Visualizing Anomalies in Electronic Health Record Data: The Variability Explorer Tool.


Hossein Estiri, Ya-Fen Chan, Laura-Mae Baldwin, Hyunggu Jung, Allison Cole, and Kari A Stephens. 2015. “Visualizing Anomalies in Electronic Health Record Data: The Variability Explorer Tool.” AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science, 2015, Pp. 56–60.


As Electronic Health Record (EHR) systems are becoming more prevalent in the U.S. health care domain, the utility of EHR data in translational research and clinical decision-making gains prominence. Leveraging primay· care-based. multi-clinic EHR data, this paper introduces a web-based visualization tool, the Variability Explorer Tool (VET), to assist researchers with profiling variability among diagnosis codes. VET applies a simple statistical method to approximate probability distribution functions for the prevalence of any given diagnosis codes to visualize between-clinic and across-year variability. In a depression diagnoses use case, VET outputs demonstrated substantial variability in code use. Even though data quality research often characterizes variability as an indicator for data quality, variability can also reflect real characteristics of data, such as practice-level, and patient-level issues. Researchers benefit from recognizing variability in early stages of research to improve their research design and ensure validity and generalizability of research findings.