Course Catalog

(number of credits in parenthesis)
Application in Econometrics and Data Analysis (3) HPEC 5010

Course Director: Amy Bond, PhD

This course has two aims. First, to introduce students to fundamental health economic topics The course is designed is accomplish these two aims using a three-pronged approached.
(1) Lectures: The first half of the course will introduce a health economic topic that will provide context for the class (2) Course readings: The second half of the course will focus on one or two class readings that both relate to the (3) Course assignments: There will be a small number of course assignments that will require students to use Stata.

Artificial Intelligence in Medicine I (3) HINF 5012

Course Director: Fei Wang, Ph.D.

Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.

Artificial Intelligence in Medicine II (3) HINF 5025

Course Director: Fei Wang, PhD
3 credits

Prerequisite: Artificial Intelligence in Medicine I
This class will teach students more advanced topics on AI in medicine. It requires students to have taken the AI in medicine I class. The contents of the class cover generalizability of AI models, computational fairness, model interpretation and explanation, privacy and security, federated learning, multi-modal learning, generative AI, causal inference, target trial emulation. The students will be asked to do a final project with teams based on the contents taught in the class, and python programming will be needed for doing the project.

Big Data in Medicine (3) HBDS 5020

Course Director: Samprit Banerjee, PhD, 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

Course Director: Xi Kathy Zhou, PhD

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

Course Director: Samprit Banerjee, PhD, 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

Course Director: Oleksandr Savenkov, PhD

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.

Clinical Informatics (3) HINF 5011

Course Directors: Sameer Malhotra, M.B.B.S., M.A. 

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.

Clinical Medicine for Informaticians (3) HINF 5024

Course Director: Mark Weiner, MD
3 credits

In addition to technical, programming and analytical skills, healthcare informaticians and data scientists need clinical domain expertise to understand and interpret real world data and analytical findings and to communicate effectively with healthcare practitioners and investigators.  This course is designed to equip informaticians with a foundational understanding of key concepts in clinical medicine, especially as they relate to the collection, application and interpretation of real world data toward clinical phenotypes and predictive analytics.   Students will learn the fundamentals of the cardiovascular, gastrointestinal, respiratory, hematological, endocrine, neurological, musculoskeletal, psychiatric, and renal systems and how diseases in these body systems are reflected in subjective and objective measures collected through patient reports, clinical observations, laboratory tests and ancillary studies.  Students will understand the clinicians approach to ordering tests to evaluate for the presence of disease.  They will also learn about the variety and classification of pharmacological therapies, the context and rationale for starting and stopping medications, and their intended and unintended effects on body systems.  Students will also learn how the physical and social environment in which patients live may impact the recognition and severity of illness, as well as  the timing, approach and outcomes of care. Students will be introduced to differentiated care in the management of different patient specialties, including pediatrics and geriatrics.

Cost-Effectiveness Analysis (3) HPEC 5005

Course Director: Ali Jalali, PhD

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 (BDS) (3) HBDS 5021

Course Director: Debra D'Angelo, MS

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.

Data Management (SQL) (3) HINF 5018

Course Directors: Yiye Zhang, Ph.D. 

Database systems are central to most organizations’ information systems strategies. At any organizational level, users can expect to have frequent contact with database systems. Therefore, skill in using such systems – understanding their capabilities and limitations, knowing how to access data directly or through technical specialists, knowing how to effectively use the information such systems can provide, and skills in designing new systems and related applications – is a distinct advantage and necessity today. The Relational Database Management System (RDBMS) is one type of database systems that are most often used in healthcare organizations and is the primary focus of this course. An overview of the non-relational database structure will also be given using Python programming language to provide a fuller picture of the current data management landscape. Further, to provide students with opportunities to apply the knowledge they learn from the lectures, various homework assignments, lab assignments, an exam, and a database implementation project will be given.

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

Course Director: Wenna Xi, PhD

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) HBDS 5014

Course Director: Samprit Banerjee, PhD, MStat 

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) HBDS 5013

Course Director: Faculty

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.

Health Behavior and Consumer Informatics (3) HINF 5017

Course Director: Faculty

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

Course Director: Mark Unruh, PhD

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

Course Director: Jyoti Pathak, PhD

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) HINF 5014

Course Director: Sam Solomon

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.

Healthcare Organization & Delivery (3) HPEC 5002

Course Director: Lisa Kern, MD, MPH

The goal of this course is to educate students about the complexity and nuances of healthcare delivery. The course will be especially useful for non-clinicians who intend to go into fields that will require a detailed understanding of healthcare. Class sessions will not summarize healthcare; rather, they will analyze healthcare – through themes such as people, time, money, communication, uncertainty, 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.

Hierarchal Modeling and Longitudinal Data Analysis (3) HBDS 5010

Course Director: Arindam RoyChoudhury, PhD

An independent biostatistician often encounters data collected on patients over a length of time, or data that are otherwise clustered. This course will give the students necessary tools to analyze such data, while building on the core biostatistics material they have learned from other courses. Specifically, the students will learn to use mixed-effect models, mixed-effect ANOVA, generalized linear mixed models (GLMM), mixed-effect Cox-regression, Bayesian hierarchical models, repeated measure and longitudinal data analysis with appropriate covariance structures.

