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Only one application needs to be completed for all courses desired during the quarter.
Deadline:
01/02/2026
Format:
In-person
Term Offered:
Winter
Overview
Electronic Health Record (EHR) data can be used for a variety of clinical, epidemiologic and translational research, and these data are becoming more accessible. This course introduces students to concepts, methods, and pitfalls related to the extraction, manipulation and analysis of data from EHRs. The course covers common EHR data structures and vocabularies, using that knowledge to inform research study design, and creation of patient cohorts and analytic extracts. We will cover both ambulatory and inpatient use cases. Students have the opportunity to design their own research projects during the course.
Course Details
The objectives for this course are for participants to understand:
- Relational database and data warehouse models as they pertain to EHR data;
- Medical vocabularies and ontologies used in EHRs;
- Construction of patient cohorts based on structured data, such as diagnosis codes, encounters, and procedures;
- Extraction of relevant associated data for a specified patient cohort, including history, medical orders, laboratory tests, and medications;
- Summarization of the description of patient cohorts from analytic files; and
- Formulating research questions that benefit from the strengths and limit weaknesses of EHR data.
The course presumes students enter with familiarity and direct experience with 1) relational database and 2) manipulation of data with either Stata or R software. This can be achieved with:
- Data Collection and Management Systems for Clinical Research (EPI 218) (or equivalent experience); and
Introduction to Statistical Computing in Clinical Research (BIOSTAT 212) or Introduction to Computing in the R Software Environment (DATASCI/BIOSTAT 213) (or equivalent experience). - If you do not have the specific prerequisites to take this course or have equivalent experience, please reach out to the course directors to discuss options. The course does not presume familiarity with epidemiologic or biostatistical methods, nor does it teach these methods, but students with this background will be able to make links to the curriculum to inform decisions made in the construction of patient cohorts and extraction of relevant associated EHR data.
Learners who would like to gain more SQL experience are encouraged to check out introductory SQL courses at the UCSF library: Intro to SQL for Data Analysis - LibCal
Lecture (Monday):
New material is introduced via a recorded lecture and recommended readings
Large Group Discussion (Tuesday):
Brief review of lecture followed by question and answer discussion. Recorded lecture should be viewed prior to this session.
Computer Laboratory A (Tuesday):
Course faculty are available to address questions regarding the weekly assignment. Students may participate either in the larger forum or in smaller group discussions.
Computer Laboratory B (Friday):
Additional time for students to work on weekly assignments and ongoing projects with group-based and one-on-one assistance from course faculty.
Assignments due (Monday):
Submission of the completed weekly assignment
Prior to the beginning of the course, students should obtain access to the UCSF Research Analysis Environment (RAE), the UCSF de-identified Clinical Data Warehouse, and the UCSF Git (a system that allows you to share and collaborate on source code).
RAE also contains instances of R and Stata to manipulate and analyze data obtained from the de-identified EHR warehouse, but students may wish to use their own copies of this software outside of RAE.
All course materials and handouts will be posted on the course's online syllabus.
Grades will be based on total points achieved on the weekly problem set homework assignments (~70%) and the final project (~30%). Late assignments are not accepted. Answer keys to problem sets will be posted shortly after the turn-in deadline.
Students not in full-year DEB Education Programs who satisfactorily pass all course requirements will, upon request, receive a Certificate of Course Completion.
Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.
For UC-Affiliated learners, the course fee is $3,200
For Non-UC-Affiliated learners, the course fee is $3,800
UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.
The objectives for this course are for participants to understand:
- Relational database and data warehouse models as they pertain to EHR data;
- Medical vocabularies and ontologies used in EHRs;
- Construction of patient cohorts based on structured data, such as diagnosis codes, encounters, and procedures;
- Extraction of relevant associated data for a specified patient cohort, including history, medical orders, laboratory tests, and medications;
- Summarization of the description of patient cohorts from analytic files; and
- Formulating research questions that benefit from the strengths and limit weaknesses of EHR data.
The course presumes students enter with familiarity and direct experience with 1) relational database and 2) manipulation of data with either Stata or R software. This can be achieved with:
- Data Collection and Management Systems for Clinical Research (EPI 218) (or equivalent experience); and
Introduction to Statistical Computing in Clinical Research (BIOSTAT 212) or Introduction to Computing in the R Software Environment (DATASCI/BIOSTAT 213) (or equivalent experience). - If you do not have the specific prerequisites to take this course or have equivalent experience, please reach out to the course directors to discuss options. The course does not presume familiarity with epidemiologic or biostatistical methods, nor does it teach these methods, but students with this background will be able to make links to the curriculum to inform decisions made in the construction of patient cohorts and extraction of relevant associated EHR data.
Learners who would like to gain more SQL experience are encouraged to check out introductory SQL courses at the UCSF library: Intro to SQL for Data Analysis - LibCal
Lecture (Monday):
New material is introduced via a recorded lecture and recommended readings
Large Group Discussion (Tuesday):
Brief review of lecture followed by question and answer discussion. Recorded lecture should be viewed prior to this session.
Computer Laboratory A (Tuesday):
Course faculty are available to address questions regarding the weekly assignment. Students may participate either in the larger forum or in smaller group discussions.
Computer Laboratory B (Friday):
Additional time for students to work on weekly assignments and ongoing projects with group-based and one-on-one assistance from course faculty.
Assignments due (Monday):
Submission of the completed weekly assignment
Prior to the beginning of the course, students should obtain access to the UCSF Research Analysis Environment (RAE), the UCSF de-identified Clinical Data Warehouse, and the UCSF Git (a system that allows you to share and collaborate on source code).
RAE also contains instances of R and Stata to manipulate and analyze data obtained from the de-identified EHR warehouse, but students may wish to use their own copies of this software outside of RAE.
All course materials and handouts will be posted on the course's online syllabus.
Grades will be based on total points achieved on the weekly problem set homework assignments (~70%) and the final project (~30%). Late assignments are not accepted. Answer keys to problem sets will be posted shortly after the turn-in deadline.
Students not in full-year DEB Education Programs who satisfactorily pass all course requirements will, upon request, receive a Certificate of Course Completion.
Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.
For UC-Affiliated learners, the course fee is $3,200
For Non-UC-Affiliated learners, the course fee is $3,800
UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.
Meet the Faculty
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How to Enroll
Whether you're exploring the field, enhancing your skills, or working toward a larger academic goal, most individual courses are open to everyone. Current learners enrolled in DEB Education programs have priority in enrollment for this course. Any extra spaces are available to non-DEB Education learners (i.e., those from the Institute for Global Health Sciences, School of Nursing, external groups, etc).
You’ll find details on course fees, how to apply and pay, and the full course schedule on the Course Enrollment & Payment page. Be sure you understand the enrollment policies and payment process before completing the enrollment form.
And remember: you only need to submit one form for all the new courses you want to take.