Graduate Certificate in Health Informatics
Attention: Starting immediately, Graduate Certificates will no longer be available. Enrolled students can either complete their existing certificate programs or consult with their advisor to explore the possibility of transitioning into a master's degree program.
Program Description
The Health Informatics Certificate is designed to enable students to acquire the core knowledge that applies to the fundamentals, principles, and practice of medical informatics. This certificate option consists of 15 credit hours of graduate level courses which are presented using online learning technology.
If after taking courses in the certificate program, a certificate-seeking student decides to pursue the M.S. degree, the student must submit a new and complete application to become a degree-seeking student and must meet all requirements for admission to the M.S. program. Previous coursework taken as a certificate seeking student does not guarantee acceptance into the M.S. degree-seeking program. If accepted into the degree program, credits with the prefix MI taken as a certificate seeking student will be automatically applied towards the degree.
CURRICULUM
The following five courses are needed for the HI certificate:
No. of Credits: 3
Pre-Requisite: None
Description: This on-line, interactive course is an introductory survey of the discipline of biomedical informatics. This course will introduce the student to the use of computers for processing, organizing, retrieving and utilizing biomedical information at the molecular, biological system, clinical and healthcare organization levels. The course is targeted at individuals with varied backgrounds including medical, nursing, pharmacy, administration, and computer science. The course will describe essential concepts in biomedical informatics that are derived from medicine, computer science and the social sciences.
Learning Objectives:
- Demonstrate in writing and verbally a basic understanding of the learned concepts of biomedical informatics and their direct application to healthcare.
- Demonstrate the ability to compare, select, apply and integrate multiple technologies in and across a healthcare organization.
- Discuss key legal and ethical issues that must be considered when implementing biomedical technology and supporting information systems.
- Differentiate multiple methods to evaluate the costs versus benefits of implementing biomedical information systems.
- Produce evidence of a forward thinking ability to stay current in biomedical informatics.
No. of Credits: 3
Pre-Requisite: None
Description: This course introduces the fundamental principles of project management from an information technology (IT) perspective as it applies to healthcare organizations (HCOs). Critical features of core project management are covered including: integration management, scope management, time management, cost management, quality management, human resource management, communication management, risk management, and procurement management. Also covered is information technology management related to project management: user requirements management, infrastructure management, conversion management, software configuration, workflow management, security management, interface management, test management, customer management, and support management. The following areas of change management related to project management will also be covered: realization management, sponsorship management, transformation management, training management, and optimization management. Students will explore and learn hands-on skills with project management software assignments, and participate in a healthcare systems implementation course-long group project intended to apply these newly developed knowledge and skills in a controlled environment.
Learning Objectives:
- Explain the genesis of project management, information technology management, change management and their importance to improving successful HIT projects in the healthcare setting
- Apply project management concepts by working on a group project as project management or active team member
- Demonstrate how to use Microsoft Project 2013 software to help plan and manage a project
- Demonstrate knowledge of project management terms and methods
- Demonstrate knowledge of information technology terms and methods as they relate to project management
- Demonstrate knowledge of change management terms and methods as they relate to project management
No. of Credits: 3
Pre-Requisite: None
Description: This course covers from basic to intermediate knowledge of the concept, the design and the implementation of database applications in healthcare. Students will study tools and data models for designing databases such as E-R Model and SQL. The course also covers Relational DBMS systems such as SQL Server, Access, Oracle and mySQL. Besides, database connectivity design (essential in data-driven web development) and database administration will also be introduced.
Students will practice designing, developing and implementing a test relational online health IT database application (myHealth) through a comprehensive project that contains the above topics.
Learning Objectives:
At the end of the course, student will be able to:
- Identify the key elements of database management system and applications in healthcare.
- Plan, document, and design a medical informatics database application.
- Identify and model healthcare database application using ER Model and query against the database with SQL.
- Identify the key concepts and process in order to SQL server, Access, Oracle or mySQL DBMS systems to build up a data-driven web application.
- Identify the basic concepts of database administration and data warehouse for decision support system (DSS).
No. of Credits: 3
Pre-Requisite: None
Description: Lean Six Sigma for Health Care (Yellow Belt) participants will learn the basic philosophy, tools, and techniques to deliver breakthrough business improvements that will reduce waiting times, improve quality, and reduce costs in a health care environment. More specifically, they will learn to apply a comprehensive set of 15-20 Lean Six Sigma process improvement tools by using the PDCA (Plan, Do, Check, Act) problem solving model. They will learn techniques for both quantitative and non-quantitative analysis, as well as methods and tools for waste reduction and process enhancement and acceleration. The course also covers how to map out processes and identify sources of variation, as well as to gain a basic understanding of descriptive statistical analysis. Finally, they will learn how to perform basic pilot studies and analyze the results, in order to determine the most effective way to improve and stabilize processes. Candidates work on either an integrated health care case study or on an actual business project, and will apply classroom techniques to the project.
Learning Objectives:
At the end of the course, student will be able to:
- Summarize Lean Six Sigma history and philosophy and describe how it applies to modern health care organizations.
- Identify opportunities for system and process improvement in health care settings.
- Use basic problem solving and critical thinking skills and apply systems thinking to quality improvement projects in hospitals and other clinical settings.
- Apply techniques to decrease health care costs, increase patient safety, improve treatment outcomes, and increase customer satisfaction.
- Identify valid and critical to quality customer and business requirements and related measures and then turn the data into actionable information to manage and improve organizational processes.
