Course Catalog

(number of credits in parenthesis)
Application in Econometrics and Data Analysis (3)
Artificial Intelligence in Medicine (3)

Course Director: Fei Wang, Ph.D.

Big Data in Medicine (3)

Course Director: Samprit Banerjee, Ph.D., MStat 

There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT-Scan, X-ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above-mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them.

Biostatistics I with R Lab (4) HBDS 5005 01

Course Director: Karla Ballman, Ph.D.

This course provides an introduction to important topics in biostatistical concepts and reasoning. Specific topics include tools for describing central tendency and variability in data, probability distributions, sampling distributions, estimation, and hypothesis testing. Assignments will involve computation using the R programming language.

Biostatistics II — Regression Analysis (3) HBDS 5008 03

Course Director: Samprit Banerjee, Ph.D., MStat 

Prerequisite: Biostatistics I

The focus of this course is theory and application of different types of regression analysis. Topics will include: linear regression, logistic regression, and cox proportional hazards regression. Additional topics will include coding of explanatory variables, residual diagnostics, model selection techniques, random effects and mixed models, and maximum likelihood estimation. Homework assignments will involve computation using the R statistical package.

Categorical and Censored Data Analysis (1.5) HBDS 5016 02

Course Director: Oleksandr Savenkov, Ph.D.

The course will describe methods related to categorical data analysis and basic concepts for censored data and Kaplan-Meier; and learn how to select appreciate methods and how to interpret the results from categorical data analysis and Kaplan-Meier.

Causal Inference with Machine Learning (3)
Clinical Informatics (3) HINF 5011 04

Course Directors: Sameer Malhotra, M.B.B.S., M.A. and David Artz, M.D., MBA

Prerequisites: Introduction to Health Informatics

Clinical information systems such as electronic health records are central to modern healthcare. This course introduces students to the complex infrastructure of clinical information systems, technologies used to improve healthcare quality and safety (including clinical decision support and electronic ordering), and policies surrounding health information technology.

Cost Effectiveness Analysis (3) HPEC 5005 03

Course Director: Sean M. Murphy, Ph.D.

Prerequisites: Biostatistics I or Introduction to Biostatistics

The cost effectiveness analysis course is a 2 part course. The first part provides an overview of techniques used to understand medical decision making under uncertainty. Participants will learn how to structure decision analysis questions, construct decision trees, and analyze outcomes using probability. The second part provides an in-depth exposure to techniques used to conduct economic evaluations of health care technologies and programs. Participants learn how to critique economic evaluations using cost-effectiveness approaches and are introduced to tools they can use to apply these techniques in their own research projects.

Data Management (SQL) (3) HINF 5018 31

Course Directors: Yiye Zhang, Ph.D. & Debra D'Angelo, M.S.

This course covers tools that students will need to create, manage and maximize value from big databases. The emphasis is on design and implementation of relational databases and the use of Structured Query Language (SQL). At the end of this course, students will be able to explain the requirements for handling large and complex datasets; be able to design, build, and query a relational database; and understand how relational databases and big-data targeted tools complement one another.

Section A (Recommended for HI): This section, designed for health informatics students, is formatted in a lecture-lab format. In addition to SQL programming exercises, this course will introduce additional information about practical data management in healthcare and research settings, and an introduction to nonrelational database structure using Python programming language.

Section B (Recommended for BDS): This section, designed for biostatistics and data science students who wish to improve their data management skills, is formatted as a flipped classroom, with video lectures watched before class, and class time devoted to programming assignments. Emphasis is on practical programming exercises in SQL using relational database software, R, and SAS.

Data Science I (R and Python) (3) HBDS 5018 01

Course Director: Elizabeth Sweeney, Ph.D.

This course provides an introduction to data science using both the R and python programming languages. In this course students will gain experience working directly with data to pose and answer questions. The course will be divided into two parts; the first part will be taught with the programming language R and the second with python. Topics covered include: reproducible research, exploratory data analysis, data manipulation, data visualization techniques, simulation design, and unsupervised learning methods.

