good understanding of how big clinical settings work. This boot camp is recommended for incoming students who have not worked as clinicians in the United States. This is a one-day workshop that serves as a great introduction to the United States healthcare system.

This course will cover the fundamentals of informatics as it applies to health care and research. Specific topics will include: radiology, imaging, and nursing informatics; using clinical data for research; overview of bioinformatics; coding in clinical care and billing; terminologies and ontology mapping; security and privacy including HIPAA and HITECH; mobile health and telemedicine; human-computer interaction; decision support; meaningful use; quality reporting; the Affordable Care Act; and clinical laboratory informatics.

This course will introduce students to key concepts in project management and team building for biomedical informatics projects. Specific topics will include: defining project scope, goals, and metrics; managerial supervision; team motivation; team communication and conflict resolution; and communication during and after projects to maximize success and impact.

This course will provide an introductory and intermediate level overview of computer science and programming for students who are not working in technology-based professions. Students will learn concepts in computer programming and how programming language works, as well as theories behind information system design and management. Specific topics include: Python programming language; fundamental data structure; algorithm design; basic project management of development projects.

This course will provide students with an understanding of critical ethical, legal and social issues related to biomedical informatics, with an emphasis on policies in the US. Specific topics include: balancing privacy and discovery in the context of big data analysis; data stewardship; human genomic data; implications of future innovation for privacy and ethics; and guarding against misuse of data.

This course will introduce students to the concepts of research design, working with healthcare data, managing secondary data sets, and basic data analysis including descriptive statistics and measures of association. The course will outline methods for identifying appropriate datasets and using tools to analyze data, evaluate hypotheses, and interpret results. The goal is to help students learn how to describe, present, summarize, and organize data effectively in the area of biomedicine.

Prerequisites
MSBI 31300: Concepts in Computer Programming with a grade of B or higher

This course will enable students to focus on applying informatics methods to real-world scenarios. The course will be customized for students wanting to pursue research or clinical informatics projects. In the research group, we will use a case-study based approach to identify appropriate datasets, use analytic tools to analyze data, evaluating hypotheses, and interpret results. In the clinical group, we will focus on the methods of diagnosing issues within current or proposed clinical systems, using informatics to address issues, and evaluate the results.

This course will provide students with an understanding of healthcare information technology (HIT) standards and interoperability. Lessons will include: a review of key standards such as IHE initiative, HL7, DICOM, CCOW, and others; the role of non-medical standards (HTTP, XML, etc.) in biomedical informatics; policy issues related to data exchange between institutions; use of service oriented architecture (SOA); enterprise business integration; and HIPAA polices and standards.

This course will allow students to explore the concept of big data and the analytic and clinical challenges it presents. Lessons will cover the challenges in capturing, storing, searching, sharing and analyzing big data including sources such as electronic health records, clinical notes, medical imaging data, genetic data, pharmacy data, and administrative data (ICD-9 codes and billing data). Current advances in data science, information extraction and predictive modeling will also be examined.
Prerequisites
MSBI 31300: Concepts in Computer Programming and MSBI 31800: Introduction to Applied Data Analysis, or MSBI 32000: Intermediate Applied Data Analysis

This course will give students an overview of computer-assisted management information and decision systems used in health organizations. Topics will include: the analysis and design of databases for decision-making; data and information flow and reporting; security, privacy and confidentiality in using decision support systems; and best practices for change management in decision support systems.

Prerequisites
MSBI 31300: Concepts in Computer Programming

This course will provide students with an introduction to the tools and applications of bioinformatics. Examples will focus on genetic and genomic technologies including basic Illumina molecular biology; utilities for DNA sequencing; and knowledge extraction engines.

Prerequisites
MSBI 32400: Introduction to Bioinformatics
This course will follow on from the Introduction to Bioinformatics and will include advanced topics such as: Linux and high performance computing; genomic data visualization; R programming in bioinformatics; and RNA sequencing data analysis.

