Description: Automation is typically tied to the execution phases of bioanalysis, but pipeline inefficiencies accrue at every stage of a project: study design, screening, execution, and data analysis. We present a framework that leverages automation, software, and statistical modeling across each stage of the project lifecycle, from sample receipt through report generation. This framework surfaces more information faster, returning decision-making power to sponsors and enabling transparent, reproducible bioanalysis at scale.
Learning Objectives:
1. Understand how automated parts compound and resolve functional bottlenecks
2. Develop a broader understanding of automation beyond liquid handling
3. Recognize how faster, richer data delivery improves sponsor oversight
