With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

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 …

While technological innovations are the invariable crux of speculation about the future of
critical care, they cannot replace the clinician at the bedside. This article summarizes the …

Abstract The European Union (EU) initiative on the Digital Transformation of Health and
Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and …

Objective Patients who receive most care within a single healthcare system (colloquially
called a “loyalty cohort” since they typically return to the same providers) have mostly …

Background Care plans documented by nurses in electronic health records (EHR) are a rich
source of data to generate knowledge and measure the impact of nursing care …

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 …

Abstract Objective Analysis of healthcare Real-World Data (RWD) provides an opportunity to
observe actual patient diagnostic, treatment and outcomes events. However, researchers …

The purpose of this article is to illustrate an investigation of methods that can be effectively
used to predict the data incompleteness of a dataset. Here, the investigators have …

The purpose of this article is to propose a methodology involving various methods that can
be used to predict the data incompleteness of a dataset. Here the investigators have …