Defining and measuring completeness of electronic health records for secondary use

TLDR

EHR data are widely used for secondary research, yet their completeness remains uncertain, affecting study validity. The study shows that explicit definitions of EHR completeness are crucial because varying conceptualizations can alter research outcomes. The authors defined four prototypical completeness concepts—documentation, breadth, density, and predictive—each requiring distinct measurement approaches, and applied these metrics to data from NewYork‑Presbyterian Hospital’s clinical data warehouse. They found that the proportion of records deemed complete is far lower than the nominal total and varies markedly with the chosen definition, producing different record subsets, underscoring that completeness is contextual and that users must clearly define and disclose their criteria.

Abstract

We demonstrate the importance of explicit definitions of electronic health record (EHR) data completeness and how different conceptualizations of completeness may impact findings from EHR-derived datasets. This study has important repercussions for researchers and clinicians engaged in the secondary use of EHR data. We describe four prototypical definitions of EHR completeness: documentation, breadth, density, and predictive completeness. Each definition dictates a different approach to the measurement of completeness. These measures were applied to representative data from NewYork-Presbyterian Hospital's clinical data warehouse. We found that according to any definition, the number of complete records in our clinical database is far lower than the nominal total. The proportion that meets criteria for completeness is heavily dependent on the definition of completeness used, and the different definitions generate different subsets of records. We conclude that the concept of completeness in EHR is contextual. We urge data consumers to be explicit in how they define a complete record and transparent about the limitations of their data.