Healthcare Analytics (MS) Online Master's Degree Program

In this course students learn to differentiate relational and non-relational databases, and to design a database to an organization's requirements. Preparing raw data for import into a database, using queries to manipulate data, and developing reports to meet stakeholder needs, and database integrity and security are also covered.

Students learn to create and use scripts to access, acquire, prepare, and transform data, including structured and semi-structured data, and to define and detect patterns in unstructured data to demonstrate how real world problems can be answered with data.

In this course students learn about data types and how to perform pre-processing activities including cleaning, integration, reduction, and transformation. Use of appropriate techniques to analyze and model data, such as classification, decision trees, association rules, and clustering, and the causes and methods of detecting outliers are also covered. Students propose additional applications for data mining based on current trends/research.

In this course students learn about deep learning, machine learning, and artificial intelligence, and compare supervised and unsupervised learning. Machine learning workflows and the challenges associated with machine learning, such as bias and heteroscedasticity, are considered. Students evaluate models, and examine uses for machine learning and artificial intelligence. Natural Language Processing is also addressed.

Students learn about the U.S. health care system, including the government's role in the industry, cost and quality issues, managed care, reimbursement mechanisms, legal and regulatory issues, profit vs. non-profit care, the role of technology, and then compare it to international health care systems. Students will begin to conceptualize new models of delivery through analysis of barriers to process improvement and the use of design thinking and analytics. Students will appraise development, implementation and management of system components.

Managing information as a strategic resource is challenging. Students will design IT systems to capture, combine, and transform data into information to measure processes/outcomes of care, support clinical decision-making and management decisions, empower patients, and improve health care operations. Students explore health information systems within the context of associated business processes to develop innovative solutions. Management of change as related to clinical systems will also be stressed to help build buy-in, engage individuals and ultimately improve workflow and system design.

Students gain systemic overview of the financial operations of common healthcare organizations, both public and private and an understanding of various financial components enabling health services to be delivered. Students identify key issues confronting the financial management of healthcare systems and provide innovative strategies for effective management of these financial management challenges. Topics explored include traditional and innovative financial management techniques and data analytical methods applied to healthcare settings, as well as financial management challenges unique to the industry.

Students will utilize specialized healthcare analytics techniques, including patient journey analysis, predictive modeling, and integration of genomics with patient data. Through case studies, students explore how analytics enhances decision-making and patient care while managing data quality and integration challenges.

Addressing the surge in healthcare data, this course provides data visualization techniques tailored for healthcare environments. Students will learn tool applications, dashboard design, and best practices, ensuring effective communication of healthcare data and insights to diverse stakeholders.

Students will integrate learning and competencies from the Business Analytics program into a single data-based project.? Given a data set, students will ask such questions as why is the data organized as it is, what are the benefits and limitations of the data set, what appropriate visualization elements should be developed, and what business recommendations should be made.