title: Visualizing Anomalies in Electronic Health Record Data: The Variability Explorer Tool.
abstract: 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.
authors: Institute of Translational Health Sciences, University of Washington, Seattle, WA.
authors: Hossein
authors: H
authors: Estiri
authors: Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA.
authors: Ya-Fen
authors: YF
authors: Chan
authors: Institute of Translational Health Sciences, University of Washington, Seattle, WA ; Department of Family Medicine, University of Washington, Seattle, WA.
authors: Laura-Mae
authors: LM
authors: Baldwin
authors: Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.
authors: Hyunggu
authors: H
authors: Jung
authors: Department of Family Medicine, University of Washington, Seattle, WA.
authors: Allison
authors: A
authors: Cole
authors: Institute of Translational Health Sciences, University of Washington, Seattle, WA ; Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA ; Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.
authors: Kari A
authors: KA
authors: Stephens