Supporting data for "EHRtemporalVariability: delineating temporal dataset shifts in electronic health records"

Temporal variability in healthcare processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal dataset shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multi-modal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large historical data from EHRs, there is a need for specific software methods to help delineate temporal dataset shifts to ensure reliable data reuse.<br>EHRtemporalVariability is an Open Source R-package and Shiny-app designed to explore and identify temporal dataset shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal-evolution through non-parametric Information Geometric Temporal plots; and enables the exploration of changes in variables through Data Temporal Heatmaps. We demonstrate the capability of EHRtemporalVariability to delineate dataset shifts in three impact case studies, one of them available for reproducibility.<br>EHRtemporalVariability enables the exploration and identification of dataset shifts, contributing to the broad examination and repurposing of large, longitudinal datasets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R-package, as well as users who are not familiar with programming via the Shiny user interface.