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Curriculum

Health Analytics

Health Analytics Curriculum

Students are required to complete 12 courses to earn the degree. The curriculum covers one entry level course (taken within first two quarters of the program) based on prior experience. Programming for Health Analytics entry course for students without a programming background and Introduction to Clinical Thinking entry course for students without clinical or healthcare experience. There are eleven core courses including a practicum course. Review curriculum details and elective choices while you consider applying to this program. Current students should refer to curriculum requirements in place at time of entry into the program.

 


 

Entry Courses

Complete one entry course in your first or second quarter of the program.

Which entry course should I take?
  • Students without an analytics background (e.g., nursing, respiratory therapy, physical therapy, physicians) should take MSHA 401, Programming for Health Analytics.
  • Students without a clinical background (e.g., engineering, economics, statistics) should take MSHA/MHI 402, Introduction Clinical Thinking.

Note: Students with neither an analytics or clinical background should take MSHA 401, Programming for Health Analytics

Entry courses:

MSHA 401 Programming for Health Analytics

This course will provide core skills in working with R and Python for analytic tasks, such as loading data, installing packages, data cleaning tasks, building and viewing tibbles, data visualization with ggplot2 (grammar of graphics), and finding answers using online resources.

MSHA/MHI 402 Introduction to Clinical Thinking

This course focuses on health care data and documentation, develops the basics of clinical reasoning, the use of diagnostic tools, health care quality and error reduction, and the use of data to improve health.

Core Courses

Complete these 11 courses to earn your degree:

MSHA 403 Introduction to American Healthcare, Digital Health, and Analytics

This course will provide an introduction to the current structure and emerging trends shaping the US Healthcare System. Students will learn what it means to navigate the confusing, bottom-up, and variously incentivized entities in the American health care system.  Additionally, we will cover foundational healthcare data sources for the 3Ps (Providers, Patients, and Payers) and fundamentals of Health Information Technology including electronic health records, health information exchanges, clinical decision support, consumerism, and the impact of big data and predictive analytics.  Topics center on how information technology and analytics enables patient care and fuels modern health care organizations.

MSHA 405 Database Systems, Big Data Management and Interoperability Standards

This course will familiarize students with standards for the harmonization of health data, such as health information exchanges (HIE’s), application programming interfaces (API’s), standard terminologies (e.g., SNOMED codes for diagnosis), and messaging. This course is important to understand how clinical data sources may be linked. Behind every analytics project is an analytical data source. In this course, students explore the fundamentals of data management and data preparation. Students acquire hands-on experience with various data file formats, working with quantitative data and text, relational database systems, and document database systems. They access, organize, clean, prepare, transform, and explore data, using database shells, query and scripting languages, and analytical software.

MSHA 407 Data Security, Ethics and Governance

This course examines selected ethical considerations that are significant components of health sciences, informatics and electronic medicine and need to be addressed during the delivery of healthcare using e-medicine models.

MSHA 409 Statistical Analysis

This is an introductory course to general concepts and fundamentals in the practice of biostatistics as commonly used in health data. This will form the foundation for more advanced topics to come in later courses, such as intermediate biostatistics. On its own, the material covered here will be sufficient to perform basic descriptive statistical analyses on your own, and indicate when you should ask for assistance.

 

The goal of this course is to teach students how to perform basic statistical analysis of health data sets in RStudio. The techniques you’ll learn in this course are important in themselves, and will form the foundation for later courses in the MSHA program, as well as learning how to be productive in R Studio.

MSHA 410 Regression and Multivariate Analysis

This course develops the foundations of predictive modeling by: introducing the conceptual foundations of regression and multivariate analysis; developing statistical modeling as a process that includes exploratory health data analysis, model identification, and model validation; and discussing the difference between the uses of statistical models for statistical inference versus predictive modeling. The high level topics covered in the course include: exploratory data analysis, statistical graphics, linear regression, automated variable selection, principal components analysis, exploratory factor analysis, and cluster analysis.

Prerequisite: MSHA 409 Statistical Analysis

MSHA 411 Generalized Linear Models

This course extends linear ordinary least-squares regression, introducing the concept of the generalized linear model and its use in making predictions in health care. The course reviews traditional linear regression as a special case of generalized linear models, and then continues with logistic regression, Poisson regression, and survival analysis. The course is heavily weighted towards practical application with large health data sets containing missing values and outliers. It addresses issues of data preparation, model development, validation, and deployment.

Prerequisite: MSHA 409 Statistical Analysis and MSHA 410 Regression and Miltivariate Analysis

MSHA 412 Feature Engineering and Text Mining

This course will provide students with the skills to develop analytical features from health datasets. Students will develop an understanding of healthcare data, particularly electronic health record (EHR) data, and use R & SQL to build features for analytical modeling. In addition to working with continuous and categorical health data, students will understand and develop skills for natural language processing to extract discrete data elements from free-text clinical documentation, such as physician notes, for the development of analytical features.

Prerequisite: MSHA 405 Database Systems, Big Data Management and Interoperability Standard

MSHA 422 Practical Machine Learning and Artificial Intelligence

Artificial Intelligence and Practical Machine Learning examines the theory, design, and practical application of systems that evolve over time to improve decision making. We will examine statistical foundations of machine learning, data science approaches to solving business problems, and evaluation of learning health systems.

MSHA 455 Data Visualization and Storytelling

Students will learn how to communicate data effectively through graphics based upon text, time-series, geospatial, and network displays.

MSHA 480 Health Analytics Leadership

This course is designed as an introduction to health analytics leadership practice, high level project management, customer engagement, and effective communication in health care organizations. Healthcare has seen a tremendous increase in available data in the past decade; however much of it is siloed and very difficult to piece together. Physicians and leaders struggle with reliability and transparency of data. Managers struggle to get the data they need to make informed decisions.

Students in analytics-based roles and disciplines will learn organizational strategies for developing and executing a robust Business Intelligence vision and strategic plan. Health care organizations with a strong business intelligence platform enable clinical and business decision making and improve the efficiency of the overall data delivery system.

Leadership strategies including data governance fundamentals, elements of the Business Intelligence (BI) maturity model, and key practices to improve organizational data literacy will be examined. Students will also learn methods to effectively lead projects and engage both leadership and key stakeholders using change management principles, models, and project management tools. Before analytic tasks are undertaken, change management ensures an organizational culture that will support a successful data analytics strategy. Project and portfolio management tools will ensure effective execution of the strategy. 

This course introduces best practices in leading change and project management, including: stakeholder engagement, project chartering, scope definition, and key metric development. Students will use these methods and models to demonstrate their understanding and ability to improve project definition and structure. Students should able to execute projects more effectively in their organizations.

The course will also focus on developing effective communication and presentation skills to translate analytics to actionable recommendations that can be used to solve problems in their organizations. Through case scenario exercises, students will deepen their ability to present data analyses and recommendations in a clear and concise manner, evaluate analyses others have done, and articulate the strengths and limitations of their analyses. Students will demonstrate success if they are able to connect and translate their analytics to purpose, process, and people.

MSHA 498 Capstone

The Capstone is intended to be the culmination of coursework towards the MSHA degree. In the Capstone, students will

  • Apply tools learned during MSHA coursework to define a problem in health care
  • Define the data needed to resolve the problem
  • Perform the analysis, and
  • Communicate the results and conclusion of the analysis in written form.

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