CLINICAL RESEARCH INFORMATICS

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Recognize the various types and sources of health data, and the key issues involved in processing health data including security, privacy, noise, and missingness Audience: Graduate 2. Conduct observational studies using data that are centered around answering research questions Audience: Graduate 3. Implement basic programming and data manipulation using R, and be familiar with a variety of techniques such as descriptive analysis, regression methods, prediction models, phenotyping, and natural language processing Audience: Graduate 4. Apply common tools for managing, accessing and mapping health data, including data standards, controlled vocabularies, and ontologies Audience: Graduate Learning Outcomes: 1. Recognize the various types and sources of health data, and the key issues involved in processing health data including security, privacy, noise, and missingness Audience: Graduate 2. Conduct observational studies using data that are centered around answering research questions Audience: Graduate 3. Implement basic programming and data manipulation using R, and be familiar with a variety of techniques such as descriptive analysis, regression methods, prediction models, phenotyping, and natural language processing Audience: Graduate 4. Apply common tools for managing, accessing and mapping health data, including data standards, controlled vocabularies, and ontologies Audience: Graduate