A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research

Abstract

Introduction: Answers to clinical and public health research questions increasingly require aggregated data from multiple sites. Data from electronic health records and other clinical sources are useful for such studies, but require stringent quality assessment. Data quality assessment is particularly important in multisite studies to distinguish true variations in care from data quality problems.

Methods: We propose a "fit-for-use" conceptual model for data quality assessment and a process model for planning and conducting single-site and multisite data quality assessments. These approaches are illustrated using examples from prior multisite studies.

Approach: Critical components of multisite data quality assessment include: thoughtful prioritization of variables and data quality dimensions for assessment; development and use of standardized approaches to data quality assessment that can improve data utility over time; iterative cycles of assessment within and between sites; targeting assessment toward data domains known to be vulnerable to quality problems; and detailed documentation of the rationale and outcomes of data quality assessments to inform data users. The assessment process requires constant communication between site-level data providers, data coordinating centers, and principal investigators.

Discussion: A conceptually based and systematically executed approach to data quality assessment is essential to achieve the potential of the electronic revolution in health care. High-quality data allow "learning health care organizations" to analyze and act on their own information, to compare their outcomes to peers, and to address critical scientific questions from the population perspective.

Figure 1

Detailed process model for multisite data quality assessment. Multiple data quality assessment cycles occur at both the single-site and multisite level. Each decision point (diamond) requires an examination of ≥ 1 data quality measures against predefined acceptability criteria. Many actions require continued engagement with sites to distinguish expected from unexpected site-specific data variability. HER indicates electronic health record; N, no; Y, yes.

Figure 2

Variability over time and across sites in number of serum fasting glucose tests performed per 1000 patients.