Saint Louis University's Center for Health Outcomes Research offers undergraduate and graduate courses in health data sciences.
Undergraduate-level courses in health outcomes research
ORES 2300: Survey of Epidemiology of Health Services Research
This course is open to all undergraduates and is a required course for students in the bachelor of science program in health management offered by the School of Public Health. It introduces methods and interpretations of measures of frequency, association, error, bias and public health impact. Epidemiological methods are presented within the context of assessing cost, quality, and access of the health care system. Employing mix of lecture, discussion, and computer-based laboratory assignments, students will explore the relationships between policy, medical care practices, and scientific understanding via epidemiology.
ORES 2310: Introduction to Clinical Medicine
This course addresses the fundamentals of diagnosis and treatment related to leading diseases. Students will be introduced to 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 faculty from the School of Medicine, 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 treatment.
ORES 2320: Interprofessional Health Outcomes Research
This course is open to all undergraduates in a health-related major and is a required course for students completing the minor in interprofessional practice. It introduces the skills for effective and efficient searching for evidence-based health care focusing on outcomes of collaborative practice for improving health status. Students will identify outcome variables to be measured and methods used in conducting outcomes research. Students will learn how to search and critically evaluate the literature and develop a plan for evaluating an interprofessional collaboration on health outcomes.
Graduate-level courses in health outcomes research
ORES 5010: Introduction to Biostatistics for Health Outcomes Research
This course introduces the basic principles and methods of biostatistics, providing a sound methodological foundation for health outcomes research. The purpose of the course is to teach fundamental concepts and techniques of descriptive and inferential statistics with applications in health care, medicine, public health, epidemiology, and health outcomes research. Basic statistics, including probability, descriptive statistics, inference tests for means and proportions, and regression methods are presented.
ORES 5100: Research Methods in Health and Medicine
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, and methods for data collection and analysis.
ORES 5120: Practical Applications of Statistical Methods
This course aims to advance the student's skills in study design, data analysis, scientific writing, and presentation/communication. This will be realized by through a series of one-hour technical skill workshops, two-hour peer review sessions and series of consultation appointments. All activities are organized around the student's selected research project. The workshops will include a take-home assignment to recap key teaching points and assess skill competency. Students will engage in peer review and critique as part of this course. Each student will be paired with a statistics consultant to support analytic method selection, design a data management plan, and verify calculations. They will have access to this consultant for one additional semester after the end of this course. This course will be graded as pass/fail with students earning a pass if they attend all workshops and peer review sessions, attend meetings with their consultant as needed, and show progress on the steps to completing their thesis.
ORES 5150: Multivariate Analysis for Health Outcomes Research
The purpose of this course is to introduce the basic principles and methods of multivariate statistics, providing students with a toolbox of statistical methods and the knowledge of when to apply the methods. This course covers advanced concepts and techniques of descriptive and inferential statistics with applications in the medical and public health fields. Multivariate methods including multiple linear regression, logistic regression, MANOVA, survival analysis, and principal components analysis are presented.
ORES 5160: Data Management
This course is an introduction to the design, maintenance and management of data involving human or animal subjects for research and analytic purposes. The course topics will cover types, sources and formats of research data and current health coding systems, working with multiple types of data files, data transfers and basic data management, and summarization and programming techniques using SPSS and SAS statistical software. The objective of this course is to help students understand, design and utilize health -related databases for health outcomes research and analysis purposes. Students completing this course will have the opportunity to apply hands-on database management skills to design, enter, manipulate and summarize health information.
ORES 5210: Foundations of Medical Diagnosis and Treatment
This course explores diagnosis and treatment of common diseases through evidence-based guidelines and algorithms. Organized around ten medical specialties of collaborating School of Medicine faculty, clinical units cover the tools and decision-making processes used in today's practice of medicine. The learning experience incorporates didactic lectures, readings, assignments, quizzes and examinations. Students will learn to analyze clinical decision problems, research emerging technologies and describe complex medical care issues to patients.
ORES 5260: Pharmacoepidemiology
This course is an introduction to pharmacoepidemiology, which is the study of the use of and the effects of drugs in large numbers of people. The course will provide an overview of the principles of pharmacoepidemiology, sources of pharmacoepidemiology data, and special issues in pharmacoepidemiology methodology. It will review commonly used study designs, special topics, and advanced methodologies used in pharmacoepidemiology studies.
