Program Description
The M.S. in Clinical Informatics consists of 11 courses with a total of 34 credits. The program can be broken down by 12 credits for the UMB Clinical Informatics PBC, 12 credits for the UMBC Data Science PBC, and 10 credits in practical courses hosted at UMB.
The instruction will primarily occur in a hyflex enviornment, offering in-person and online options, which will include both synchronous and asynchronous learning.
Program Completion Timeline
- The degree is designed for completion within 2 years academic years, but students are allowed up to 5 years to complete the program.
- Program enrolls in the fall and spring semester.
By the completion of the proposed M.S. in Clinical Informatics, students will develop core competencies in four key areas: foundations and theory, clinical decision making and care process improvement, health information systems, and leadership and change management, as defined by the American Medical Informatics Association.
Graduates will walk away with the following competencies, and more:
- Describe the key concepts of Clinical Informatics, Nursing Informatics, Pharmacy Informatics, and Clinical Research Informatics.
- Analyze key concepts, models, and theories of informatics
- Analyze quality improvement efforts regarding safety, effectiveness, efficiency, patient-centeredness, timeliness, and equity.
- Understand the nature and cognitive aspects of human decision making.
- Understand evidence-based medicine, evidence sources, evidence grading, implementation of guidelines, and information retrieval and analysis
- Build effective of interdisciplinary leadership teams and communication strategies.
- Critically evaluate health information systems applications by type of functionality, setting where systems are used, telehealth capabilities, and relationship to the electronic health record.
- Understand computer systems, including programming, control structures, data structures, software development methods, computing architectures, networking, security, data management, data manipulation, and data sharing.
- Analyze approaches to human factors engineering.