Curriculum
Below is the course of study for the Health Informatics Minor. We also provide a sample plan of study for students.
Core courses (3 course units):
BMIN 502 |
This course is intended to provide in-depth, practical exposure to the design, implementation, and use of databases in biomedical research, and to provide students with the skills needed to design and conduct a research project using primary and secondary data. Tiopics to be covered include: database architectures, data normalization, database implementation, client-server databases, concurrency, validation, Structured-Query Language (SQL) programming, reporting, maintenance, and security. All examples will luse 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. Taught by: Holmes Course usually offered in spring term Also Offered As: EPID 635 Activity: Lecture 1.0 Course Unit |
NURS5730 |
Innovation, defined as a hypothesis-driven, testable, and disciplined strategy, is important to improve health & healthcare. Employing new ways of thinking, such as with design thinking, will help open up possibilities of ways to improve health & the process of healthcare. Incorporating current & emerging social & digital technologies such as mobile apps, wearables, remote sensing, and 3D printing, affords new opportunities for innovation. This course provides foundational content & a disciplined approach to innovation as it applies to health & healthcare. A flipped classroom approach with the in-class component focusing on group learning through design thinking activities. The course is open to undergraduate nursing students as a case study & upper-level undergraduates and graduate students from across the Penn campus. The course provides a theoretical foundation in design thinking & may provide an overview of innovation technology & digital strategies as well as social & process change strategies. To enhance the didactic component, students will actively participate in a design case study. Students will be matched by interest and skill level with teams & will work with community-based organizations, healthcare providers and/or innovation partners. Student teams will meet their partners to identify & refine a health or healthcare problem to tackle. Students will work throughout the semester to create an innovative solution that will be pitched to their community-based organization, healthcare provider, and/or innovation partner at the end of the semester. Taught by: Leary Course usually offered in fall term Also Offered As: NURS 357 Prerequisites: Completed freshman & sophomore level courses or graduate student status. Activity: Lecture 1.0 Course Unit |
NURS6510 |
This course is designed to introduce the student to fundamental concepts and issues surrounding technology and information management in today’s rapidly changing health care environment. Emphasis will be placed on defining informatics and the models and theories used in its development. To prepare the student to take a leadership role in information system design and selection the class will study the process of information systems analysis, implementation and evaluation involving functional, organizational and human aspects. |
Elective courses (1 course units):
BMIN 501 |
This course is designed to provide a survey of the major topic 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. Taught by: Holmes Course usually offered in fall term Also Offered As: EPID 632 Activity: Lecture 1.0 Course Unit |
BMIN 503 |
In this course, we will use R and other freely available software to learn fundamendal 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 explanatory 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. Taught by: Himes Course usually offered in fall term Also Offered As: EPID 600 Activity: Lecture 1.0 Course Unit |
BMIN 505 |
TBA |
CIS 519 |
Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real problems. This course will introduce supervised learning (decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning. Additionally, the course will discuss evaluation methodology and recent applications of machine learning, including large scale learning for big data and network analysis. One-term course offered either term Also Offered As: CIS 419 Prerequisite: CIS 121 or an equivalent course in data structures Activity: Lecture 1.0 Course Unit |
CIS 545 |
Course usually offered in spring term Activity: Lecture 1.0 Course Unit |
CIS 560 |
This course focuses on programming the essential mathematical and geometric concepts underlying modern computer graphics. Using 3D interactive implementations, it covers fundamental topics such as mesh data structures, transformation sequences, rendering algorithms, and curve interpolation for animation. Students are also introduced to two programming languages widely used in the computer graphics industry: C++ and GLSL. The curriculum is heavily project-based, and culminates in a group project focused on building an interactive first-person world exploration application using the various real-time interaction and rendering algorithms learned throughout the semester. Course usually offered in fall term Also Offered As: CIS 460 Prerequisites: CIS 120 (CIS 120 and CIS 240 are useful pre- or co-requisites) Activity: Lecture 1.0 Course Unit |
DYNM 646 |
The U.S. worplace has long been one of the foremost spheres in which racial and ethnic inequality is created and perpetuated. This course investigates how racial and ethnic inequality affect our experiences in the workplace as well as how we as employees, managers, and the like, can positively impact upon our work environments against bias to promote equality and inclusion. Although most Americans largely perceive the employment relationship as one’s personal relationship with his/her “boss,” one’s occupation and/or “job” encompasses much more than that. How we come to work at the jobs that we do is about our access to larger institutional structures within society including education, family background, and importantly, our ascribed location within the social hierarchy. In the first part of the course, we focus on understanding race and ethnicity as dynamic social and political constructs that evolve through time and space and to recognize how these constructs are related to social stratification, intergroup and intragroup relations, and economic and political hierarchies within U.S. society. Some of the key concepts we explore are: race, prejudice, discrimination, class, racism, ethnicity, identity, migration and immigration, segregation, and civil rights. The objective here is to introduce you to the study of race and ethnicity within the U.S. context and to provide a better understanding of how and why race continues to be such a powerful stratifying agent. For the rest of the semester, we will examine how workplace inequality gets reproduced along racial and ethnic fault lines. We will study in-depth how and why personal and organizational biases remain mechanisms of inequity as well as how social class and gender intersect with race/ethnicity to contribute to workplace discrimination. Taught by: Torres Prerequisites: Course permits for non-DYNM students: https://www.sas.upenn.edu/lps/graduate/dynamics/course-permit Activity: Seminar 1.0 Course Unit Notes: DYNM Category: F. DYNM Concentrations: LMC, GL |
