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Perelman School of Medicine at the University of Pennsylvania

Master of Biomedical Informatics

Classroom

Course Descriptions

The following courses are required for all MBMI students (click the arrow for details about each course):

BMIN 501: Introduction to Biomedical Informatics

Offered during the fall semester, Mondays, 4-7pm (1 CU)

Course director: Fuchiang (Rich) Tsui, PhD

This course is designed to provide a survey of the major topics areas in medical informatics, especially as they apply to clinical research.  Through a series of lectures and demonstrations, students will learn about topics such as databases, natural language, clinical information systems, networks, artificial intelligence and machine learning applications, decision support, imaging and graphics, and the use of computers in education. (This course has been offered in the past as EPID 632.)

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BMIN 502: Databases in Biomedical Research

Offered during the spring semester, Tuesdays, 4:30-7:30pm (1 CU)

Course director: Dokyoon Kim, PhD

This course is offered during the spring semester and is intended to provide in-depth, practical exposure to the design, implementation, and use of databases in biomedical research. This course is intended to provide students with the skills needed to design and conduct a research project using primary and secondary data. Topics to be covered include: database architectures, data modeling approaches, data normalization, database implementation, client-server databases, concurrency, validation, Structured-Query Language (SQL) programming, reporting, maintenance, and security. All examples will use problems or data from biomedical domains. MySQL will be used as the database platform for the course, although the principles apply generally to biomedical research and other relational databases.  (This course has been offered in the past as EPID 635.)

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BMIN 503: Data Science

Offered during the fall semester, Tuesdays and Thursdays, 1:30-3pm (1 CU)

Course director: Blanca Himes, PhD

This course will use R and other freely available software to learn fundamental data science applied to a range of biomedical informatics topics, including those making use of health and genomic data. After completing this course, students will be able to retrieve and clean data, perform exploratory analyses, build models to answer scientific questions, and present visually appealing results to accompany data analyses; be familiar with various biomedical data types and resources related to them; and know how to create reproducible and easily shareable results with R and github.  (This course has been offered in the past as EPID 600.)

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BMIN 504: Special Topics in Biomedical Research

Offered during the spring semester, Mondays, 4-7pm (1 CU)

Course director: Jason Moore, PhD

This course is designed to provide an in-depth look at four topics that are of essential importance in biomedical informatics. Each topic will be allotted four consecutive weeks in the class schedule, as four modules, with the intention that each module becomes its own “mini-course”. The topics for each module may rotate from semester to semester, based on these criteria:

  • Historical importance to the current field of biomedical informatics research and/or practice
  • Cutting-edge developments in biomedical informatics
  • Topics not covered in depth in BMIN 501
  • Consensus of the program leadership and teaching faculty

Possible modules include:

  • Deep learning methods for mining biomedical data
  • Visualization analytics for clinical research
  • Methods for integration of observational and ecological data for public health surveillance
  • Informatics implications for distributed research networks
  • Human computer interaction and patient safety
  • Nature-inspired analytics for biomedical informatics
  • Network science applications in biomedical informatics
  • Intersections of clinical research, clinical and clinical research informatics, and clinical decision making

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BMIN 505: Precision Medicine and Health Policy

Offered during the spring semester, Tuesdays, 5-8pm (1 CU)

Course director: Mary Regina Boland, PhD

This course is designed to provide an in-depth exploration in various health policy implications of informatics research and ways to incorporate precision medicine science into the healthcare system using informatics. The course will be divided into four modules. The first will cover topics regarding hospital performance, comparing hospitals with each other using standardized metrics and algorithms, quality-of-care assessments and will engage students to learn ways to improve on the current standards using informatics approaches. The second module will focus on understanding biases in the current clinical practice guidelines and informatics methods designed to assess these biases and improve guidelines for the future. We will touch on topics such as ethnicity bias: e.g., many medications were not tested on diverse populations and this could have important implications for members of those populations; gender bias: e.g., many laboratory value ‘normals’ are not gender-specific but were generated on male-only populations. The implications that these factors was/may have in future will be discussed along with informatics solutions. The third module will focus on interpretation of genetic results, focusing on gene variants with known clinical implications. Several different types of genetic variants will be discussed (e.g., variant of uncertain significant, potentially deleterious, etc.) and their implications for the design of clinical decision support tools. The fourth module will center around the prenatal genetic testing space and the implications for patients, and providers regarding having detailed genetic information from prenatal through to birth and beyond. The implications this will have in the next decade of clinical science will be discussed along with health policy implications, and the role of clinical decision making in this space.

