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 Data quality (DQ) must be consistently defined in context. The attributes, metadata,
and context of longitudinal real-world data (RWD) have not been formalized for quality …

Today, data availability has gone from scarce to superabundant. Technologies like IoT,
trends in social media and the capabilities of smart-phones are producing and digitizing lots …

Purpose To explore the impacts that structuring of electronic health records (EHRs) has had
from the perspective of secondary use of patient data as reflected in currently published …

Background Traditional statistical approaches to prediction of outcomes have drawbacks
when applied to large clinical databases. It is hypothesized that machine learning …

Background Routine Data Quality Assessments (RDQAs) were developed to measure and
improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs …

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 …

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 …

Objective The aim of this study is to guide healthcare instances in applying process analytics
on healthcare processes. Process analytics techniques can offer new insights in patient …

Background Documenting medical and lifestyle preconception health risk factors in
electronic medical records (EMRs) could assist general practitioners (GPs) to identify those …