Master of Science in Population Health Sciences

This course covers empirical identification strategies for using non-experimental data to conduct causal analysis. Students will become familiar with common methodological problems that prevent causal interpretation, and strategies to address it. Students will learn how to and when to implement commonly used econometrics tools such as differences-in-differences, instrumental variables, and regression discontinuity designs. Students will replicate past studies in healthcare that have employed these models and will become familiar with data graphing techniques often used to argue for causal interpretation. Additionally, students will produce three short papers using each of the three strategies. Students are strongly encouraged to connect these brief reports with their thesis.

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 systematic reviews, observational studies, risk adjustment, propensity score matching, instrumental variables, and the use of clinical registries and insurance claims data. Students will learn why comparative research has come to prominence, what makes good comparative effectiveness research, the main methods used and the advantages and disadvantages of each. The course will not be a statistics or how-to course. Sessions will consist of both lectures from the instructors and experts on selected topics, as well as student presentations.

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

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 trains students to identify data sources for policy evaluations and to answer research questions. Through hands-on work, students will develop the skills to manage and analyze datasets. This includes formatting files for analyses and assessing the validity and consistency of data sources. In addition to regular data assignments, students will be required to complete individual projects. 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.

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.

The role of health information technology in improving healthcare quality, safety, and medical decisions, as well as the potential for unintended consequences.

Addresses technological and policy issues required to exchange clinical data across settings and maintain privacy and security within and across health care organizations.

An introduction to informational, technological, policy, social, and organizational concerns in healthcare informatics.

Provides an overview of health and healthcare delivery. The course will familiarize students with systems of health and healthcare at a "macro" scale through a variety of readings and in-class discussions. Topics include healthcare delivery models, structure, organizations, policy, finance and workforce. In addition, students will be exposed to the processes of healthcare at a "micro" scale, through field experiences in a variety of healthcare settings such as inpatient wards, emergency department, ancillary departments and outpatient clinics. This course provides an introduction to the fundamental concepts in organizational structure and management and an overview of the different health and public health organizations that compose the U.S. health system. In addition, through field experiences students will be exposed to the processes of health care. Throughout the semester, students will observe, record, and interpret the daily practice of health care in such settings as: inpatient wards, emergency departments, ancillary departments, and outpatient clinics.

Economic incentives embedded in the healthcare 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, payers, insurers, and health care providers and implications for 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 and to contain cost growth.

An introduction to the fundamentals of biostatistics with primary emphasis on understanding of statistical concepts behind data analytic principles. This course will also teach R, a freely available software, 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.

This course is designed to introduce students to the fundamentals of health services research. Health services research is the discipline that measures the effectiveness of interventions designed to improve healthcare. These interventions can include changes to the organization, delivery and financing of health care, including the use of computers in 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, choosing a study design, 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.

The U.S. 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.

Participatory Medicine is a model of cooperative health care that seeks to achieve active involvement by patients, professionals, caregivers, and others across the continuum of care on all issues related to an individual's health. The availability of social media, smartphones, self-monitoring devices and direct-to-consumer e-services far outstrips evidence about the efficiency, effectiveness and efficacy of using them for health improvement. The aim of this module is to examine how health informatics research is contributing to generate richer and more robust evidence about healthcare aims, health data processes, and health outcomes associated with Participatory Health Technologies.