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To discuss the origins of HL7 and its subsequent impact on interoperability in hospitals.

To describe real-world practices and variation in implementation of the Information Blocking provisions amongst healthcare organizations caring for pediatric patients.

Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic regression models trained in pediatric inpatients, for pediatric inpatient clinical prediction tasks.

This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences.

This article proposes a framework to support the scientific research of standards so that they can be better measured, evaluated, and designed.

Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations, and discusses potential innovations.

This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.

To investigate how information communication technology (ICT) factors relate to the use of telemedicine by older people in Ireland during the pandemic in 2020. Furthermore, the paper tested whether the supply of primary care, measured by General Practitioner's (GP) accessibility, influenced people's telemedicine options.

Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%.