This session will first provide an overview of data analytics in laboratory medicine and how it can be used to improve quality and efficiency within the clinical laboratory. We will then illustrate the utility of various data analytics tools using real-world examples of data-driven quality improvement initiatives in clinical chemistry, urinalysis, coagulation, and toxicology. Finally, we will discuss practical considerations for successful data analytics projects.
Learning Objectives:
- Describe three data analytics applications in the clinical laboratory
- Compare and contrast two different tools for data analytics
- List a common pitfall in data analytics projects and a strategy for avoiding it