Implementation Science and AI Ethics (3) HINF 5023

Course Director: Marianne Sharko MD, MS
3 credits

This course will provide an overview of implementation science and introduce issues surrounding ethics in the use of artificial intelligence (AI) in healthcare. It will explore the challenges in the safe and effective implementation of predictive models, large language models and generative AI in healthcare. It will identify ethical issues surrounding the use of AI in healthcare through the lens of the medical ethical principles of autonomy, beneficence, nonmaleficence and justice and will provide a framework for evaluating the ethics of AI generated tools from the perspective of multiple stakeholders, including patients, providers, health systems and payors. Students will examine predictive models created to assist in healthcare management, understand the challenges in their effective and appropriate implementation, and appreciate the potential for unintended consequences and safety risks. We will explore the need to develop clinical decision support tools that are guided by the principles of fairness, appropriateness, validity, effectiveness, and safety (FAVES). We will discuss the importance of informaticists and providers as advocates for seeking transparency in predictive algorithms, and utilizing measures of reliability, validity, and effectiveness in their outcomes. We will address the importance of advocating for equity in accessibility and the need to address bias in the development of AI-generated clinical decision tools.

We will introduce implementation science, frameworks and theories, including Diffusion of Innovation, RE-AIM and PRISM. We will include projects to provide practical experience in the process of implementation that will highlight research methods, measures, and potential barriers and facilitators.
We will invite experts in the field to provide guest lectures and to lead student workshops within their areas of expertise.
The learning style is mostly student-driven, using “flipped classroom”, participatory exercises, teamwork, workshops and presentations. After the midterm, students will work to define a project, analyze related literature, and give a presentation in the final week.

Incentives in the US Healthcare System (3) HPEC 5007

Course Director: Yuhua Bao, PhD

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

Course Director: Angelica Meinhofer, PhD

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

Course Director: Arindam RoyChoudhury, PhD

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

Course Director: Marianne Sharko, MD, MS

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

Course Director: Jiani Yu, PhD

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 healthcare 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 US Healthcare Policy and Delivery (3) HPEC 5001

Course Director: Arian Jung, PhD

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.

Master's Project I and Professional Development (2) HCPR 9010

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 (3) HCPR 9020

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

Course Director: Faculty

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 asCou well as faculty and fellow classmates.

Modern Methods for Causal Inference (3) HBDS 5017

Course Director: Ivan Diaz, PhD

The goal of this course is to introduce a core set of modern statistical concepts and techniques to the students, and to demonstrate how to use them to answer complex research questions in healthcare. The students will acquire knowledge on causal inference methods using machine learning, including directed acyclic graphs, non-parametric structural equation models, inverse probability weighting, g-computation, survival analysis, marginal structural models, longitudinal data, mediation analyses, effect modification, and precision medicine. This course will use the free software R to perform all statistical analysis.

Natural Language Processing (3) HINF 5016

Course Director: Yifan Peng, PhD

This course introduces students to the field of natural language processing (NLP), applied to the health domain. NLP focuses on text data, which lacks the structure of conventional tabular data. In the health domain text is abundant in electronic health records, the medical literature and on the Web. Important applications of NLP include information extraction (pulling facts out of text) and information retrieval (searching through a collection of texts). The course presents methods for working with text: identifying the elements (words and symbols), recognizing sentence boundaries, parsing syntactic structures, assigning meaning, and establishing the structure of the discourse as a whole. The students build skills with these methods through laboratory work.

Pharmaceutical Statistics (3) HBDS 5019

Course Director: Faculty

Pharmaceutical studies use many statistical methods that are not routinely taught as part of conventional biostatistics courses. In this course, the students will learn the statistical methods specifically used in pharmaceutical studies.

The course is divided into three modules.
(1) “Statistical Aspects of Phase I Clinical Trial” will include 3+3 Design, accelerated titration; up and down designs; continual reassessment method (CRM), Modified CRM, TITE CRM, Bayesian Logistic Regression Model (BLRM), escalation with overdose control (EWOC), toxicity probability interval (TPI) and modified TPI (mTPI).
(2) “Statistical Aspects of Phase II Clinical Trial” will include design and analyses for One stage and Simon’s Two Stage Designs, Multi-arm Phase II design.
(3) “Statistical Aspects of Phase III Clinical Trial” will include randomization, design and analysis for parallel, crossover, factorial, seamless Phase II/III, Adaptive and SMART designs.

Qualitative Methods for Health Research (3) HPEC 5012

Course Director: Czarina Navos Behrends, Ph.D., MPH

This course provides an introduction to qualitative theory and methods in health research. Topics will include qualitative research theory, development of qualitative research proposals, interview approaches, qualitative analysis, mixed methods, and theoretical frameworks. The aim of this course is to develop introductory, basic skills for conducting a qualitative research study from beginning to end by providing a combination of education on qualitative theory and providing opportunities to apply that education to a semester long project that mimics a qualitative health research study. This course will use a combination of didactic lectures, discussion, and small group work.

Research Methods in Health Informatics (3) HINF 5004

Course Director: Yunyu Xiao, PhD

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

Course Director: Zhengming Chen, PhD, MPH, MS

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

Course Director: Linda Gerber, PhD

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 Designs and Comparative Effectiveness (3) HPEC 5006

Course Directors: Alvin Mushlin, MD, ScM

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

Course Director: Sze Yan Liu, PhD, MPH

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.