This course provides project-based experiential learning in clinical informatics through engagement with real-world quality improvement initiatives embedded within an academic health system. Students will work with clinical, operational, and informatics stakeholders to define problems, assess workflows and data needs, develop and implement informatics-enabled interventions, and evaluate the impact using quality improvement methods and measures. Projects will expose students to the practical challenges of translating informatics concepts into clinical practice, including stakeholder engagement, implementation, data and measurement, iterative improvement, and sustainability. Emphasis is placed on applying clinical informatics knowledge to address meaningful health system priorities while developing practical skills in interdisciplinary collaboration, project execution, and data-driven improvement.
Covers recent advances in data management systems. Topics include complex queries and optimizations, XML data management, spatial data management, distributed and parallel databases, NoSQL databases, and MapReduce based data processing systems. Discusses foundations of data models, transaction models, storage, indexing and querying methods.
Introduces students to the fundamentals of computer science and Python programming with a focus on solving problems in biomedical informatics. Students will learn to write, interpret, and modify code, analyze biomedical data, and explore real-world biomedical applications.
Introduces students to imperative programming language concepts using C. Covers variables, data types, expressions, functions, control-flow constructs, and writing safe and modular programs. No prior programming experience required.
The design of database applications including Entity-Relationship data modeling, the relational data model, SQL, application development, and database administration. Students complete a project designing and implementing a database application.
Covers fundamentals of biomedical informatics including Electronic Health Records, biomedical and healthcare data management and standards, medical imaging informatics, medical image analysis, bioinformatics, and public health informatics and GIS.
A multidisciplinary field intersecting clinical informatics, medical physics, engineering, computer and information sciences. Covers the whole imaging chain: image creation and acquisition, distribution and management, storage and retrieval, processing and analysis, visualization and interpretation. Topics include radiological imaging modalities, DICOM standards, PACS systems, digital pathology, and emerging technologies in Imaging Informatics.