Health Care Analytics Certificate

• Total Required Credits: 12 (Four 3-credit courses)
• Estimated Cost: $8,520 ($710 per credit hour, this is a discounted rate from the MBA/MSBA $1,206  per credit hour), significantly lower than the $12,450 national average cost for comparable graduate certificate programs.
• Length: 3 semesters or one academic year
• Format: In-person or fully online, courses can be counted toward the M.S. in Business Analytics (MSBA) or up to 9 credits into the Master of Business Administration (MBA)

Housed in the AACSB-accredited Martha and Spencer Love School of Business at Elon University, this Graduate Certificate in Healthcare Analytics is to train healthcare professionals and data analytics experts to enhance and transform healthcare through cutting-edge information and analytics technologies within a concise 10-month duration and at a significantly lower cost than the degree programs. Students enrolled in the program will benefit from the deliberately career-focused, practice-oriented curriculum, and the Love School of Business’s innovative approach to education. Upon completion, students will be equipped with the necessary healthcare terminology and knowledge and strong practical skills in data analytics, AI, and data management to develop data-driven healthcare systems to solve real-world medical and healthcare management problems.

Students have the option to roll all the earned 12 credits directly into the M.S. in Business Analytics (MSBA) or up to nine credits into the Master of Business Administration (MBA) if choosing to pursue a formal master’s degree at Elon University later.

Deadlines

July 15 (Fall)
Nov 4 (Winter)

• Completed Application

• A bachelor’s degree from an accredited college or university.

A minimum GPA of 3.0 (on a 4.0 scale) is recommended. We welcome applicants with diverse backgrounds who intend to pursue a career in data analytics, particularly in healthcare analytics. Applicants with health sector work experience are especially welcome to apply.

• Official transcripts of all undergraduate and any graduate studies completed or taken outside of Elon University (please note that Elon transcripts will be requested on your behalf)

Transcripts can be emailed to gradadm@elon.edu or mailed to:

Elon University
Office of Graduate Admissions
2750 Campus Box
Elon, NC 27244

• Statement of purpose addressing how this certificate will help you meet your career goals
• Current Resume
• Two letters of recommendation from an academic or professional source

Our deliberately crafted curriculum strives for an optimal balance between healthcare knowledge, up-to-date technology development, and real-world case studies, ensuring graduates possess integral knowledge and desirable tools and skills in healthcare analytics, maximizing job and career opportunities. Students are required to complete the following four 3-credit courses to earn the certificate, for a total of 12 credit hours, in 10 months and spanning three semesters.

Semester 1

• HCA 5110: Applied Healthcare Statistics (3 Credits)
• HCA 5210: Healthcare Information Systems (3 Credits)

Semester 2

• HCA 5300: Healthcare Analytics (3 Credits)

Semester 3

• HCA 5600: Advanced Machine Learning for Healthcare (3 Credits)

HCA 5110: Applied Healthcare Statistics (3 Credits)

This introductory statistics course is designed for people entering a healthcare profession that requires a solid grasp of statistical methods as the foundation of their work. This will also form the foundation for more advanced topics to come in later courses. The course will emphasize the principles of statistical reasoning, underlying assumptions, hypothesis testing, and careful interpretation of results. It differs from traditional statistics courses as topics will be covered in the context of their direct application to healthcare. Topics include data presentation and summarization, descriptive statistics, regression analysis, fundamental probability theory, and random variables, introductory decision analysis, estimation, confidence intervals, hypothesis testing, and ANOVA.

HCA 5210: Healthcare Information Systems (3 Credits)

This course is to provide students with a broad understanding of the strategic application of information systems technology and leveraging information systems to analyze clinical data and support decision-making in healthcare organizations. Specifically, we will present the fundamental principles, relevant healthcare knowledge and terminology, and building blocks of healthcare information systems, including the characteristics of data, information, and knowledge, the common algorithms for health applications, and technological components in real-world clinical processes. It also introduces the technical framework for handling the collection, storage, and optimal use of biomedical data. Our emphasis is on conceptual frameworks as well as a deeper level of real-world applications, covering the planning, implementation, and evaluation of information systems, and how they relate to practical healthcare decision-making and management. It also provides a basic understanding of data standards and requirements, and the critical concepts and practices in mapping and interpreting health information.

HCA 5300: Healthcare Analytics (3 Credits)

This core course is designed to build solid foundational skills and knowledge in healthcare data analytics for healthcare analysts and technology professionals. With the increasing adoption of electronic health record systems, the ability to understand, analyze, and solve problems from data has become increasingly important in the healthcare area. Big data analytics is becoming central to the healthcare industry, both regarding delivering effective outcomes and controlling escalating costs. Students will explore the value proposition for clinical intelligence and the role of analytics in supporting a data-driven healthcare system. Students will apply knowledge and skills from healthcare data mining, data science, machine learning, AI, and data management to address practical healthcare business and clinical intelligence challenges. In addition, the topics covered in this course will provide a foundation for a future advanced deep learning for healthcare course.

HCA 5600: Advanced Machine Learning for Healthcare (3 Credits)

This advanced machine learning course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. Students are expected to learn deep learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, autoencoder, attention models, graph neural networks, and deep generative models. Students will also get a chance to learn different healthcare applications using deep learning methods such as clinical predictive models, computational phenotyping, patient risk stratification, treatment recommendation, clinical natural language processing, and medical imaging analysis. This course will focus on hands-on experiences for data scientists and machine learning practitioners to implement various practical healthcare models on diverse medical data.

The burgeoning prevalence of big data and recent strides in AI technologies have exerted a positive impact on the expansion of the healthcare analytics market. As per the Market Analysis Report compiled by Grand View Research (Grand View Research 2021), the healthcare analytics market attained a valuation of USD 12.2 billion in 2021 and is projected to experience a compound annual growth rate (CAGR) of 19.2% from 2023 to 2030. Despite these advancements, Global Market Insights (2022) identifies a shortage of skilled professionals in the healthcare sector capable of smoothly adopting novel technologies such as big data analytics, machine learning, AI, and management information systems. The lack of technical and statistical expertise among these professionals emerges as a key impediment to market growth.

Graduates of this program are prepared to excel in a wide range of organizations and roles. In addition, a graduate certificate could also help professionals who are already working in the related healthcare analytics domain qualify for senior or leadership roles in the field.

Possible employers are:

• Clinics and hospitals
• EHR companies
• Health technology start-ups
• Insurance providers
• Pharmaceutical companies
• Pharmacies
• Medical/healthcare consulting and software firms
• Healthcare research institutions
• Governmental agencies
• Public-sector/non-profit organizations
• Other healthcare organizations and facilities

Possible job titles include:

• Health IT developer
• (Clinical) data analysts and scientists
• Clinical informaticist
• Clinical systems application specialist
• Pharmacy informaticist
• Software developer
• (Clinical) systems analyst
• Health informatics specialist
• (Clinical) informatics specialist