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.

A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks

“…[10][11][12][13][14] This is a well-recognized problem; numerous efforts have been made to establish techniques to validate this data source. 12,[15][16][17][18][19][20] In a review of 35 empirical studies, Chan, Fowles, and Weiner found a substantial lack of agreement regarding which data quality (DQ) dimensions were important to assess. 12 The authors discovered that, of the included studies, "66 percent assessed accuracy, 57 percent completeness, and 23 percent data comparability."…”

A pragmatic approach for measuring data quality in primary care databases

“…They suggest that, since this framework is data independent, it could be used as a template for measuring quality in other medical registries. More recently Kahn et al [7] emphasise the need for multisite data quality comparisons and propose "a more standardised and comprehensive approach" which incorporates and simplifies similar metrics (based on those suggested by [8] in a pragmatic approach).…”

Methods for examining data quality in healthcare integrated data repositories

“…It is therefore important to assess the quality of data used for these studies for often present data quality (DQ) issues and differentiate between natural and extraneous variations in data 15 . Assessing quality of health data needs to account for semantic and syntactic heterogeneity in health data and the diverse needs of practitioners of DQA.…”

“…Quality Knowledge Repository (QKR): We extracted DQC, their definitions and applicable measures, their relationships, and the computability of DQC in existing DQF (e.g. Kahn 15 , Weiskopf 18 from literature. We identified primitives existing in different DQF to develop a DQ metamodel and implemented it as the QKR.…”