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BMIN 506: Standards and Vocabularies

Offered during the first half of the fall semester, Wednesdays, 4-7pm (.5 CU)

Course director: Michael Padula, MD, MBI

This survey course is designed to provide an overview of health information standards and clinical terminologies.  Through a series of lectures, demonstrations, and hands-on exercises, students will learn about topics such as standards, interoperability, data modeling, vocabularies, and health information exchange. 

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BMIN 507: Human Factors

Offered during the second half of the fall semester, Wednesdays, 4-7pm (.5 CU)

Course directors: Ross Koppel, PhD and Julia Szymczak, PhD

The course will cover five main topic areas: 1. usability, 2. evaluation and measurement of usability, 3. workflow, 4. user-centered design, 5. implementation, and 6. continuing improvement/optimization. Each topic area will incorporate principles, methods, and applications. In the principles section for each topic, the course will clearly define terminology related to the topic area (e.g., What is workflow?), review how key concepts relate to each other (e.g., relationship between human factors engineering and human-computer interaction), and examine the relevance of the topic area in Applied Clinical Informatics. The methodology section for each topic will address qualitative, quantitative, and computational methods used for the design, implementation, and evaluation of health information technology. The applications section for each topic will use case studies based in the topic area to examine the real-world application of principles and methods. The course will cover a wide range of contexts, from homes/communities to organizations to a broader regional scale.

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HPR 611: Implementation Science

Offered during the fall semester (1 CU)

Course Directors: Frances K. Barg, PhD, MEd, Judy Shea, PhD and Rinad Beidas, PhD

The course is largely case‐based, evaluating examples of both successful and unsuccessful translational efforts. The structure of the course will focus on 3 successive stages—(1) organizational theory for designing interventions designed to change practice, (2) implementation strategies for optimizing adoption, and (3) program evaluation methods for assessing the success or failure of implementation. Specific tools in qualitative and mixed methods approaches will be emphasized. Master's students and professionals involved & interested in implementation science are eligible.

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BMIN 508: Capstone

Offered during the spring semester (1 CU)

With mentorship from their Capstone Advisor, students will develop and present the results of a clinical informatics project relevant to their interests. During this semester-long course, students will attend a weekly seminar in which they develop, propose, implement, and present their capstone project. Students meet with regularly with their Capstone Advisor, who is also invited to attend the seminars. The seminar affords both students and advisors the opportunity for cross-fertilization of ideas and skills, and ultimately the honing of projects to a high level of value for the students and the clinical environments in which they conduct their projects.

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In addition, students must take 2 course units (CUs) of elective coursework. Possible electives include, but are not limited to:

         Telehealth and mHealth Systems and Applications (BMIN 509)

          Ai I:  Intro to Ai (BMIN 520)

          Ai II:  Machine Learning (BMIN 521)

          Ai III:  Natural Language Process (BMIN 522)

          Introduction to Python Programming (BMIN 525)

          Introduction to Machine Learning (CIS 519)

          Big Data Analytics (CIS 545)

          Data Visualization and Interaction Design (CIS 560)

          Organizational Project Management (DYNM 619)

          Process Improvement Tools and Strategies (DYNM 634)

          Longitudinal and Clustered Data (EPID 621)

          Data Mining (ESE 545)

          Introduction to Bioinformatics (GCB 535)

          The American Health Care System (HCIN 600)

          Health Care Operations (HCIN 601)

          Behavioral Economics and Decision Making (HCIN 602)

          Evaluating Health Policy and Programs (HCIN 603)

          Health Economics (HCIN 604)

          Translating Ideas Into Outcomes (HCIN 607)

          Leading Change in Health Care (HCIN 617)

          Medical Devices (HCMG 853)

          Comparative Health Care Systems (HCMG 859)

          E-Health: Business Models and Impact (HCMG 866)

          Qualitative Methods Research (HPR 503)

          Principles and Practice of Quality Improvement and Patient Safety (HPR 504)

          Clinical Economics and Decision Making (HPR 550)

          Systems Thinking in Patient Safety (HPR 650)

          Decision Models and Uncertainty (OIDD 621)

          Decision Support Systems (OPIM 672)

          Health Communication in the Digital Age (PUBH 565)

Courses not on the above list must receive approval from the Program Director. Transfer of credit from external institutions may be possible for some students, subject to approval.