Data Science II - Statistical Learning (3)

The course starts with logistic regression and discriminant analysis with emphasis on classification and prediction. This course would cover some of more advanced topics such as regularized regression, resampling methods, tree-based methods and support vector machines.

Design and Analysis of Biomedical Studies (3)

Course Director: Kathy (Xi) Zhou, Ph.D.

The course covers topics important in the application of statistical methods and relevant statistical software packages (primarily R) to biomedical studies, with an emphasis on applications in the design and analysis related to biomedical experiments, clinical trials and observational studies. The course uses real-world case studies to introduce commonly used experimental designs in biomedical research and discuss a variety of statistical methods and analytic tools for analyzing data generated from those studies. The course promotes good statistical/analytical practice through the introduction of several widely adopted reporting guidelines and tools for carrying out reproducible data analysis. The course aims to help students develop expertise in applying statistical methods and analytical tools, including developing their own R packages, to solve the design and data analysis challenges in biomedical studies and beyond.

Foundations of Health Policy and Economics (3) HPEC 5001.01

Course Director: Hye-Young (Arian) Jung, Ph.D.

This course provides an introduction to basic economic concepts associated with health care and current policy issues facing the US health care system. Topics will include the historical foundations of the health care system, how the health care sector differs from other markets, financing of health care and the role of government, the structure and functions of public and private health insurance, economic components of the delivery system, and understanding the challenges of health care reform. These topics will be examined from the view of payers, providers, and regulators, and the interactions of these stakeholders. Students will also be introduced to international comparisons of health care systems.

Health Behavior and Consumer Informatics (3) HINF 5017 03

Consumer health informatics (CHI) is the study of consumer information needs and technologies that provide consumers with the information they need to be more engaged in self-care and healthcare. This introductory CHI course will present an overview of theories of health and information behavior; key concepts and terminology; and main application domains. We will explore how health behavior theories provide a framework for explaining consumers’ health behaviors and how CHI tools that are built with a theoretical foundation can promote health behavior change. The course will cover CHI applications in major application domains including electronic patient portals, mobile health (mHealth), and telehealth. Students will learn how to assess end-user needs and technological practices of potential users who experience health information and technological disparities. Students will also learn how to design for end-users, evaluate CHI applications and research.

Health Data for Research (SAS) (3) HPEC 5003 03

Course Director: Mark Unruh, Ph.D.

Addresses challenges in the use of electronic clinical data for research purposes, such as electronic health records, clinical data warehouses, electronic prescribing, clinical decision support systems and health information exchange. Students will learn how clinical processes generate data in these different systems, the tasks required to obtain data for research purposes and steps to prepare data for analysis. Examples of research uses of clinical data will be drawn from case studies in the literature. Students will acquire skills in data review, preparation and analysis through hands-on experience with clinical data.

Health Information Standards and Interoperability (3) HINF 5020 03

Course Director: Jyoti Pathak, Ph.D.

In modern healthcare. exchange of clinical data across multiple stakeholders — between healthcare organizations, between providers and patients, and among agencies and governmental entities — is pivotal. Health information standards provide the “backbone” to achieve uniform data interoperability and exchange across multiple heterogeneous systems. This course will introduce existing and emerging clinical data modeling, terminology and knowledge representation standards.

Healthcare in the US - Policy Making and Political Strategy (3)
Healthcare Organization and Delivery (3) HPEC 5002 01

The goal of this course is to help students understand the complexity and nuances of healthcare delivery. The course will include seminar-style lectures and discussions, along with opportunities to directly observe healthcare in such settings as a pediatric outpatient clinic, an adult emergency department, and a pathology lab. Lectures and discussions will not summarize healthcare; rather, they will analyze healthcare — through themes such as people, time, money, communication, decision making, and others. Students will come away from the course with a deeper appreciation of why it is difficult to change healthcare. They will then be able to anticipate the intended and unintended consequences of interventions and policies that they and others might implement.

