Biomedical Informatics Curriculum and Timeline

applying informatics methodology in a biomedical settingOur curriculum has undergone scrutiny by faculty and industry partners to ensure its relevance and applicability to the current workforce needs in biomedical fields. The goal of the Master of Science in Biomedical Informatics program is for students to learn and master the following:

  • informatics methodology, applying tools and techniques to both research and applied problems in biomedical settings
  • effective communication with diverse professional audiences regarding informatics issues and solutions
  • management of  biomedical informatics projects
  • understanding of the ethical, privacy, and data security issues in the field

Students will be required to complete five core courses, four electives, and a Capstone Project.

Prerequisites

Applicants will be expected to have introductory level competency in:

  • Statistics: this can be fulfilled by a course such as Introduction to Statistics or Biostatistics.
  • Health sciences or clinical care: this can be fulfilled by a survey or overview course in health sciences or a clinical degree.

You may fulfill these requirements through Graham School prior to matriculation. We will offer a boot camp for each of these subjects in February-March for Spring start and in August-September for Autumn start. Please see the bottom section of the Course Schedule page for more information on when boot camps will be offered.

In your application, please be sure to indicate in your personal statement which courses in your transcript fulfill these requirements or how you plan to fulfill these requirements.

Please confirm the planned course and documentation requirements with the Master of Science in Biomedical Informatics administrative team (mscbmi@uchicago.edu) prior to enrolling in prerequisites.

Core Courses
  1. Introduction to Biomedical Informatics

    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.

  2. Concepts in Computer Programming

    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.

    OR

    Advanced Concepts in Computer Programming - A Case Study Approach in Biomedical Informatics
    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.

  3. Applied Research/Clinical Informatics

    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.
    Prerequisite: Concepts in Computer Programming

  4. Ethics and Policy Questions: Genomics, Health Care, and Big Data

    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.

  5. Leadership and Management for 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.

Electives

Students are able to customize their degree by selecting coursework to build concrete technical skills in bioinformatics, healthcare informatics, and big data analytics. Four elective courses are required for degree completion.

  • Advanced Concepts in Computer Programming - A Case Study Approach in Biomedical Informatics

    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.

  • HIT Integration, Interoperability Standards

    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.

  • Big Data and Health Care

    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.

  • Decision Support Systems & Health Care

    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.

  • Introduction to Bioinformatics

    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.

  • Advanced Bioinformatics: Genome Analysis

    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.

  • Geographic Information Systems and Health Information

    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.

  • Healthcare Innovation and Entrepreneurship

    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.

Capstone Project

As a culminating experience, students will put into practice the knowledge and skills they learned during their coursework through a Capstone Project. Students will have the opportunity to develop and implement a biomedical informatics project with an industry or University partner or in their workplace.

Students will complete this project over the course of three quarters in the program. The three quarter sequence will include the following topics:

  • Planning your project and developing your proposal
  • Implementation of your project
  • Writing up the results

Capstone Projects should fall under one of the following categories:

  • Design a data collection platform for a new research project
  • Evaluate and propose solutions for an existing information system or database
  • Develop an analytic framework for reporting results from an existing dataset

The Administrative Director will assist students in identifying an appropriate Capstone Project mentor. Mentors may be program or other University of Chicago faculty, industry partners, or other appropriate professionals in the field. Students will present their final projects to a panel, including program instructors and other industry experts, who will then evaluate the project and provide feedback to the student and mentor.

Timeline

From start to finish the Master of Science in Biomedical Informatics degree can be earned in as little as 12 months to a maximum of four years.

12 month schedule, 3 courses per quarter
(example: start September 2016 and finish August 2017)
Quarter 1-2 3 courses
Quarter 3 3 courses plus Capstone implementation
Quarter 4 Capstone writing

 

27 month schedule, 1 course per quarter
(example: start March 2016 and finish June 2018)
Quarters 1-7 1 course
Quarters 8 1 course plus Capstone implementation
Quarter 9 1 course plus Capstone writing