ESE 545 |
Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, students will learn to apply, analyze and evaluate principled, state-of-the-art technique s from statistics, algorithms and discrete and convex optimization for learning from such large data sets. The course both covers theoretical foundations and practical applications. Course usually offered in spring term Prerequisites: ESE 530, ENM 503, or equivalent Activity: Lecture 1.0 Course Unit |
HCMG 857 |
In healthcare or anywhere else across science, or business, or sports, the importance of data and analytics (“D&A”) is virtually unquestioned. That, however, does not mean that it needs no elucidation. We posit that precisely because of its ubiquity, leaders have fallen into a pattern of both overuse and over dependency on D&A. In this course, we begin with a fundamental understanding of the state of D&A in healthcare and then move onto examples of its use in converting from business questions to implemented solutions. We “sidestep” into the world of algorithms/machine-learning/AI and causal inference, but our focus is on business applications of these tools to the available data in the healthcare industry. As we discuss examples, we always seek to show how human creativity needs to be at the heart of the questions being probed. We highlight today’s data universe in healthcare, the level of integration we have achieved, and the immensity of the remaining task, all with an eye to the business opportunities that exist now. We end with a showcasing of the art of the possible - in 2019 - and with (hopefully) a clear look ahead at what remains to be achieved. A curious mind, enthusiastic contribution to the class discussion (35% of your grade), a single team-based case write-up (15% of your grade), and a final project (50% of your grade) form the bulk of preparation needed for the course. Taught by: Grennan, Mahadevan Course usually offered in fall term Activity: Lecture 0.5 Course Units Notes: Pursuit of MBA - we expect most students to be 2nd years. We assume no prior knowledge of statistical tools/concepts beyond the level of the MBA Core, though of course further training in data science is a plus, and we welcome those with more advanced preparation. |
HCMG866 |
This course will introduce students to the main components of Health Information Technology (HIT) and how HIT currently effects, and in the future, may change health care operating models. Although it will not prepare students for primary technology management positions, it will help them understand the role of information technology in the success of the delivery system and other important healthcare processes. It will provide a foundation that will prepare them as managers, investors and consultants to rely upon or manage information technology to accomplish delivery system objectives. The course will give special attention to key health care processes, and topics such as the drive for provider quality and cost improvements, the potential ability to leverage clinical data for care improvement and product development, the growth of new information technologies for consumer directed healthcare and telemedicine, the strategies and economics of individual HIT companies and the role of government. The course relies heavily on industry leaders to share their ideas and experiences with students. |
NURS5480 |
This course examines the process that leads to change in health care settings and situations. Students will develop skills that lead to effective negotiations in interpersonal and organizational settings. Included in the discussion are: concepts of organizational structure and power, negotiating in difficult situations, and the role of the health care professional in negotiation and change. The course also examines techniques leading to successful implementation of negotiated change in the practice setting.This course satisfies the Society & Social Structures Sector for Nursing Class of 2012 and Beyond. |
NURS6120 |
Healthcare delivery is complex and constantly changing. A primary mission of leading healthcare organizations is to advance the quality of patient care by striving to deliver care that is safe, effective, efficient, timely, cost-effective, and patient-centered (Institute of Medicine). The goal of this interprofessional course is to provide students with a broad overview of the principles and tools of quality improvement and patient safety in healthcare as well address the knowledge, skills and attitudes as defined by the Quality and Safety Education for Nurses (QSEN) guidelines. It will provide a foundation for students or practicing clinicians who are interested in quality improvement and patient safety research, administration, or clinical applications.Content will address the history of the quality improvement process in healthcare, quality databases and improvement process tools and programs. Through the use of case studies and exercises students will be become familiar with the use of several quality improvement programs and tools. For example, the Plan-Do-Study- Act (PDSA) cycle, Six Sigma and the Toyota Production System known as Lean Production processes will be addressed. Students can use this course to identify the tools and design the methods that they plan to employ in a quality improvement or patient safety project in their area of interest. Also offered as HQS 612. |
NURS6500 |
This blended online/in-classroom graduate level course integrates principles of systems thinking with foundational concepts in patient safety. Utilizing complexity theories, students assess healthcare practices and identify factors that contribute to medical errors and impact patient safety. Using a clinical microsystem framework, learners assess a potential patient safety issue and create preventive systems. Lessons learned from the science of safety are utilized in developing strategies to enhance safe system redesign. Core competencies for all healthcare professionals are emphasized, content is applicable for all healthcare providers including, but not limited to, nurses, pharmacists, physicians, social workers and healthcare administrators, and may be taken as an elective by non-majors. Also offered as: HQS 650. |
NURS8490 |
The growth and development of electronic health records, genetic information, sensor technologies and computing power propelled health care into the big data era. This course will emphasize data science strategies and techniques for extracting knowledge from structured and unstructured data sources. The course will follow the data science process from obtaining raw data, processing and cleaning, conducting exploratory data analysis, building models and algorithms, communication and visualization, to producing data products. Students will participate in hands-on exercises whenever possible using a clinical dataset. Taught by: Bowles, K; Milo Course usually offered in spring term Prerequisites: Students must have completed at least two, sequential graduate level statistics courses. Activity: Lecture 1.0 Course Unit |
PUBH 565 |
Also Offered As: NURS 353, NURS 565 Activity: Lecture 1.0 Course Unit |