- Map out work flow processes using Excel/Visio to identify sources of waste.
- Apply the PDCA/DMAIC model in accordance with Lean Six Sigma principles.
- Conduct beginning-level descriptive statistical analyses to determine baselines and identify improvements
- Learn different improvements designs to most effectively improve and stabilize processes.
- Analyze measurement patterns and results of biomedical information utilizing basic statistical concepts in conjunction with Lean Six Sigma-specific software (e.g.,SigmaXL) to synthesize pertinent data.
- Identify risks and basic root causes for typical process challenges.
1) MI 5152 Information Security in Health Care
Description:
The course will cover concepts, applications and techniques of data security in healthcare system. Topics include healthcare industry, regulatory environment, decision making, policy assurance, information management, access control, risks and vulnerabilities management, database security, web security, personnel and physical security issues, and issues of law and privacy. Areas of particular focus include secure healthcare system design, implementation, data encryption and decryption, attacks, and techniques for responding to security breaches.
Learning Objectives:
- Identify healthcare organizations and third party affiliates.
- Prioritize threats to healthcare information resources.
- Define an information security strategy and architecture.
- Plan for and respond to intruders in a healthcare information system.
- Identify the practical application of risk management and decision making.
- Identify the practical application of risk assessment.
2) MI 5204 Clinical Decision Support Systems
Description:
This course introduces students to theoretical, statistical, and practical concepts underlying modern medical decision making. Students will be provided a review of the multiple methods of knowledge generation for clinical decision support systems (CDSS) and create their own prototype of CDSS. Current implementations of stand-alone and integrated CDSS will be evaluated. Techniques for planning, management, and evaluation of CDSS implementations will be reviewed. Human factors, including work-flow integration, and the ethical, legal and regulatory aspects of CDSS use will be explored, as applicable to commercial implementations in patient care settings. Future models of healthcare, supported by CDSS and evidence-based medicine, will be discussed and reviewed.
Learning Objectives:
- Describe the scope and kinds of clinical decision support systems; analyze CDSS effectiveness in terms of implementing for diagnostic and therapeutic purposes.
- Evaluate the linkage of CDSS to the basic concepts of evidence-based medicine.
- Apply practice guidelines for clinical decision support, including commonly-used formalisms and authoring tools for computer-interpretable guidelines.
- Describe the social and political forces driving implementations of CDSS in the clinical field.
- Compare and contrast the types of CDSS available in commercial and research implementations.
- Apply statistical methods and logic concepts, such as probability, regression, Boolean logic, set theory, and inference, to underlying medical decision making.
- Evaluate at least three methods of knowledge generation for CDSS, including decision trees, neural networks, and Bayesian analysis.
- Compare the advantages and disadvantages of supervised vs. unsupervised learning methods in data-mining applications.
- Evaluate how CDSS fold into the overall hospital and/or medical office health information technology environment.
- Analyze technology and business characteristics of successful CDSS implementations using recent industry cases as guidelines and input to build student’s own attributes of an effective CDSS implementation.
- Recognize business and clinical implementation and maintenance challenges in commercial CDSS projects, as well as possible resolutions to these challenges.
- Assess risks involved with poor CDSS implementations from the following standpoints: health outcomes, quality of care, medical error rates, and patient and provider satisfaction standpoints.
- Discuss ethical and regulatory issues involved in design and implementation of CDSS systems.
- Identify opportunities for use of CDSS in personal health records and shared decision-making.
3) MI 6700 Computational Informatics
Description:
This course will provide an introductory, hands-on experience for life science researchers in bioinformatics using R and Bioconductor. Emphasis will be placed on accessing, formatting, and visualizing genomics data. Most analyses will deal with “little” data (no mapping or assembly of short reads), but some techniques to work with “big” data (e.g. BAM files) will be covered. Lecture and lab will both be held in a computer lab, so lecture will be “hands-on”. Working in small groups is encouraged.
Learning Objectives:
- Students will learn the fundamentals of bioinformatics analyses of genomics data using R and Bioconductor.
- Students will gain a greater appreciation for bioinformatics and the parallels with “wet bench” experiments.
- Students will be introduced to the concept of “literate programming” and how it can be applied to document their work are write legible reports.
- Students will be prepared for more advanced courses in R or bioinformatics, or for continued self-learning.
ADMISSION REQUIREMENTS
- Completed application form
- Non-refundable application fee of $50
- Official transcripts of all undergraduate, graduate, and professional education (Must have a bachelor's, master's, or doctoral degree from a regionally accredited college or university)
- Official course by course evaluation of all foreign transcripts. Agencies that can complete this evaluation can be found here.
- Applicants whose native language is not English are required to demonstrate English proficiency. One of the following standardized tests listed below will currently satisfy the university's English requirement for nonnative English speakers:
- Test of English as a Foreign Language (TOEFL) 213 on the computer-based test; 79-80 on the Internet based test.
- International English Language Testing System (IELTS) 6.0 on the test module
- Scholastic Assessment Test (SAT) with a score of at least 500 in the reading section, or the American College Test (ACT) with a score of at least 20 on the verbal section
Test results must be sent directly from the testing agency to the center you applied for. Proof of English language competency can also be in the form of successful completion of a degree at an approved U.S. institution of higher education.
PROGRAM LENGTH
Students have a maximum of five years to complete the program. The expected average completion time is one year (2-3 semesters).
TUITION AND FEES
Visit the Tuition and Fees page for more information.