Background Unexpected variability across healthcare datasets may indicate data quality
issues and thereby affect the credibility of these data for reutilization. No gold-standard …

Objective To assess the variability in data distributions among data sources and over time
through a case study of a large multisite repository as a systematic approach to data quality …

Background To describe an automated method for assessment of the plausibility of
continuous variables collected in the electronic health record (EHR) data for real world …

Objectives To demonstrate how data-driven variability methods can be used to identify
changes in disease recording in two English electronic health records databases between …

Objective Large clinical databases are increasingly used for research and quality
improvement. We describe an approach to data quality assessment from the General …

Introduction The proliferation and use of electronic medical records (EMR) in the clinical
setting now provide a rich source of clinical data that can be leveraged to support research …

Objective To evaluate the effects of Process-Reengineering interventions on the Electronic
Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal …

Methods: We describe a data quality analysis tool (called Achilles Heel) developed by the
Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare …

Background To gain maximum insight from large administrative healthcare datasets it is
important to understand their data quality. Although a gold standard against which to assess …

Background Many research initiatives aim at using data from electronic health records
(EHRs) in observational studies. Participating sites of the German Medical Informatics …