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 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 …

Heart failure (HF) with preserved ejection fraction (HFpEF) represents a prototypical
cardiovascular condition in which machine learning may improve targeted therapies and …

Background Electronic health record (EHR) data, collected primarily for individual patient
care and billing purposes, compiled in health information exchanges (HIEs) may have a …

Introduction Chronic heart failure (CHF) is a major public health concern. From a public
health perspective, the epidemiology of CHF needs to be distinguished from that of its …

Objective The increasing reliance on electronic health records (EHRs) for research and
clinical care necessitates robust methods for assessing data quality and identifying …

A deep integration of routine care and research remains challenging in many respects. We
aimed to show the feasibility of an automated transformation and transfer process feeding …

Background Various data quality issues have prevented healthcare administration data from
being fully utilized when dealing with problems ranging from COVID-19 contact tracing to …

Objective Poor knowledge of the relationships between physical function (PF) in the hospital
and patient outcomes in an inpatient rehabilitation facility (IRF) limits the identification of …

Background: Data that needs to be documented for clinical studies has often been acquired
and documented in clinical routine. Usually this data is manually transferred to Case Report …