Leveraging Clinical Laboratory Analytics

 

The clinical laboratory generates an enormous amount of data that can be used to improve laboratory efficiency, maintain high-quality testing, and support clinical and operational decisions. In this webinar, we will review the fundamental architecture and data elements common in clinical laboratory testing. Then, using specific examples, we will see how those concepts can be applied to create and analyze reports and dashboards that support clinical and operational decision-making. We will also discuss the importance of data wrangling and some of the potential pitfalls that come with clinical laboratory data.

Lecture Presenter

Jenna Rychert, PhD

Jenna Rychert, PhD

Adjunct Associate Professor
University of Utah School of Medicine
Medical Director, Operational Informatics, Microbial Immunology, and Customer Support; Director, Laboratory and Clinical IT
ARUP Laboratories

Dr. Jenna Rychert is the medical director of Operational Informatics and the director of Laboratory and Clinical IT. She also serves as a medical director of Microbial Immunology, Exception Handling, Referral Testing, and Client Response Communications teams at ARUP Laboratories. She is an adjunct associate professor at the University of Utah School of Medicine. She received her bachelor’s degree in mathematics from Boise State University and her doctorate in microbiology, immunology, and parasitology from Louisiana State University Health Sciences Center. Dr. Rychert served as a postdoctoral fellow at Massachusetts General Hospital and Harvard Medical School while studying the immunology of acute HIV infection. She completed a clinical microbiology fellowship at Massachusetts General Hospital and is certified by the American Board of Medical Microbiology (ABMM). Dr. Rychert is an active participant on the Logical Observation Identifier Names and Codes (LOINC) Laboratory Committee. Her clinical and research interests include laboratory informatics, healthcare interoperability, regulatory oversight of clinical laboratories, and global health.

Objectives

After this presentation, participants will be able to:

  • Provide examples of common clinical laboratory data elements that can create pitfalls in data analysis
  • Identify methods for collecting and analyzing data
  • Discuss the importance of data wrangling

Sponsored by:

University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories