Biostatistics for Electronic Health Record (EHR) Data
** available as of 01/01/2026
** available as of 01/01/2026
Prerequisites: Students must have completed MPBH 449.
This course delves into the analysis and interpretation of data from Electronic Health Records (EHRs), a cornerstone of clinical, epidemiologic, and translational research. Emphasizing a hands-on approach, it covers EHR system fundamentals, data extraction and cleaning, privacy standards, case identification, study design, and advanced statistical analysis. Students will learn to navigate EHR data structures, medical vocabularies, and relational databases, applying these skills to design research studies and create patient cohorts. The goal is to equip students with the ability to harness EHR data for innovative healthcare solutions.
Outcomes: Explain relational database and data warehouse models pertinent to EHR data; Use medical vocabularies and ontologies (i.e., ICD, CPT, NDC, SNOMED, and LOINC codes) for accurate case identification; Develop skills in constructing patient cohorts and extracting relevant data; Identify controls and apply propensity score matching in observational research; Design robust matched case-control and retrospective cohort studies using EHR data; Recognize privacy and security standards, including HIPAA regulations; Apply advanced statistical methods to analyze EHR data and assess research findings critically.
This course delves into the analysis and interpretation of data from Electronic Health Records (EHRs), a cornerstone of clinical, epidemiologic, and translational research. Emphasizing a hands-on approach, it covers EHR system fundamentals, data extraction and cleaning, privacy standards, case identification, study design, and advanced statistical analysis. Students will learn to navigate EHR data structures, medical vocabularies, and relational databases, applying these skills to design research studies and create patient cohorts. The goal is to equip students with the ability to harness EHR data for innovative healthcare solutions.
Outcomes: Explain relational database and data warehouse models pertinent to EHR data; Use medical vocabularies and ontologies (i.e., ICD, CPT, NDC, SNOMED, and LOINC codes) for accurate case identification; Develop skills in constructing patient cohorts and extracting relevant data; Identify controls and apply propensity score matching in observational research; Design robust matched case-control and retrospective cohort studies using EHR data; Recognize privacy and security standards, including HIPAA regulations; Apply advanced statistical methods to analyze EHR data and assess research findings critically.