A hackathon as a tool to enhance research and practice on electronic health record systems' interoperability for chronic disease management and prevention.

Objectives: The development of a standardized technical framework for exchanging electronic health records is widely recognized as a challenging endeavor that necessitates appropriate technological, semantic, organizational, and legal interventions to support the continuity of health and care. In this context, this study delineates a pan-European hackathon aimed at evaluating the efforts undertaken by member states of the European Union to develop a European electronic health record exchange format. This format is intended to facilitate secure cross-border healthcare and optimize service delivery to citizens, paving the way toward a unified European health data space. Methods: The hackathon was conducted within the scope of the X-eHealth project. Interested parties were initially presented with a representative clinical scenario and a set of specifications pertaining to the European electronic health record exchange format, encompassing Laboratory Results Reports, Medical Imaging and Reports, and Hospital Discharge Reports. In addition, five onboarding webinars and two professional training events were organized to support the participating entities. To ensure a minimum acceptable quality threshold, a set of inclusion criteria for participants was outlined for the interested teams. Results: Eight teams participated in the hackathon, showcasing state-of-the-art applications. These teams utilized technologies such as Health Level Seven-Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Document Architecture (CDA), alongside pertinent IHE integration profiles. They demonstrated a range of complementary uses and practices, contributing substantial inputs toward the development of future-proof electronic health record management systems. Conclusions: The execution of the hackathon demonstrated the efficacy of such approaches in uniting teams from diverse backgrounds to develop state-of-the-art applications. The outcomes produced by the event serve as proof-of-concept demonstrators for managing and preventing chronic diseases, delivering value to citizens, companies, and the research community.

Towards an Explainable AI-Based Tool to Predict Preterm Birth.

Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women' objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision-recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.

Can Artificial Intelligence Enable the Transition to Electric Ambulances?

The electrification of the transportation sector is seen as a main pathway to reduce CO2 emissions and mitigate the earth's climate change. Currently, Electric Vehicles (EVs) are entering the market fast. Although EVs have not been used as ambulances yet, the transition to the new type of vehicle is a matter of time. Thus, in this paper we discuss a number of research questions related to the efficient deployment of electric ambulances, focusing on the Artificial Intelligence (AI) point of view and we propose a framework for developing online algorithms that schedule the charging of electric ambulances and their assignment to patients.