Hierarchical Modeling and Longitudinal Data Analysis (3)
Incentives in the US Healthcare System (3) HPEC 5007 04

Course Director: Yuhua Bao, Ph.D.

Economic incentives embedded in the health care system shape the behaviors of key stakeholders. This course provides an overview and analysis of incentives in the current US health care system for consumers/patients, health care providers, payers and insurers, and other stakeholders such as pharmaceutical and medical device companies. Discussion centers around how the medical care market differs from markets for other goods and services and how incentives interact to affect health care delivery and outcomes. We then use the lens of incentives to examine the rationale and consequences – both intended and unintended – of major reform models designed to align incentives with improving the quality and experience of care while containing the growth of health care costs.

Introduction to Applied Econometrics for Health Policy (3) HPEC 5004 03

Prerequisites: Biostatistics I or Introduction to Biostatistics

With an emphasis on empirical applications, this course equips students with the tools necessary to empirically analyze non-experimental data at levels often required in professional environments. Applied Econometrics for Health Policy is designed with twin objectives in mind. The first is to provide students with the ability to critically analyze the empirical analysis done by others at a level sufficient to make intelligent decisions about how to use that analysis in the design of health policy. The second is to provide students with the skills necessary to perform empirical analysis on their own, or to participate on a team involved in such empirical analysis. Students will become proficient in using multiple regression analysis using cross-sectional and panel data, including in ways that provide causal interpretation.

Introduction to Biostatistics with Stata Lab (4) HBDS 5001 01

Course Director: Arindam RoyChoudhury, Ph.D.

An introduction to the fundamentals of biostatistics with primary emphasis on understanding of statistical concepts behind data analytic principles. This course will be accompanied with a Stata lab to explore, visualize and perform statistical analysis with data. Lectures and discussions will focus on the following: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; the development of statistical methods for analyzing data; and development of mathematical models used to relate a response variable to explanatory or descriptive variables.

Introduction to Health Informatics (3) HINF 5001 01

Health informatics is the body of knowledge that concerns the acquisition, storage, management and use of information in, about and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. The discipline of health informatics sits at the intersection of several fields of research – including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. In recent years we have witnessed how the collection, storage and usage of digital health data has exponentially grown. Increases in the complexity of health information systems have driven growth in demand for a specialized workforce. This course introduces the field of health informatics and provides students with the basic knowledge and skills to pursue a professional career in this field and apply informatics methods and tools in their health professional practice.

Introduction to Health Services Research (3) HBDS 5002 01

Course Director: Yiye Zhang , Ph.D., M.S.

This course is designed to introduce students to the fundamentals of health services research. Health services research is the discipline that measures the evaluations of interventions designed to improve healthcare. These interventions can include changes to the organization, delivery and financing of health care and various healthcare policies. Common outcome measures in health services research include (but are not limited to) patient safety, healthcare quality, healthcare utilization, and cost. Specific topics to be covered in this course include: refining your research question, identifying common research designs and their strengths and weaknesses, minimizing bias and confounding, selecting data sources, optimizing measurement, and more. There will also be a component of the course that explores how to present your ideas and iteratively refine your work, based on feedback from peers and reviewers. This course includes both lectures and interactive group discussions. Students will be able to apply the methods learned in this course to their masters’ research projects.

Introduction to Operations Research in Health Policy (3) HPEC 5009 01

Course Director: Nathaniel Hupert, M.D., MPH

Every component of health care delivery, from patient scheduling and bed management to information utilization and logistics, is amenable to improvement using approaches based on operations research (OR), the branch of engineering that calls itself “the science of better.”  This course will introduce students to key concepts and methods in OR, including queuing theory, simulation, and optimization.  Applications using common spreadsheet software and/or free online modeling applications will be emphasized. Student teams will then use these tools to design an efficient, high-performance outpatient clinic.

