Curriculum

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: 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.

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.

This course is designed to introduce students to the fundamentals of health services research, which evaluates 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 master’s research projects.

This is the capstone course of all masters-level graduate education programs. Its two aims are to: (1) help students to discover and develop effective ways of managing and working together with all the stakeholders within the healthcare field, and (2) accelerate a student's development of 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 duration of the MS program: 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.

The US healthcare system is in the midst of changes catalyzed in part by the continued effects of the Affordable Care Act, the 2008 recession, and the COVID pandemic. 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.

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.

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.

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.

Prerequisites: Biostatistics I or Introduction to Biostatistics 

With an emphasis on empirical applications, this course equips students to empirically analyze non-experimental data at levels often required in professional environments. Applied Econometrics for Health Policy is designed with two 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.

This is the 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.

Prerequisites: Biostatistics I or Introduction to Biostatistics

The cost effectiveness analysis course starts with 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 course then moves onto 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.

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. 

This course provides students with an introduction to health economic modeling and statistical methods central to applied pharmacoeconomics. Through lectures and applied exercises, students will develop a working knowledge of how to select, justify, and apply appropriate statistical distributions to represent a range of health outcomes and cost measures in pharmacoeconomic evaluations of medical products and healthcare programs. Students will also learn how to identify and synthesize multiple sources of evidence in developing computational models for applied pharmacoeconomic analyses. Through hands-on exercises in Stata and Microsoft Excel, students will gain practical experience applying parametric, non-parametric, and simulation-based methods using common healthcare data sources.

During the first half of the course, which consists of lectures, readings, and assignments using Stata, we will introduce a health economic topic that will provide context for the class. The second half of the course will focus on one or two class readings that both relate to the assignments.

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.

This course will cover the conceptual underpinnings, the policy context, and the methods for comparative effectiveness research (CER), with an emphasis on 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 other experts,  as well as student discussions and presentations.

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. A recurring theme will be 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 interests of participants to the extent possible.

This course offers a comprehensive introduction to applied machine learning models in the context of health policy and health economics. Students will learn how to develop, evaluate, and deploy machine learning models to study important health policy and economic topics across different care settings, such as predicting mortality to support hospice decision making, risk adjustment for insurance plans, and machine learning boosted causal inference methods for health policy evaluations. Throughout the course, we will focus on understanding the potential disparities associated with machine learning-based decision making in health policy and health economics.