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. 2015 Mar 25;2015:56-60.
eCollection 2015.

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

Free PMC article

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

Hossein Estiri et al. AMIA Jt Summits Transl Sci Proc. .
Free PMC article


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.


Figure 1:
Figure 1:
Outcome of the Variability Explorer Tool on the full cluster of depression ICD-9 codes
Figure 2:
Figure 2:
Variability in depression data using ICD-9 code 296.3×
Figure 3:
Figure 3:
Variability in depression dato using ICD-9 code 311

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