The MHA curriculum addresses the growing demand of data analysts with the knowledge and skills to integrate, analyze and translate the results of health data. Students will have unique opportunities to study and practice analytic methods applied particularly to health data, with basic and advanced skills in data mining, statistics and database processing.
- MHA 500 | Introduction to Healthcare Analytics (3 credits)
- MHA 501 | Programming Tools and Techniques in Data Management (3 credits)
- MHA 505 | Healthcare Delivery Systems (3 credits)
- MHA 502 | Research Methods (3 credits)
- MHA 512 | Applied Statistical Analysis (3 credits)
- MHA 513 | Data Visualization (3 credits)
- MHA 504 | Predictive Data Analytics (3 credits)
- Elective (3 credits)
Total: 30 credit hours *Curriculum listed above effective Fall 2020*
Introduction to Healthcare Analytics (3 credits)
The course introduces basic concepts in healthcare analytics. Students will develop data analysis skills with an emphasis on statistical reasoning. The course is designed to teach students how to use data to make informed decisions. This process includes reviewing the data, exploring all the underlying assumptions, summarizing and analyzing the data and finally translating the results. Discussions and assignments will focus on honing data interpretation and the ability to strategically apply analysis results to improve health outcomes.
Programming Tools and Techniques in Data Management (3 credits)
This course is designed to train students in basic and advanced statistical programming languages (such as SAS or R) together with techniques and tools necessary for data management and data mining. Students will develop skills in the data management process for analytics including data acquisition, cleansing and debugging. Students will be able to relate and aggregate these data in analytic databases, data marts and data warehouses, and will be able to explore different analytical decision tools through case studies and projects.
Healthcare Delivery Systems (3 credits)
This course focuses on the identification and analysis of factors and interrelationships which influence the operation of health services organizations, with specific attention to local health departments, hospitals, multi-institutional systems, integrated health systems and strategic alliances. These organizations will be viewed and discussed comparatively with other types of health service agencies.
Research Methods (3 credits)
This course introduces research methods in a healthcare setting. Students will be able to learn about development of research questionnaire and design, methodology, data collection and sampling techniques, sample size and power analysis, research ethics and validation and effective dissemination of research. Students will be able to explore and evaluate different types of research procedures and outcomes in the healthcare sector.
Applied Statistical Analysis (3 credits)
This course provides students with the skills and knowledge to apply basic statistical methods in the field of healthcare analytics. The course covers commonly used descriptive and inferential statistical methods applied to discrete and continuous random variables. Examples from the field of healthcare will be utilized to illustrate these concepts in applied settings. Students will use the statistical software package R, a free software for statistical computing and graphics throughout the course.
Data Visualization (3 credits)
This course is intended to be a step-by-step introduction to the world of visual analytics and is designed for the beginner and intermediate users of data visualization. The course will help students to understand and apply important concepts and techniques in data visualization, moving from simple to complex situations and then combine them in interactive dashboards. Topics to be covered include data connection, different graphs and charts, quick table calculations, designing interactive dashboards, mapping, unions and joins
Predictive Data Analytics (3 credits)
This course focuses on statistical inference and hypothesis testing methods in predictive analytics. Students will learn the application of statistical methods for analyzing both continuous and discrete data for knowledge discovery. Analytic continuous and discrete data concepts and methods are developed with practical skills in exploratory data analysis. Descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, analysis of variance and covariance (ANOVA and ANCOVA), contingency tables, logistic regression, multinomial and multivariate models will be covered. Application of various statistical methods using case studies and real-world data will leverage statistical assessment and interpretation.
- Leadership and Professionalism (3 credits)
This course will expose learners to effective leadership approaches and skill sets. Topics will include fundamentals of leadership, leadership and professionalism self-assessment, leadership philosophy, professionalism, essential leadership and professional skills, modeling best leadership practices and behaviors, ethics in leadership, institutional and program accreditation, handling conflict and emerging issues. Learners will apply this learning to their professional life through a series of practical exercises.
- Population Health and Preventive Care (3 credits)
This course discusses the determinants of health, health behavior change, measuring health status and influences on health status including health disparities and socioeconomic status. This includes discussion on how healthcare organizations utilize this information to improve health status among populations. Additionally, students will be able to identify and understand population-based approaches aimed at health improvement.
Data Mining and Machine Learning (3 credits)
This course covers healthcare analytics using data mining and machine learning techniques. Statistical software, such as SAS or R, will be implemented for data exploration and visualization, classification, clustering and time series analysis. Decision trees, nearest neighbor algorithm, Bayesian analysis, neural network, genetic algorithm and support vector machine methods will be introduced to the students. Case studies and real-world data will leverage students’ data mining and machine learning outcomes.
Practicum Project (3 credits)
The practicum is designed to demonstrate the student’s accumulated learning experience through an approved healthcare analytics project. The goal of the practicum is to provide students with the opportunity to apply academic theory and acquired technical skills to community-based healthcare research and service in a practice setting. The completed product should bring together the student’s technical competency, communication skills and research capabilities. The practicum project will be guided by the faculty.