HDS 5130 - Healthcare Organization, Management, and Policy

3 Credits

The course is designed to give students frameworks, analytic tools, informational resources, and specialized expertise in health administration and health policy. This background will prepare students for professional work in the health sector in medical and health settings, as researchers, managers or program developers, or as professionals responsible for analysis, evaluation, or advocacy. The course emphasizes knowledge of the organization and financing of health care, politics, the influence of Medicare and Medicaid policies, and the implications of health policy for diverse populations. The course will particularly focus on the implications of the recently enacted health reform – the Patient Protection and Affordable Care Act (ACA) of 2010. Offered in spring.

HDS 5210 - Programming for Health Data Scientists

3 Credits

Students will be introduced to concepts in computer programming using the Python programming language. Students will learn to conceptualize steps required to perform a task, manipulate files, create loops, and functions. By the end of this course, students will have a basic understanding of computer programming, a working knowledge of the Python programming language, and they will be able to share their scripts to collaborate with other team members.

Attributes: MPH-Biostatistics, Grad Pol Sci Skills, Social Work PhD Specilization

HDS 5230 - High Performance Computing

3 Credits

This course is needed to give Health Data Science students the skills they will need to work with big healthcare data and modern high-performance computing environments during their careers.

Prerequisite(s): HDS 5310; HDS 5210

Attributes: Social Work PhD Specilization

HDS 5310 - Analytics and Statistical Programming

3 Credits (Repeatable for credit)

This course will serve as the foundation for all subsequent coursework. Students will learn statistical concepts of probability theory, sampling theory, null hypothesis significance testing, and Bayesian estimation. They will develop expertise in the R statistical programming language and Markdown syntax, and learn to collaborate with one another using the git and github version-tracking/sharing tools. By the end of this course, students will have a basic knowledge of statistical concepts, be able to execute analyses in R, share work with collaborators, and document their results.

Attributes: Bioinformatics & Comp Bio Elec, MPH-Epidemiology, MPH-Health Management & Policy, Social Work PhD Specilization

HDS 5320 - Inferential Modeling

3 Credits

Students will learn to conceptualize research questions as statistical models, and parameterize those models from real-world data. The course will start by introducing the linear model, then expand into generalized linear models, nonlinear models, mixed and multilevel models, and Cox survival models. Students will have a working knowledge of how to use statistical models to gain an understanding of the influence of individual predictor variables on health outcomes.

Prerequisite(s): HDS 5310

HDS 5330 - Predictive Modeling and Machine Learning

3 Credits

In contrast to the statistical modeling course which focuses on understanding the influence of variables on outcomes, this course will focus on predicting individual health outcomes using modern automated model development algorithms. By the end of this course, students will be able to create predictive analytics using popular machine learning packages in R and Python.

Attributes: Bioinformatics & Comp Bio Elec, MPH-Epidemiology, MPH-Biostatistics, Social Work PhD Specilization

HDS 5930 - Special Topics

3 Credits (Repeatable for credit)

HDS 5960 - Capstone Experience

3 Credits

This course is designed to offer data science students an opportunity to practice their skills in an industry setting, to learn the roles that various members of a data science team play in an organization, and to begin building a network of professional contacts and references.

Prerequisite(s): ORES 5300; HDS 5210; HDS 5310

Restrictions:

Enrollment limited to students in the MS Health Data Science program.

HDS 5980 - Graduate Independent Study in Health Data Science

1 or 3 Credits (Repeatable for credit)

ORES 2310 - Introduction to Clinical Medicine

3 Credits

This course addresses the fundamentals of diagnosis and treatment related to the practice of medicine for leading diseases. Students will be introduced to the basic science concepts of medicine, including anatomy, physiology, microbiology, and genetics in the context of evidence-based screening and treatment guidelines used by medical subspecialties. Class sessions, taught by medical school faculty, employ a mix of lecture, discussion, hands-on demonstrations, and care simulation. Student assignments include analysis of diagnostic criteria and treatment options available to clinicians and development of patient-directed communications about medication use.

