Twin Cities campus
 
Twin Cities Campus

Health Services Research, Policy, and Administration M.S.

School of Public Health - Adm
School of Public Health
Link to a list of faculty for this program.
Contact Information
School of Public Health, MMC 819, A395 Mayo Memorial Building, 420 Delaware Street, Minneapolis, MN 55455 (612-626-3500 OR 1-800-774-8636)
  • Program Type: Master's
  • Requirements for this program are current for Fall 2022
  • Length of program in credits: 34
  • This program requires summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The Health Services Research, Policy & Administration (HSRPA) MS program includes a robust, multidisciplinary core curriculum that provides the foundation for conducting health services research and health analytics. Students work closely with their advisor on an area of specialization to develop a program tailored to their interests and professional needs. The School of Public Health is accredited by the Council on Education for Public Health (CEPH).
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
Other requirements to be completed before admission:
Good math skills are essential. Previous coursework in algebra, statistics, or other quantitative coursework is recommended.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 100
    • Paper Based - Total Score: 600
  • IELTS
    • Total Score: 7.0
  • MELAB
    • Final score: 80
The preferred English language test is Test of English as Foreign Language.
Key to test abbreviations (TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan B: Plan B requires 34 major credits and 0 credits outside the major. The final exam is oral. A capstone project is required.
Capstone Project:The Plan B project comprises either an industry-specific project involving student collaboration with a local organization, or independent research conducted on a relevant topic of interest. The project is selected in consultation with and guided by the advisor.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
Required Coursework (21-22 credits)
In consultation with the advisor, select at least 21 credits from the following list. Students must receive a B- or better for PubH 6450 and 6451. The majority of courses must be taken A-F.
PUBH 6250 - Foundations of Public Health (2.0 cr)
PUBH 6450 - Biostatistics I (4.0 cr)
PUBH 6451 - Biostatistics II (4.0 cr)
PUBH 6724 - The Health Care System and Public Health (3.0 cr)
PUBH 6742 - Ethics in Public Health: Research and Policy (1.0 cr)
PUBH 6806 - Principles of Public Health Research (2.0 cr)
Plan B Project (2 credits)
Take 2 Plan B Project credits in consultation with the advisor.
PUBH 7894 - MS in Health Services Research, Policy, and Administration Plan B Project (1.0-5.0 cr)
Using and Managing Data (4-5 credits)
Take 2 or more course(s) from the following:
· PUBH 6748 - Analyzing Administrative Data for Healthcare Operations and Research (2.0 cr)
· PUBH 6813 - Managing Electronic Health Information (2.0 cr)
· PUBH 6845 - Using Demographic Data for Policy Analysis (3.0 cr)
Electives (12-13 credits)
Select remaining coursework from the recommended electives, or courses from any specialization, in consultation with the advisor to meet the 34-credit minimum.
Programming and Analytic Methods (6 credits)
CSCI 5511 - Artificial Intelligence I (3.0 cr)
CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
HINF 5502 - Python Programming Essentials for the Health Sciences (1.0 cr)
HINF 5531 - Health Data Analytics and Data Science (3.0 cr)
MILI 6963 - Healthcare Analytics (2.0 cr)
PA 5929 - Data Visualization: Telling Stories with Numbers (2.0 cr)
PUBH 6107 - Excel Skills for Data Management in Public Health Settings (1.0 cr)
PUBH 6325 - Data Processing with PC-SAS (1.0 cr)
PUBH 6420 - Introduction to SAS Programming (1.0 cr)
PUBH 6717 - Decision Analysis for Health Care (2.0 cr)
PUBH 6739 - Data Dashboards and Visualization with Tableau (1.0 cr)
PUBH 6819 - Qualitative Research Theory and Methods for Health and Health Services Research (2.0 cr)
PUBH 7264 - Data Visualization in R (1.0 cr)
PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
Relevant public health/health services courses (6-7 credits)
Relevant Summer Public Health Institute courses, which vary from year to year, may also be included per advisor approval. Students may take PUBH 6320 or 6341 but not both.
PUBH 6735 - Principles of Health Policy (3.0 cr)
PUBH 6803 - Conducting a Systematic Literature Review (3.0 cr)
PUBH 6810 - Survey Research Methods (3.0 cr)
PUBH 6832 - Economics of the Health Care System (3.0 cr)
PUBH 6855 - Medical Sociology (3.0 cr)
PUBH 6862 - Cost-Effectiveness Analysis in Health Care (3.0 cr)
PUBH 6864 - Conducting Health Outcomes Research (3.0 cr)
PUBH 8804 - Advanced Quantitative Methods Seminar (3.0 cr)
PUBH 8814 - Mixed Methods: Quantitative and Qualitative Strategies in Research (2.0 cr)
PUBH 8816 - Implementation Science (2.0 cr)
Epidemiology
PUBH 6320 - Fundamentals of Epidemiology (3.0 cr)
or PUBH 6341 - Epidemiologic Methods I (3.0 cr)
Joint- or Dual-degree Coursework:
JD/MS-HSRP&A Students may take a total of 8 credits in common among the academic programs. Students may take a total of 8 credits in common among the academic programs.
