Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods

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Abstract

The proliferation of the electronic health record (EHR) has led to increasing interest and opportunities for nurse scientists to use EHR data in a variety of research designs. However, methodological problems pertaining to data quality may arise when EHR data are used for nonclinical purposes. Therefore, this article describes common domains of data quality and approaches for quality appraisal in EHR research. Common data quality domains include data accuracy, completeness, consistency, credibility, and timeliness. Approaches for quality appraisal include data validation with data rules, evaluation and verification of data abstraction methods with statistical measures, data comparisons with manual chart review, management of missing data using statistical methods, and data triangulation between multiple EHR databases. Quality data enhance the validity and reliability of research findings, form the basis for conclusions derived from the data, and are, thus, an integral component in EHR-based study design and implementation.

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Missing data: our view of the state of the art.

Joseph Schafer, John W Graham (2002)

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

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How can I deal with missing data in my study?

D A Bennett (2001)

Missing data in medical research is a common problem that has long been recognised by statisticians and medical researchers alike. In general, if the effect of missing data is not taken into account the results of the statistical analyses will be biased and the amount of variability in the data will not be correctly estimated. There are three main types of missing data pattern: Missing Completely At Random (MCAR), Missing At Random (MAR) and Not Missing At Random (NMAR). The type of missing data that a researcher has in their dataset determines the appropriate method to use in handling the missing data before a formal statistical analysis begins. The aim of this practice note is to describe these patterns of missing data and how they can occur, as well describing the methods of handling them. Simple and more complex methods are described, including the advantages and disadvantages of each method as well as their availability in routine software. It is good practice to perform a sensitivity analysis employing different missing data techniques in order to assess the robustness of the conclusions drawn from each approach.

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Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Nicole Weiskopf, Chunhua Weng (2013)

Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.