Public health professionals rely on clinical data every day, yet the path from raw electronic health record (EHR) information to meaningful chronic disease surveillance metrics is rarely straightforward. Behind every percentage, rate, or trend line lies a series of decisions—about data quality, sample characteristics, and the methods used to adjust or refine estimates. This course is designed to illuminate that path.

Learners begin by grounding themselves in the essential terminology that shapes conversations about clinical data usefulness. From there, the course explores how sample population characteristics influence whether crude, unadjusted metrics can be trusted—or whether they risk misrepresenting the communities they are meant to reflect. Participants practice identifying situations where simple queries are sufficient and where strategic adjustments can significantly improve the value of the data retrieved.

Threaded throughout the course is a case study in which scenarios are introduced where advanced statistical approaches become not just helpful but necessary. Learners discover two key methods used to produce adjusted surveillance estimates and gain clarity on when these techniques should be brought into the workflow. Through videos, interactive checkpoints, and a case study woven throughout each module, participants build a practical understanding of how to request, interpret, and refine EHR‑derived metrics with confidence.

In just 90 minutes, this self‑paced experience equips anyone engaged in data modernization efforts with the tools to transform clinical data into actionable, trustworthy insight—ensuring the story the data tells is accurate, meaningful, and ready to support public health decision‑making.