Introduction to US Health Care Policy and Delivery (3) HPEC 5001 01

This course provides an introduction to basic economic concepts associated with health care and current policy issues facing the US health care system. Topics will include the historical foundations of the health care system, how the health care sector differs from other markets, financing of health care and the role of government, the structure and functions of public and private health insurance, economic components of the delivery system, and understanding the challenges of health care reform. These topics will be examined from the view of payers, providers, and regulators, and the interactions of these stakeholders. Students will also be introduced to international comparisons of health care systems.

Leading Healthcare Transformation (3) HINF 5014 03

The US healthcare system is in the midst of transformational changes that have been catalyzed in part by the continued effects of the Affordable Care Act and the 2008 recession. This course will look at the major trends occurring in healthcare from a provider viewpoint, how leaders are both responding to and anticipating these changes, and how these changes will shape the healthcare system of the future. The goal of this course is to provide students with an understanding of the nature and context of the changes happening in healthcare, while also offering real-world perspectives from industry leaders who will speak to how they are adapting to and even shaping these changes in their roles. Upon completing this course, both clinical and non-clinical students will have gained greater insight into the healthcare system, which they will be able to apply to their current and future roles.

Master's Project I and Professional Development (1) HCPR 9010 01

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Master's Project II (2) HCPR 9020 03

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Master's Project III (3) HCPR 9030 03

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Pharmaceutical Statistics (3)
Research Methods in Health Informatics (3) HINF 5004 01

Informatics innovations have their desired impact only when they have high quality, are highly usable, are integrated into their organizational setting, and are widely adopted and used. That makes it critical for informatics students to understand not only how informatics innovations work, but also the users and settings in which they are used. Students will learn methods and models for: measuring data and system quality; assessing and predicting technology adoption (what makes technology sticky?); improving human-computer interaction via human factors engineering; understanding organizational and systemic challenges in the real world; influencing patients’ health behavior and decisions; and assessing quality, safety, and cost outcomes using health services research study designs. In this mixed methods course, students will gain experience using both quantitative and qualitative methods.

Statistical Programming with SAS (3) HBDS 5011 01

Course Director: Zhengming Chen, Ph.D., MPH, M.S.

This course provides introduction to the statistical software SAS. Students will receive a hands-on exposure to data management and report generation with one of the most popular statistical software packages.

Study Design (1.5) HBDS 5015 01

Course Director: Linda Gerber, Ph.D.

The course will describe and apply measures of disease incidence and prevalence, and measures of effect; explain the basic principles underlying different study designs, including descriptive, ecological, cross-sectional, cohort, case-control and intervention studies; assess strengths and limitations of different study designs; identify problems interpreting epidemiological data: chance, bias, confounding and effect modification; address validity, intra-rater reliability and inter-rater reliability.

Study Design and Comparative Effectiveness (3)
Study Designs and Methods for Comparative Effectiveness Research (3) HPEC 5006 04

Course Director: James Flory, M.D., M.S.C.E.

This course will cover the conceptual underpinnings, the policy context, and the methods for comparative effectiveness research (CER) highlighting key issues and controversies. It will provide students with an understanding of the analytic methods and data resources used to conduct comparative effectiveness research. Topics that will be discussed include, observational studies, risk adjustment, propensity score matching, instrumental variables, meta-analysis/systematic reviews and the use of clinical registries and electronic health record data. Students will learn why comparative research has come to prominence, what constitutes good comparative effectiveness research, the main methods used and the advantages and disadvantages of each without being a statistics course. Sessions will consist of lectures from the instructors and experts on selected topics, as well as student discussions and presentations.

Survey Research Methods (3) HPEC 5008 04

This course is intended to familiarize students with the theory and application of survey research methods, with an emphasis on application. It will lead students through the process of developing their own survey. Topics will include survey populations and sampling, development of survey instruments, survey administration, post-survey processing and data analysis. Recurring themes throughout these topics are common errors in surveys, their consequences for findings and strategies to minimize these errors in survey design. Students will learn to develop an original research proposal featuring a survey questionnaire as well as critically evaluate existing surveys. The course will be tailored to the specific needs and problems of participants to the extent possible.