ORES 5300: Foundations of Outcome Research I
This course will assist students in understanding outcomes research and provide a background in the basic tools used in outcomes studies.
ORES 5310: Foundations of Outcome Research II
This course introduces more methodologically complex principles and methods of health outcomes research, building on the skills acquired in Foundations of Outcomes Research I. The course examines defining health outcomes, purposes and methods of risk adjustment, and assessment of quality and cost of care.
ORES 5400: Pharmacoeconomics
Pharmacoeconomics involves the assessment of the costs and benefits associated with pharmaceutical interventions. The purpose of this course is to introduce the student to the concepts associated with pharmacoeconomic analyses. The goal of the course is to allow the student to appropriately interpret the merits of pharmacoeconomic literature to allow for informed decision making.
ORES 5410: Evaluation Sciences
This course deals with the application of research methods to judge the success of health programs. The course focus is public health programs and health services, although the concepts and methods are equally relevant to other sectors. Lectures and discussions concerning problems and techniques are combined with field experiences in health services delivery or health programs.
ORES 5420: Clinical Trials and Analysis
This course is designed to provide students with an understanding of the main concepts and issues in clinical trial design and interpretation. The course will concentrate on the design, conduct, analysis, interpretation, and dissemination of results in clinical trials research. Topics include bias control, random error control, randomization, blocking, masking, precision of estimation, power, sample size, accrual dynamics, types of trial designs, analysis of trial results and federal regulations. The overarching goal of the course is to familiarize students with the clinical trials process.
ORES 5430: Health Outcomes Measurement
This course provides students with an understanding of the principles of instrumentation and measurement of health outcomes. The course concentrates on techniques and instruments most commonly utilized in outcomes research including measuring health status, quality of life, patient satisfaction, function and disability, and compliance and adherence. Methods of assessing reliability and validity of measures are emphasized.
ORES 5440: Comparative Effectiveness Research
This course will cover the fundamental concepts of Comparative Effectiveness Research-research evaluating the benefits and harms of alternative treatment methodologies. Content includes the historical context of CER and its research priorities, methodologies specific to CER and patient-centered outcomes research. Students will have the opportunity to evaluate existing CER research and to propose a CER project in their own area of interest.
ORES 5560: R Programming
This course will introduce students to the R statistical programming language, as well as some of the added features of the R Studio integrated development environment for R. Students will learn the basics of R programming including operators, assignment, object classes, vectors, matrices, data frames, and lists. The will also learn to import and clean tabular data, transform variables, run common statistical procedures, and create figures and tables. Finally, students will learn R Studio's R Markdown syntax for generating notebooks and reports.
Advanced R Programming
This course assumes students are familiar with basic R syntax, data structures, and common procedures. Here students will learn more about writing functions, using loops to control the flow of a script, organizing work into and R Studio Project, and writing packages. Student will gain familiarity with S3 and S4 class systems. They will create interactive figures using ggplot2 and shiny. Additional specialized topics (importing GIS shape files, for example) as requested by students could be covered if time allows.
ORES 5550: SAS Programming I
This course will introduce one to the SAS environment (version 9.4) and basic SAS programming language. Students will learn the basics for 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 data into SAS, organize it, analyze it, and interpret the results.
ORES 5900: Capstone
This course is designed to allow students to integrate the knowledge and skills developed over the course of the M.S. in Health Outcomes Research and Evaluation Sciences program. Students will design and complete an outcomes study or program evaluation over the course of the semester culminating in a formal presentation of the study and results. The overarching goal is to incorporate and utilize research skills in a real-world setting.
Graduate-level courses in health data science
HDS 5310: Analytics and Statistical Programming
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.
HDS 5320: Inferential Modeling of Health Outcomes
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.
HDS 5330: Predictive Modeling and Machine Learning
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.
HDS 5210: Programming for Data Scientists
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.
High Performance Computing in the Health Care Industry
Modern EHR and claims databases can be enormous. A simple query may take weeks to run on a standard computer, if it can even be run at all. In this class, students will learn to overcome the challenges of data storage, memory, and processing limitations to facilitate query and analysis of large EHR and medical claims databases using modern Big Data tools such as Hadoop and MapReduce.
HDS 5130: Health Care Organization, Management and Policy
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.
Nothing is more important than experience. Health Data Science students will be placed with industry partners to assist with real data management and analysis in a real-world setting. At the end of the practicum, students will have made industry contacts and gained real experience to help kick-start their careers.