 
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PUBH 6250 - Foundations of Public Health
Credits: 2.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
In this course we will examine values, contexts, principles, and frameworks of public health. We will provide an introduction to public health, consider the history of public health, social/political determinants, impact of health disparities on race, class and gender, moral and legal foundations, public health structures, historical trauma and cultural competence, health and human rights, advocacy and health equity, communication and financing, and the future of public health in the 21st century. Grounded in theory and concepts, we will incorporate core competencies and skills for public health professionals and will focus on developing problem solving and decision-making skills through critical analysis, reflection, case studies, readings, and paper assignments.
PUBH 6450 - Biostatistics I
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
This course will cover the fundamental concepts of exploratory data analysis and statistical inference for univariate and bivariate data, including: ? study design and sampling methods, ? descriptive and graphical summaries, ? random variables and their distributions, ? interval estimation, ? hypothesis testing, ? relevant nonparametric methods, ? simple regression/correlation, and ? introduction to multiple regression. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [College-level algebra, health sciences grad student] or instr consent
PUBH 6451 - Biostatistics II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
This course will cover more advanced aspects of statistical analysis methods with a focus on statistical modeling, including: ? two-way ANOVA, ? multiple linear regression, ? logistic regression, ? Poisson regression, ? log binomial and ordinal regression, ? survival analysis methods, including Kaplan-Meier analysis and proportional hazards (Cox) regression, ? power and sample size, and ? survey sampling and analysis. There will be a focus on analyzing data using statistical programming software and on communicating the results in short reports. Health science examples from the research literature will be used throughout the course. prereq: [PubH 6450 with grade of at least B, health sciences grad student] or instr consent
PUBH 6724 - The Health Care System and Public Health
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Overview of health care delivery, finance systems within public health context. Components of health care system: financing, role of employers/public programs, health care delivery system, managed care. Collaborative interventions between managed care, public health. prereq: Public health or grad student or instr consent
PUBH 6742 - Ethics in Public Health: Research and Policy
Credits: 1.0 [max 1.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Introduction to ethical issues in public health research/policy. Ethical analysis. Recognizing/analyzing moral issues.
PUBH 6806 - Principles of Public Health Research
Credits: 2.0 [max 2.0]
Typically offered: Every Fall & Spring
Evaluation of public health research literature and planning for independent research projects. Formulation of research question, research design, sampling techniques, use of research concepts, and data analysis. Data collection techniques, including questionnaires, interviews, and data analysis. prereq: Pub hlth or grad or professional school student or instr consent
PUBH 7894 - MS in Health Services Research, Policy, and Administration Plan B Project
Credits: 1.0 -5.0 [max 10.0]
Grading Basis: S-N only
Typically offered: Every Spring
Plan B project. prereq: [Health Services Research, Policy/Administration] MS student
PUBH 6748 - Analyzing Administrative Data for Healthcare Operations and Research
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This is an introductory course designed to teach students how to effectively analyze administrative data (e.g., billing or claims data, clinical registries, enrollment records) for use in both healthcare operations and research. During the course, students will learn about accessing common administrative data sources; the structure of administrative data; coding and billing systems; best practices for developing and implementing analytic plans; creating cohorts; strategies for risk assessment; developing measures; approaches for missing data; approaches for linking across data sources; reporting considerations; quality control; and strategies for effective data presentation. The course will provide practical, hands-on training for individuals to effectively analyze and report healthcare outcomes for operations and research using administrative data. COURSE PREREQUISITES Some basic programming (e.g., SAS, Stata, R) and statistics knowledge preferred. Please feel free to email the instructors to discuss options for gaining this background. Students from all academic programs welcome.
PUBH 6813 - Managing Electronic Health Information
Credits: 2.0 [max 2.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Managing health information is a central function of health care organizations. Information is used for managing population health, profiling providers, and measuring quality. This course describes relational data theory, normalization, and Structured Query Language (SQL) will be used to create and query databases. Students will be introduced to the basic programming skills necessary to manage data in research projects. Programming aspects of the course will use SQL procedure in the SAS language. prereq: Admission to a University of Minnesota Masters program or Permission of instructor.