ORES 2320 - Interprofessional Health Outcomes Research

2 Credits

Offered by the Department of Health and Clinical Outcomes Research (HCOR) within the School of Medicine, this course will provide students the skills vital to developing a measurable research question, investigating the current literature, and incorporating a study design that best answers their research questions. Furthermore, this course will encourage students to look at healthcare research from the scope of social determinants and socio-cultural contexts to better understand health disparities and inequities.

Attributes: UUC:Dignity, Ethics & Just Soc, UUC:Social & Behavioral Sci

ORES 5010 - Introduction to Biostatistics for Health Outcomes

3 Credits

This course is designed to introduce basic principles of descriptive and inferential statistics. The course will cover fundamental concepts and techniques of descriptive and inferential statistics with application to health outcomes research. This course contributes to the First Dimension by preparing students for advanced study in areas related to Outcomes Research and contributes to the Second Dimension by teaching students tools and methods of research.

Attributes: Health & Rehab Sci Research

ORES 5100 - Research Methods in Health & Medicine

3 Credits

This online course is designed to provide an introduction to the techniques, methods, and tools used for research in the health sciences. Students will obtain an understanding of the research process and scientific method, specific study designs, methods for data collection and analysis. This is a very applied and hands-on course and is focused entirely on the unique aspects of research in the health sciences. This course will utilize Blackboard for all lectures, online discussions, assignment submission, and examinations.

Attributes: Aviation Elective (Graduate), Aviation Research (Graduate), Health & Rehab Sci Research

ORES 5160 - Data Management

3 Credits

This course will cover the basic skills necessary for maintaining databases as well as ensuring data quality and manipulating data. The course will also introduce an experiential component in data base design and management. The course is designed for health outcomes research masters students and doctoral level students in public health. This course contributes to the First Dimension by preparing students for advanced study in areas related to Outcomes Research and contributes to the Second Dimension by teaching students tools and methods of research.

Attributes: MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, Social Work PhD Specilization

ORES 5210 - Foundations of Medical Diagnosis and Treatment

3 Credits

Taught by medical school faculty, this course in an introduction to clinical medicine for graduate students. Basic science concepts include anatomy, physiology, microbiology/hematology, infectious diseases, genetics, immunology, endocrinology and metabolic pathways. Primary organ systems and their associated diseases will also be covered, with special emphasis on their diagnosis and treatment.

ORES 5260 - Pharmacoepidemiology

3 Credits

This course is an introduction to pharmacoepidemiology - the use and effects of drugs in human populations. It provides an overview of the principles of pharmacoepidemiology, sources of pharmacoepidemiology data, and special issues in pharmacoepidemiology methodology. It reviews commonly used study designs, special topics and advanced methodologies for pharmacoepidemiologic studies.

Attributes: MPH-Maternal & Child Health

ORES 5300 - Foundations of Outcomes Research I

3 Credits

This course will assist students in understanding outcomes research and provide a background in the basic tools used in outcomes studies. The course will enable students to 1) conceptually define the meaning and purpose of outcomes research, 2) understand the role of epidemiology, biostatistics, health economics, and database and information technology in conducting outcomes research, 3) evaluate the usefulness and utility of outcomes measures, 4) recognize the different types of measures used in outcomes research, including clinical, health status, quality-of-life, health care utilization, and patient satisfaction, 5) obtain a basic appreciation of statistical analyses appropriate for outcomes research, and 6) interpret the results of health outcomes research.

Attributes: Health & Rehab Sci Research, Social Work PhD Specilization

ORES 5320 - Scientific Writing and Communication

3 Credits

The purpose of this course is to take students step-by-step through the process of writing a journal article appropriate for publication in a scientific journal. We will focus on each section of the article for several weeks as students complete assignments related to successfully writing the section and receive feedback on weekly assignments. The last part of the course will focus on taking the research findings presented in the journal article and preparing a poster that could be presented at a research conference. Overall, students will improve their ability to communicate complex research findings in writing to their peers via publication in the peer-reviewed literature and to the broader scientific community through presentation of a poster.

