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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 5020
Database and Data Integration in Biomedical Research

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. Topics 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 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. NOTE: Non-majors need permission from the department


1 Course Unit

Innovation in Health: Foundations of Design Thinking

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

Healthcare Informatics

Healthcare systems and consumers today are becoming increasingly reliant on information technology. The objective of this course is to provide a foundation for knowledge about health information technology and to expose students, clinicians, and administrators to the breadth of tools and systems currently used in practice. We will explore topics such as mobile health applications/telehealth and their implications for clinical practice and impact on patient outcomes; electronic health records, data analytics, and visualization tools and how these can effectively be used to support decision making and patient care.

Elective courses (1 course units):

BMIN 5010
Introduction to Biomedical and Health Informatics

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 medical data standards, electronic health record systems, natural language processing, clinical research informatics, clinical decision support, imaging informatics, public health informatics, and consumer health informatics. It is recommended that students have basic familiarity with biomedical concepts. Non-majors need permission from the department.


1 Course Unit

BMIN 5030
Data Science for Biomedical Informatics

In this course, we 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 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. Recommended prerequisite: Introductory-level statistics course. Familiarity with programming or a willingness to devote time to learn it. NOTE: Non-majors need permission from the department.


Also Offered As: EPID 6000

1 Course Unit

BMIN 5060
Standards and Clinical Terminologies

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. It is recommended that students have completed BMIN 5010 prior to enrolling in this course. NOTE: Non-majors need permission from the department.


0.5 Course Units

CIS 5190
Applied Machine Learning

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.

Fall or Spring

Mutually Exclusive: CIS 4190

Prerequisite: CIS 1210

1 Course Unit

CIS 5450
Big Data Analytics

In the new era of big data, we are increasingly faced with the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification. Recommended: broad familiarity with probability and statistics, as well as programming in Python. Additional background in statistics, data analysis (e.g., in Matlab or R), and machine learning is helpful (example : ESE 5420).

Fall, Spring, and Summer Terms

1 Course Unit

CIS 5600
Interactive Computer Graphics

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.


1 Course Unit

DYNM 6460
Race, Ethnicity, and the American Workplace

The U.S. workplace 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. At the beginning of the course, we will spend time studying race and ethnicity as dynamic social and political constructs that evolve through time and space. We will examine how these constructs relate to social stratification, intergroup and intragroup relations, and economic and political hierarchies within U.S. society. The objective here is to provide you with a better understanding of how and why race continues to be such a powerful stratifying agent in contemporary America. We will spend time discussing the enduring power of structural racism in U.S. society–it’s evolution since slavery, and its ability to restrict Black & Brown achievement and success within all spheres. How has the Covid-19 pandemic and the BLM movement further brought to light the rigidity of our peculiar system of racial stratification? How can we work to promote true equity and inclusion now? How can we come to work as our “authentic selves” where everyone has a seat at the table? What has history taught us about these issues? And, how can we learn both as individuals and members of organizations to make racial diversity, equity, and inclusion normative experiences for all? Work is a microcosm of our broader lived experiences and it is likely the most “diverse” place we experience in our lifetime. Simultaneously, we will focus on understanding history and evolution of diversity, equity and inclusion practices in the workplace as they relate to addressing racial and ethnic inequality. How have diversity and inclusion practices in the private and public sector evolved over time? How do these practices reflect broader historical and societal trends concerning social and racial inequality? What does it mean to go from compliance to commitment? Have we moved from “diversity for its own sake” to true and meaningful inclusion? What kinds of new initiatives and commitments have organizations made since the BLM protests this summer? How has BLM impacted the experiences of employees of color to-date and where are things headed now? For the rest of the semester, we will examine how workplace inequality gets produced and reproduced along racial and ethnic fault lines. Do D, E, & I programs tailored to distinct groups alleviate issues of marginalization for employees? Why are successful D, E, & I programs profitable for big business? In addition, we will examine the intersections of race, gender, and class in the workplace; how do these intersections impact how we address inequality in hiring, promotions, and recidivism? 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. We will host several guest lecturers throughout the semester. Non-Dynamics students: please include a brief job description in your Permissions Request.

1 Course Unit

ESE 5450
Data Mining: Learning from Massive Datasets

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.


1 Course Unit

HCMG 8570
Healthcare Data and Analytics

Health care data creates unparalleled opportunities to save lives, improve health, strengthen the health care workforce, reduce costs, and increase efficiency. But it also presents a unique set of challenges ranging from privacy to data consistency. In this course, we begin by surveying the health care data landscape and then turn to how to use this rich data to better manage care and organizations. We will refine the art of asking good questions and gain first-hand experience applying analytics to answer them. We will also examine innovative businesses focused on health care data and analytics. At the end of this course, students will: (1) Understand the topography of the health care data landscape, (2) Have the skills necessary to be thoughtful consumers of evidence on health care, (3) Be able to use data and analytics to improve care and health care management, and (4) Anticipate business opportunities in health care data and analytics.


Mutually Exclusive: HCMG 3570

0.5-1 Course Unit

HCMG 8660
The Digital Transformation of Health Care

Healthcare is in the early stages of extraordinary change in the business model of care delivery and financing. This transformation will lead to a system based on the proactive management of health, integration of care across the continuum, blurred boundaries between care providers and purchasers and the placement of the consumer at the center. As has been the case in other industries, this new business model will be based on a foundation of diverse, potent, and well implemented information technology. This course will help prepare students to lead a digital health future. Specifically, the course will cover three major areas. (1)The context of health care information technology: the size, composition and evolution of the digital health market; federal government agencies, and related regulations, that shape the market; leadership roles and factors that enable healthcare organizations to effectively implement and leverage information technology. (2)Emerging technologies that will fuel the transformation of healthcare: artificial intelligence and advanced analytics; interoperability; telehealth; consumer-directed digital health; use of behavioral economics to influence patient and provider decisions. (3)Digital health use by specific sectors of the healthcare industry: healthcare providers; health plans; retail-based primary care; life sciences; wellness and chronic disease management. The course will include lectures from industry leaders who will share their ideas and experiences.


0.5 Course Units

Negotiations in Healthcare

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.

Principles and Practice of Healthcare Quality Improvement

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.

Systems Thinking in Patient Safety

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.

Exploring Data Science Methods with Health Care Data

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.


Also Offered As: BMIN 5490

1 Course Unit

PUBH 565
Health Comm Digital Age

Also Offered As: NURS 353NURS 565

Activity: Lecture

1.0 Course Unit