PUBH 6845 - Using Demographic Data for Policy Analysis
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Spring
How to pose researchable policy questions, locate existing data, turn data into a usable format, understand data documentation, analyze data, communicate findings according to standards of the professional policy community. Quantitative issues. prereq: [Grad level research methods course, basic statistics course] or instr consent
CSCI 5511 - Artificial Intelligence I
Credits: 3.0 [max 3.0]
Course Equivalencies: CSci 4511W/CSci 5511
Prerequisites: [2041 or #], grad student
Typically offered: Every Fall
Introduction to AI. Problem solving, search, inference techniques. Logic/theorem proving. Knowledge representation, rules, frames, semantic networks. Planning/scheduling. Lisp programming language. prereq: [2041 or instr consent], grad student
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
HINF 5502 - Python Programming Essentials for the Health Sciences
Credits: 1.0 [max 1.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Computer programming essentials for health sciences/health care applications using Python 3. Intended for students with limited programming background, or students wishing to obtain proficiency in Python programming language. prereq: Junior or senior or grad student or professional student or instr consent
HINF 5531 - Health Data Analytics and Data Science
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Data science methods and techniques for the extraction, preparation, and use of health data in decision making. prereq: Junior or senior or professional student or grad student or instr consent
MILI 6963 - Healthcare Analytics
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course prepares students to analyze large health care databases with a focus on advanced applications with health insurance claims data. The course is designed to be a STEM offering with the use of statistical programming languages including R, Tableau, and SAS. This course is designed to appeal to students with an interest in developing data science as a core skill and already have knowledge of some programming tools, and experience with data manipulation in Excel, SQL, or Access. The course utilizes a novel synthetic health insurance claims database representing 300 million covered lives of the major private and publicly insured insured populations in the United States. Major topics include market sizing, actuarial projection, quality of care metrics, and national health account calculation.
PA 5929 - Data Visualization: Telling Stories with Numbers
Credits: 2.0 [max 2.0]
Typically offered: Every Fall & Spring
Tools for communicating quantitative information in an intelligent, effective and persuasive way. Topics covered include 1) writing and speaking about data; 2) data management in Excel in order to prepare data for charting; 3) understanding and ability to deploy core concepts in of design, layout, typography and color to maximize the impact of their data visualizations 4) determining which types of statistical measures are most effective for each type of data and message; 5) determining which types of design to use for communicating quantitative information; and 6) designing graphs and tables that are intelligent and compelling for communicating quantitative information.
PUBH 6107 - Excel Skills for Data Management in Public Health Settings
Credits: 1.0 [max 1.0]
Typically offered: Every Spring
Hands-on course on computer skills to learn a wide range of methods to manipulate public health data. Students will be given ?raw? datasets and practice computer methods to clean, filter, recode, combine, tabulate and report data within the Excel and Access environments. The course is ideal for students who may not pursue more advanced quantitative training but still want to feel comfortable using these widely available programs to produce quality datasets for further analysis, and to generate summary results or reports in their work as public health practitioners.
PUBH 6325 - Data Processing with PC-SAS
Credits: 1.0 [max 1.0]
Typically offered: Every Spring
Introduction to methods for transferring/processing existing data sources. Emphasizes hands-on approach to pre-statistical data processing and analysis with PC-SAS statistical software with a Microsoft Windows operating system.
PUBH 6420 - Introduction to SAS Programming
Credits: 1.0 [max 1.0]
Typically offered: Periodic Fall & Summer
Use of SAS for analysis of biomedical data. Data manipulation/description. Basic statistical analyses (t-tests, chi-square, simple regression).
PUBH 6717 - Decision Analysis for Health Care
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Introduction to methods/range of applications of decision analysis and cost-effectiveness analysis in health care technology assessment, medical decision making, and health resource allocation.
PUBH 6739 - Data Dashboards and Visualization with Tableau
Credits: 1.0 [max 1.0]
Typically offered: Every Spring & Summer
The ability to analyze data is an essential skill for public health practitioners in all areas of professional practice. Data analysis is necessary to identify important emerging and/or current trends, problems, and issues that require action. It is important for anyone engaged in public health work to be able to locate relevant and accurate sources of data relative to public health issues, analyze it, synthesize it, and format it in a way that it is clear and compelling to specific audiences. This course provides an introduction to data analysis and presentation through the creation of a dashboard to present data. While this course uses Tableau software, the concepts covered in this course apply to any public health setting and are transferable to other dashboard and data visualization tools.
PUBH 6819 - Qualitative Research Theory and Methods for Health and Health Services Research
Credits: 2.0 [max 4.0]
Typically offered: Every Fall
This course is designed for graduate students who expect to use qualitative methods in their research and/or those who desire to expand their knowledge base with a deeper understanding of the types of qualitative methods and mixed methods being used in health and health services research today. The course gives students a broad overview of various data collection and analysis methods. The purpose of the course is to prepare students to conduct a variety of approaches or methodologies in qualitative research design and mixed methods suited to the health and health services research and health policy research questions they wish to pursue. PubH 6819 is intended for students interested in pursuing academic qualitative research and/or as a follow-up to an introductory course like PubH 6636 Qualitative research methods in public health practice.
