Abstract Information systems such as Electronic Health Record (EHR) systems are
susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is …

Highlights•FAIRification use cases at Roche and AstraZeneca.•More prospective
FAIRification expected in future.•Broad applicability of data quality assessment …

Even though data preparation constitutes a large proportion of the total effort involved in
electronic health record (EHR) based secondary analysis, guidelines for EHR data …

Introduction Secondary use of electronic health record (EHR) data for research requires that
the data are fit for use. Data quality (DQ) frameworks have traditionally focused on structural …

Background: Electronic health record (EHR) data have many quality problems that may
affect the outcome of research results and decision support systems. Many methods have …

Defining and evaluating data quality can be a complex task as it varies depending on the
specific purpose for which the data is intended. To effectively assess data quality, it is …

Background: Despite the many opportunities data reuse offers, its implementation presents
many difficulties, and raw data cannot be reused directly. Information is not always directly …

Objectives The value of healthcare data is being increasingly recognised, including the
need to improve health dataset utility. There is no established mechanism for evaluating …

Individual researchers and research networks have developed and applied different
approaches to assess the data quality of electronic health record (EHR) data. A previously …

Healthcare data has economic value and is evaluated as such. Therefore, it attracted global
attention from observational and clinical studies alike. Recently, the importance of data …