Attributes: MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Biostatistics

ORES 5400 - Pharmacoeconomics

3 Credits

Pharmacoeconomics is one of the cornerstones of Health Outcomes Research. This course is designed to teach clinicians and new researchers how to incorporate pharmacoeconomics into study design and data analysis. Participants will learn how to collect and calculate the costs of different alternatives, determine the economic impact of clinical outcomes, and how to identify, track and assign costs to different types of health care resources used. This is a required course for the MS in Outcomes Research and Evaluation Sciences but may also be of interest to students in Public Health and Health Administration. This course contributes to the First Dimension by providing students with advanced skills in highly valued research area and contributes to the Second Dimension by developing students’ ability to effectively communication complex information.

ORES 5410 - Evaluation Sciences

3 Credits

This course will examine methods for evaluation of health programs in both organizational and community contexts. Topics include formative research, process evaluation, impact assessment, cost analysis, monitoring outcomes, and evaluation implementation. Strengths and weaknesses of evaluation designs will be discussed. This is a required course for the MS in Outcomes Research and Evaluation Sciences Program but may also be of interest to students in Public Health, Health Administration, and Allied Health. This course contributes to the First Dimension by providing students with advanced skills in the evaluation sciences and contributes to the Second Dimension by developing students’ ability to effectively communicate complex statistical information.

ORES 5430 - Health Outcomes Measurement

3 Credits

This course is designed to introduce students to the principles of health outcomes measurement. Specifically, students will be introduced to the most common measures seen in health outcomes and health services research as well as measure development and assessment of psychometric properties. Topics will include generic vs. disease specific measures, instrument design, scaling, reliability and validity, addressing measurement error, Classical Test Theory, and Item Response Theory. This course contributes to the First Dimension by providing students with advanced skills in a highly valued research area and contributes to the Second Dimension by developing students' ability to effectively communicate complex statistical information.

Attributes: Health & Rehab Sci Research, MPH-Behavior Sci & Health Equi, MPH-Epidemiology, MPH-Global Health, MPH-Health Management & Policy, MPH-Maternal & Child Health, MPH-Biostatistics, Social Work PhD Specilization

ORES 5440 - Comparative Effectiveness Research

3 Credits

This course is designed to introduce students to the principles of comparative effectiveness research. Specifically, students will be introduced to the concept of comparative effectiveness research, common research methods and statistical analyses, and translation and dissemination. This course contributes to the First Dimension by providing students with advanced skills in a highly valued research area and contributes to the Second Dimension by developing students' ability to effectively communicate complex statistical information.

ORES 5550 - SAS Programming I

1 Credit

In the era of big data and outcomes research, skilled scientists can organize, manipulate, and analyze using many different tools. Programming in SAS is an essential skill. This course introduces the SAS environment and programming language. Students will learn data management, descriptive analysis, and statistical inference testing using a hands-on approach. By the end of the course, students will be able to import, organize, and analyze data as well as interpret the results.

Prerequisite(s): (ORES 5010, BST 5000, or BST 5020)

ORES 5560 - R Programming

3 Credits

This course will teach students how to use the R statistical programming language to perform data analytics. We will start with an overview of the environment and an introduction to how data is represented in R (vectors and dataframes). We will then quickly move into actual data analysis. We will cover importing data from different source files. We will then go over typical initial data management tasks and exploratory data analysis, including both numerical and visual approaches. We will cover more complex operations on dataframes, including aggregation by clusters and various data merges. In the second half of the course, we will cover the implementation of various statistical techniques in R, including group comparisons (typical scientific table one), linear regression, and the creation of reproducible reports in R. (Offered in summer.)

ORES 5970 - Research Topics in Outcomes Research

0-3 Credits (Repeatable for credit)

ORES 5980 - Graduate Independent Study in Outcomes Research

1-3 Credits (Repeatable up to 6 credits)

ORES 6930 - Special Topics

3 Credits (Repeatable for credit)

ORES 6950 - Special Study for Exams

0 Credits (Repeatable for credit)

This Special Study for Exams course indicates that a student will be taking the exams the semester they are registered for.

ORES 6970 - Advanced Research Topics in Outcomes Research

1-3 Credits

ORES 6980 - Graduate Independent Study in Outcomes Research

0-3 Credits

ORES 6990 - Dissertation Research

0-6 Credits (Repeatable for credit)