This chapter covers data provenance or data lineage, the detailed history of how data was created and manipulated, as well as the process of ensuring the validity of such data by documenting the details of its origins and transformations. Data provenance is a central challenge when working with data. Computing helps but also hinders our ability to maintain records of our work with the data. The best science will result when we adopt strategies to carefully and consistently record and track the origin of data and any changes made along the way. For instance, we want to know where (by whom) a dataset was created and what was the process used to create it. Then, if there were any changes, such as fixing erroneous entries, we need to have a good record of such changes. With these goals in mind, we discuss best practices for tracking data provenance. While such practices generally take time and effort to implement, making them seem tedious in the short term, over time, your research will become more reliable, and you and your collaborators will be grateful.

Introduces the basic concepts of quality improvement: structure, process, and outcomes; and the importance of considering them early in your developments. Explores the relevance of a Plan-Do-Study-Act cycle with examples from various Geriatric ED initiatives. Outlines some essential metrics that are relevant in every Geriatric ED and a process for tracking specific metrics within certain initiatives. Describes ways you can track your data – manual, chart review, data dashboard, electronic health record redesign.