DQe-v: A Database-Agnostic Framework for Exploring Variability in Electronic Health Record Data Across Time and Site Location

Abstract

Data variability is a commonly observed phenomenon in Electronic Health Records (EHR) data networks. A common question asked in scientific investigations of EHR data is whether the cross-site and -time variability reflects an underlying data quality error at one or more contributing sites versus actual differences driven by various idiosyncrasies in the healthcare settings. Although research analysts and data scientists have commonly used various statistical methods to detect and account for variability in analytic datasets, self service tools to facilitate exploring cross-organizational variability in EHR data warehouses are lacking and could benefit from meaningful data visualizations. DQ^e-v, an interactive, database-agnostic tool for visually exploring variability in EHR data provides such a solution. DQ^e-v is built on an open source platform, R statistical software, with annotated scripts and a readme document that makes it fully reproducible. To illustrate and describe functionality of DQ^e-v, we describe the DQ^e-v's readme document which includes a complete guide to installation, running the program, and interpretation of the outputs. We also provide annotated R scripts and an example dataset as supplemental materials. DQ^e-v offers a self service tool to visually explore data variability within EHR datasets irrespective of the data model. GitHub and CIELO offer hosting and distribution of the tool and can facilitate collaboration across any interested community of users as we target improving usability, efficiency, and interoperability.

Keywords: Data Quality; Data Variability; Data Warehouse; Electronic Health Records.

Figure 1

Workflow for DQ^e-v

Figure 2

The Variability Preview Tab Previewing Diagnoses Per Visit Per Year

Figure 3

The Exploratory Analysis Tab’s Plots Help Identify Years and Site Locations with High Variability in the Number of Diagnoses Per Visit Note: The yellow box highlights a period with relative high variability.

Figure 4

Visualization of Probability Density Functions in Density Plots Tab for Number of Creatinine Labs Per Patients with a CKD Diagnosis and 1+ Visits Note: Colors signify time units.

Figure 5

Outputs of the Regression-Based Analysis on Hemoglobin A1c Labs Per Patient with a Diabetes Diagnosis and 1+ Visits. (Site-location Names Are Obstructed.)