PUBH 7264 - Data Visualization in R
Credits: 1.0 [max 1.0]
Grading Basis: OPT No Aud
Typically offered: Every Summer
In this course, you will learn how to manipulate data and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 7461 - Exploring and Visualizing Data in R
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 6735 - Principles of Health Policy
Credits: 3.0 [max 6.0]
Course Equivalencies: PubH 6735/PubH 6835.
Grading Basis: A-F or Aud
Typically offered: Every Fall
The purpose of this course is to introduce students to the policy environment that influences and shapes public health and the provision of health care services, to enhance understanding of the historical and political context of health policy, to develop strategies for analysis of health policy issues, and to communicate effectively in the policy environment. Credit will not be granted if credit has been received for PubH 6835.
PUBH 6803 - Conducting a Systematic Literature Review
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Project-based class to develop systematic review skills for evidence-based practice. Draws from AHRQ and Cochrane systematic review methodology; supported by examples from the Minnesota Evidence-based Practice Center. Use for master?s thesis, dissertation, or to support research proposals. Prereq: research study design or epidemiology.
PUBH 6810 - Survey Research Methods
Credits: 3.0 [max 3.0]
Grading Basis: OPT No Aud
Typically offered: Every Spring
Theory/application of survey research in data collection. Sampling, item development, instrument design/administration to conduct survey or be aware of issues related to design/implementation. Identification of sources of error in survey research.
PUBH 6832 - Economics of the Health Care System
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Examines applications of microeconomic principles to the U.S. health care system. Topics include demand for medical care, insurance theory and selection issues, provider payment, competition in health care markets, the health care workforce, pharmaceutical prices and innovation, health care spending growth, quality of care, externalities, the relationship between income and health, and the economics of the opioid epidemic. Prerequisite: an introductory economics or microeconomic theory course ? or permission of the instructor.
PUBH 6855 - Medical Sociology
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to common theoretical/empirical approaches used by sociologists to study health/illness. How content reflects social inequalities in health/illness. Social processes that shape experience of health/illness. prereq: [[Grad or professional school] student, previous experience with statistical software] or instr consent
PUBH 6862 - Cost-Effectiveness Analysis in Health Care
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Government regulations. New technologies. Diagnosis/treatment protocols. Strengths, limitations, appropriateness of different approaches. prereq: instr consent; introductory econ course recommended
PUBH 6864 - Conducting Health Outcomes Research
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Major concepts/principles in conducting health outcomes research that evaluates medical care. Developing study designs matched to research questions. Frequently used study designs. Evaluating health outcomes. Analytical approaches. prereq: Introductory course in epidemiology or health services research methods or instr consent
PUBH 8804 - Advanced Quantitative Methods Seminar
Credits: 3.0 [max 6.0]
Typically offered: Spring Even Year
Understand/competently use advanced quantitative methods in applied social science, policy, demographic research. Methods considered largely within or related to framework of regression analysis. Effort will be made to reflect interests of class. prereq: This is an advanced, doctoral-level course. Students are expected to have completed a full year of doctoral-level introductory statistical and/or econometric classes in their respective field prior to enrolling in this course (e.g., PubH 7401-2, ApEc8211-2, SOC 8801-8811). Exceptions may be granted with instr consent.
PUBH 8814 - Mixed Methods: Quantitative and Qualitative Strategies in Research
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall
The purpose of this course is for students to integrate qualitative strategies with quantitative approaches in research designs. Students will examine the strengths and challenges of using a mixed-methodological framework when selecting conceptual models to guide public health research questions, frame measurement and data collection, appraise strengths and weaknesses of study designs when addressing public health questions of interest.
PUBH 8816 - Implementation Science
Credits: 2.0 [max 2.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
A major focus of health research is the design of high quality interventions. However, whether and how these interventions are deployed successfully in clinical or community settings receives less attention. Given the extensive investment of time and resource in conducting health research, surprisingly few of these intervention innovations are ever "translated" to services, programs, or policies that benefit the lives of individuals, families, and communities. To address this challenge, implementation science has emerged as a set of theories and methodological approaches to enhance the translational process of evidence to practice. The goal of this course is to provide an overview of the key methodological considerations (theory, conceptualization, design, and analysis) when translating science to real world, everyday contexts using implementation science.
PUBH 6320 - Fundamentals of Epidemiology
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
This course provides an understanding of basic methods and tools used by epidemiologists to study the health of populations.
PUBH 6341 - Epidemiologic Methods I
Credits: 3.0 [max 3.0]
Course Equivalencies: PubH 6320PubH /6341
Grading Basis: A-F only
Typically offered: Every Fall
Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.