Increasingly, health care professionals are integrating data and data analysis techniques into the formulation of strategies, approaches, and frameworks for addressing complex health issues. Among the various data analysis tools being used in the health care and public policy fields Geographic Information Systems (GIS) are unique in their ability to tie health data to a geographic location. This makes a GIS much more than mapping software; by providing users with a suite of tools for manipulating, analyzing, and visualizing data in a spatial way a GIS can reveal relationships, trends and patterns that would not be apparent in other data analysis applications. GIS analysis methods can be used across disciplines to answer the kinds of complex multi-dimensional questions frequently found in the health care field.

In this class, students will learn about fundamental GIS concepts while building the basic skills necessary to integrate a GIS into a decision making process. Concepts presented in lecture will be put into practice through hands-on laboratory exercises utilizing QGIS, a cross-platform free and open-source desktop geographic information system. Additionally, the course will incorporate cases studies into laboratory exercises to ground GIS analysis techniques in real world learning scenarios.

This interdisciplinary course will provide the fundamental knowledge for healthcare innovation and entrepreneurship. The course will start with healthcare problem identification, innovative solution design, and eventually pitching the business concept to potential investors. Guest speakers from various schools will provide real-world perspectives and practical knowledge from different angles. Students will be divided into groups and work on either a quality improvement project or a business proposal throughout the 10-week course. There will be a final project presentation at the end of the course. Students are encouraged to later submit the project to annual UChicago app challenge or new venture challenge if applicable.

Prerequisites
MSBI 31300: Concepts in Computer Programming with a grade of B+ or higher

This course will introduce students to advanced concepts in computer programming through real-world "end-to-end" case studies. Additionally, this course will provide a comprehensive introduction to the domain of biomedical informatics from a computer programming perspective and will also provide implementation examples that are representative of problems that practitioners in the medical field have to solve. During the course, students will learn how biomedical informaticists access and process healthcare and medical data. Topics will include: common IT methods and tools, important numerical algorithms, commercial products as well as open-source tools and libraries.

Everyday thousands of lines of print are dedicated to the promise of Chat-bots, AI Doctors, Robot Surgeons, Image Recognition, Self-driving Cars, and other disruptive technologies. The science of biomedical informatics evaluates the interface of humans and information resources or technologies to ask the question: We can do it, but are we better for it? Each week we will discuss the spectrum of evidence generated on emerging technology through the lens of popular culture, news and articles, primary research, and fundamental research methodologies. Specifically, each week we will review an emerging disruptive technology or information resource in healthcare through the frame of the week’s Friedman chapter, a student-selected primary research paper, and a popular news topic.

Prerequisites
MSBI 31300: Concepts in Computer Programming and MSBI 31800: Introduction to Applied Data Analysis, or MSBI 32000: Intermediate Applied Data Analysis

As health and healthcare digitizes, the potential to better monitor, target and respond to the needs of populations increases. However, the exponential growth in health information technologies, and associated consumer products, has created an informatics skills gap for even veteran population health professionals. This course provides both a hands-on introduction and conceptual foundation for public health informatics. Students will work with open source surveillance data sets, apply standard terminologies and vocabularies, while furthering their understanding of the intersection of public health practice and informatics. This course will prove valuable for any student tasked with evaluating new data sets for monitoring the health of populations.

Prerequisites
MSBI 32000: Intermediate Applied Data Analysis

This introductory course will present an overview of the basics concepts, techniques and algorithms used in Machine Learning. By taking this course students will be introduced to the basic ideas and intuition behind modern Machine Learning techniques. Students taking this course should be able to program in Python in order to complete the assignments required. Due to the mathematical nature of Machine Learning, this course will assume a reasonable level of mathematical maturity.

This course will provide an introductory view of the breadth of healthcare delivery science covering a wide array of topics. The course sessions will delve into the fields and disciplines that comprise HDS including but not limited to: approaches (e.g., quality improvement, implementation science), techniques (e.g., human factors and human-centered design), data and analytics (e.g., program evaluation, machine learning), program design and implementation (e.g., stakeholder engagement, prototype development), and needs and risk assessments, among other topics. Students will have an opportunity to practice many of the skills being demonstrated and taught by the course lecturers and will work on a project of their own throughout the course.

This course will guide students through the writing of their capstone project proposal.

In this course, students will begin work on their capstone projects.

Students will work on writing and prepare for the presentation of their capstone projects.