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This is the GitHub repository for the R/Medicine 2022 pre-conference workshop R/Medicine 101: Intro to R for Clinical Data. There is a course website here: https://stephan-kadauke.quarto.pub/intro-to-r-for-clinical-data-rmed2022/

Stephan Kadauke, MD, PhD (he/him) is the Assistant Director of the Cell and Gene Therapy Lab in the Department of Pathology at the Children's Hospital of Philadelphia. He leads the Cell and Gene Therapy DataOps group which builds and deploys predictive models and other data products for the care of children who receive bone marrow transplants and other cell therapies. Stephan developed a curriculum in Reproducible Clinical Data Analysis tailored for physicians and other healthcare professionals. He is passionate about ways to use data to improve the care of children.

Joseph Rudolf, MD (he/him) is the Medical Director for the automated core laboratory at ARUP Laboratories in Salt Lake City, Utah. He earned his medical degree from the University of Washington School of Medicine in Seattle, Washington. He completed his residency training in Clinical Pathology and fellowship in Clinical Informatics at the Massachusetts General Hospital in Boston, Massachusetts. His clinical and research interests focus on the intersection of informatics and clinical operations including clinical decision support, utilization management, and reporting and analytics. He is also passionate about clinical process improvement and initiatives to support quality and safety.

Patrick Mathias, MD, PhD (he/him) is the Vice Chair of Clinical Operations and Associate Medical Director of the Informatics Division in the Department of Laboratory Medicine and Pathology at the University of Washington School of Medicine. His interests include developing data science and analytics as a core competency to improve clinical lab operations and laboratory stewardship, and applying clinical informatics approaches to mitigate laboratory-associated diagnostic errors. He is interested in developing and improving programming and data science education across all levels of pathology practice.

All of the material in this GitHub repository is copyrighted under the Creative Commons BY-SA 4.0 copyright to make the material easy to reuse. We encourage you to reuse it and adapt it for your own teaching as you like!