Clinical Informatics Workshop In-Person
In this course, you will get an introduction to the field of clinical informatics, using computational methodologies to mine and analyze Electronic Health Record Data (EHR). In this workshop, we will go over examples of studies that incorporate and feature EHR data and current state of the field, including barriers, issues, and how to overcome them working with these data. We will further focus on the utility of common data models, such as Observational Medical Outcomes Partnership (OMOP) model created by the OHDSI consortium, which is in the center of the UC-Health Data Warehouse. Lastly, we will demonstrate tools and applications developed at UCSF for interfacing with such data, including an R-package: ROMOP and a visualization tool: PatientExploreR. This demonstration includes interactive tutorials.
By the end of the workshop participants should:
- Understand components of EHRs
- Understand strengths and weaknesses of EHR work
- Understand what a common data model is (e.g. the OMOP model)
- Know how to extract and search for EHR data in R, using the ROMOP
- Know how to use PatientExploreR tool to visualize EHR data
There are no prerequisites for this course other than having an interest in clinical informatics and EHR data analysis. Some exposure and familiarity with R is recommended but not at all required.
This course does not require any special software to be installed. However, you should bring your laptop to the workshop so you can follow along with exercises using ROMOP and PatientExploreR. Installation of R is recommended, but again not required for tutorials.
Benjamin Glicksberg, PhD is a Postdoctoral Scholar in Atul Butte’s lab in the Bakar Computational Health Sciences Institute at UCSF. He is also the developer of ROMOP and PatientExploreR.
For UCSF Faculty:
Additional seats reserved for UCSF Faculty. Register at this link.
- Wednesday, Mar 13 2019
- 1:00pm - 4:00pm
- Time Zone:
- Pacific Time - US & Canada (change)
- Mission Hall 1407
- Mission Bay
- Data Science Initiative > Bioinformatics and Statistics Data Science Initiative Data Science Initiative > Programming