Course Descriptions

This is the core introductory course for students beginning a master’s degree in Biomedical Informatics or for students in other graduate degree programs seeking an introductory overview of the core theories, challenges, research methods and areas for the development of health information management systems and applications.

Building on BMI 501 as a prerequisite, this course surveys the structures and information management challenges of the U.S. health care system, public health system and biomedical research system as well as major other international health care systems. It also surveys other health care informatics application domains that build on or complement electronic health record systems.

This course provides a technical overview of the current computing and information technology systems, programing languages and software development tools available to manage, access and analyze health and biomedical research information effectively in patient care and research settings. Course work includes lectures, demonstrations and readings as well as individual and group hands-on problem exercises with test versions of representative current electronic health record and other health information databases, programming languages and internet/web health information portals.

Focusing on clinical data and research, this course surveys the essential elements of statistical data analysis methods and research strategies that are needed for health and biomedical research information systems and for health information management applications for clinicians and researchers.

This course is required in the first two semesters of the MS degree program to give each student experience working directly with up to four members of the BMI faculty. Each semester will include work on two different faculty-mentored research or practicum projects. Each project will involve regularly scheduled weekly or bi-weekly time learning about, and helping with some aspect of a faculty member’s research or working under a faculty-member’s guidance on an informatics application development or other practicum project in a health care, public health or health-industry setting.

This course, or BMI 611, is required in the final two semesters of the MS degree program. Students choosing this option will complete a biomedical informatics research project under the guidance of a member of the BMI faculty and a thesis advisory committee. The thesis research will culminate with the completion of a paper suitable for publication as well as a final oral defense, including an oral presentation of the results of the research project.

This course, or BMI 610, is required in the final two semester of the MS degree program. Students choosing this will complete a project to evaluate, test or help implement a health information system or information management process in a health care, public health, or health-related industry setting under the supervision of a member of the BMI faculty as well as a professional informatics mentor. The practicum project will culminate with the completion of a paper suitable for publication as well as a final oral presentation of the results of the practicum project.

Building on BMI 501, 502, 504 and 506 as prerequisites, this course provides an in-depth survey of the data standards, data analytic methods, data analysis tools, databases and information management systems and applications associated with clinical-genomic population research and the U.S. public health system. Students will learn clinical trial design and data analysis for varying populations. Students will learn genetic epidemiology. Methods will include hierarchical clustering, vector space methods, semantic clustering, machine learning, modeling and simulations (including bootstrap methods). Students will learn linear and non-linear methods of data analysis. Students will be given an introduction to complexity theory and will be shown some methods for reducing dimensionality in complex systems (including computing techniques).

Building on BMI 504 or an equivalent introductory course in biomedical statistics as a prerequisite, this course provides doctoral students with the ability to effectively understand and use a number of key advanced statistical analysis methods and tools used in biomedical informatics research. These include regression and correlation analysis, the analysis of variance and covariance, distribution-free and nonparametric analysis methods and the methods used in demography and vital statistics analysis.

Building on introductory overviews provided in BMI 503 and 504, this course provides an in-depth introduction to the needs, challenges, standards, software applications and tools for biomedical data mining and natural language processing. The most common biomedical data mining methods are reviewed with lab time for using the IBM SPSS Modeler with problem datasets. Similarly, the steps needed to automate the processing and analysis of electronic biomedical text are reviewed, with lab time for using the GATE software package with NLP problem sets. The course concludes with an in-depth review of the unique challenges of processing clinical language and a look at current NLP published research.

Building on the introduction to BMI research methods in BMI 504, this course provides an in-depth review of the methods for conducting effective and unbiased evaluations of health information systems, including economic or cost analysis studies and the challenges associated with these methods. The course includes an exploration of the place of evaluation within the field of biomedical informatics; the major objectivist (quantitative) and subjectivist (qualitative) evaluation study methods; the motivations and methods for economic (cost) analysis as a component of evaluation studies; and the strategies for proposing evaluation studies, communicating their results and dealing with ethical, legal and regulatory issues associated with information systems evaluation.

This course provides an overview of the methods, systems, tools and databases available for the storage, analysis and interpretation of the increasingly voluminous molecular genome and protein data. The course focuses on the use of these biological data for research in molecular biology, systems biology, genetics and genomics as well as for translating and integrating biological data with clinical health care data to help predict and prevent disease and help clinicians, patients and consumers understand and use this information to maintain health. The course also includes a brief review of current core terminology and concepts in molecular biology, systems biology, genetics and genomics for students without previous course work or training in biomedicine.

Building on BMI 501 as a prerequisite, this course provides an in-depth survey of the data standards, data analysis tools, databases and information management systems and applications associated with clinical population research and the U.S. public health system.

Building on BMI 501 as a prerequisite, this course provides an in-depth exploration of the purpose, scope, technical structures and uses of electronic health records (EHR) and other clinical health care information systems.  Then, building on a review of current research on human cognition and decision making, the course critically reviews the purposes, scope, technical structures and ethical uses of computer-based decision support systems in clinical health care and consumer health settings.

Building on BMI 501 as a prerequisite, this course first provides a review of the theories underlying biomedical knowledge generation and the methods and tools for knowledge acquisition, modeling and representation as well as the management and maintenance of biomedical knowledge sources. The second part of the course provides an in-depth review of current theories and research underlying the development of biomedical ontologies as well as a comparative critical analysis of the major current biomedical ontologies and the methods and tools for biomedical ontology development and evaluation.

Building on BMI 501 as a prerequisite, this course reviews the interdisciplinary theoretical frameworks, design concepts and analytical foci used in human factors engineering and ergonomics for biomedical information systems. These include the physical, cognitive, organizational/social and environmental challenges of human-computer interactions and a range of human factors approaches to systems design and evaluation. The course also looks at the mediating roles of information technology on clinical and research user performance and the potential implications of a range of innovative new design concepts for biomedical information systems.

This course provides an overview of core business concepts for students in Biomedical Informatics. For those students who will enter management roles such as Chief Medical Informatics Officer, an understanding of business processes and methods is essential for professional success. In addition to introducing basic business topics such as accounting and finance, this course will also emphasize leadership skills, including change management and emotional intelligence.

In consultation with his/her faculty advisor, each student may elect to explore a particular area of biomedical informatics in more depth with a member of the BMI faculty with expertise in this area of research or application development. The choice of topic areas will depend, in part, on the availability of the faculty with expertise during the semester the student is seeking this kind of elective. In addition, the number of credit hours and the format of the course will depend on the interests and needs of the student and whether other students are interested in taking an elective on the same topic area. Each special topic elective will include, at a minimum, a program of background, in-depth readings and “laboratory” hands-on work with resources and tools needed in this topic area of BMI research and application development.

In consultation with his/her faculty adviser, each student may also choose, as an elective, to participate in a more advanced research project with a faculty mentor. This could be to learn more about the research methods that will be needed to complete the student’s thesis or, for students planning to continue beyond the master’s degree to a PhD, to explore another possible area of research focus for a doctoral dissertation. The number of credit hours will depend on the interests and needs of each student.