Objective We extended a 2013 literature review on electronic health record (EHR) data
quality assessment approaches and tools to determine recent improvements or changes in …

Objective To synthesize data quality (DQ) dimensions and assessment methods of real-
world data, especially electronic health records, through a systematic scoping review and to …

Background No standards exist for the handling and reporting of data quality in health
research. This work introduces a data quality framework for observational health research …

Background The promise of digital health is principally dependent on the ability to
electronically capture data that can be analyzed to improve decision-making. However, the …

With the recent advancement of novel biomedical technologies such as high-throughput
sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics …

PURPOSE Image analysis is one of the most promising applications of artificial intelligence
(AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases …

Oncology drug development increasingly relies on single‐arm clinical trials. External
controls (EC s) derived from electronic health record (EHR) databases may provide …

Introduction In the health care environment, a huge volume of data is produced on a daily
basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health …

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 …

Data quality (DQ) is the degree to which a given dataset meets a user's requirements. In the
primary healthcare setting, poor quality data can lead to poor patient care, negatively affect …