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Using a Data Quality Framework to Clean Data Extracted from the Electronic Health Record: A Case Study.

Oliwier Dziadkowiec¹, Tiffany Callahan², Mustafa Ozkaynak¹

¹University of Colorado, College of Nursing, Anschutz Medical Campus.

EGEMS (Washington, DC)

|July 19, 2016

Summary

This summary is machine-generated.

Applying a data quality (DQ) framework to electronic health record (EHR) data improves analysis accuracy. Cleaning EHR data using this DQ framework yields more reliable statistical parameter estimates for research.

Keywords:

Applied Statistics Data Quality Electronic Health Records Relational Databases

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Area of Science:

Health Informatics
Data Science
Clinical Research

Background:

Electronic Health Records (EHRs) offer vast potential for research.
Inadequate data preparation in EHRs can lead to erroneous research findings.
This impacts clinical decision-making and Comparative Effectiveness Research (CER).

Purpose of the Study:

Assess a data quality (DQ) framework for cleaning EHR data from EPIC databases.
Evaluate the impact of DQ framework cleaning on statistical parameter estimates.
Provide practical SPSS Syntax for applying DQ concepts to EHR data.

Main Methods:

Utilized two large emergency department (ED) datasets from EPIC (adult and pediatric).
Applied a DQ assessment framework with five key concepts to EHR data extracts.
Developed SPSS Syntax for implementing DQ checks and cleaning procedures.

Main Results:

The DQ framework was appropriate for cleaning EHR data from EPIC.
Data cleaned with the DQ framework showed more accurate statistical parameter estimates.
SPSS Syntax was successfully developed to address DQ concepts.

Conclusions:

The DQ framework by Kahn et al. enhances the accuracy of EHR data analysis.
Future work includes developing R functions and an R package for EHR data cleaning and analysis.
Improved data quality in EHRs is crucial for reliable research outcomes.