The Italian “Istituto Superiore di Sanità” (ISS) identifies hospital-acquired infections (HAIs) as the most frequent and serious complications in healthcare. HAIs constitute a real health emergency and, therefore, require decisive action from both local and national health organizations. Information about the causative microorganisms of HAIs is obtained from the results of microbiological cultures of specimens collected from infected body sites, but microorganisms’ names are sometimes reported only in the notes field of the culture reports. The objective of our work was to build a NLP-based pipeline for the automatic information extraction from the notes of microbiological culture reports. We analyzed a sample composed of 499 texts of notes extracted from 1 month of anonymized laboratory referral. First, our system filtered texts in order to remove nonmeaningful sentences. Thereafter, it correctly extracted all the microorganisms’ names according to the expert’s labels and linked them to a set of very important metadata such as the translations into national/international vocabularies and standard definitions. As the major result of our pipeline, the system extracts a complete picture of the microorganism.

Innovation in cancer therapy has increased childhood cancer survival rates. However, survivors are still at risk of developing late effects. In the digital transformation of the health sector, the Survivorship Passport (SurPass) can support long-term follow-up care plans. Gaps in seamless connectivity among hospital departments, primary care, combined with the time of health professionals required to collect and fill-in health data in SurPass, are barriers to its adoption in daily clinical practice. The PanCareSurPass (PCSP) project was motivated to address these gaps by a new version of SurPass (v2.0) that supports semi-automatic assembly from organizational Electronic Health Record (EHR) systems of the treatment summary data using HL7 FHIR, to create SurPass, and to link it to regional or national digital health infrastructures in six European countries. In this paper we present the methodology used to develop the SurPass technical implementation strategy with special focus on the European Health Data Space (EHDS). The recently provisionally approved EHDS regulation instruments a digital health data ecosystem with opportunities for cost-effective SurPass implementation across Europe. Moving forward, a European HL7 FHIR SurPass Implementation Guide along with synthetic data sets, and validation tools can enrich the European Electronic Health Record Exchange Format (EEHRxF) with use cases on health & wellness of childhood cancer survivors.

Telemedicine can provide benefits in patient affected by chronic diseases or elderly citizens as part of standard routine care supported by digital health. The GATEKEEPER (GK) Project was financed to create a vendor independent platform to be adopted in medical practice and to demonstrate its effect, benefit value, and scalability in 8 connected medical use cases with some independent pilots. This paper, after a description of the GK platform architecture, is focused on the creation of a FHIR (Fast Healthcare Interoperability Resource) IG (Implementation Guide) and its adoption in specific use cases. The final aim is to combine conventional data, collected in the hospital, with unconventional data, coming from wearable devices, to exploit artificial intelligence (AI) models designed to evaluate the effectiveness of a new parsimonious risk prediction model for Type 2 diabetes (T2D).

The Italian Digital Health Solutions in Community Medicine (DHEAL-COM) project aims to develop digital technologies to improve community medicine. This study focuses on type 2 Diabetes Mellitus (T2DM), identifying and structuring key data requirements to improve standardization and interoperability. Four main data domains, Patient Details, Medical History, Lifestyle, and Health Monitoring, were defined to group essential information for comprehensive diabetes care. Following recent Italian healthcare regulations, the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) release 4 (r4) was used to map these requirements. FHIR r4 resources such as Patient, Observation, and Condition were employed to represent the requirements, and LOINC codes were integrated to improve the mapping quality. This approach promotes interoperability and enables smooth integration of data across health systems. Future steps include developing an Implementation Guide (IG) and extending this methodology to other chronic conditions under investigation within the DHEAL-COM project.

The GATEKEEPER (GK) Project was financed by the European Commission to develop a platform and marketplace to share and match ideas, technologies, user needs and processes to ensure a healthier independent life for the aging population connecting all the actors involved in the care circle. In this paper, the GK platform architecture is presented focusing on the role of HL7 FHIR to provide a shared logical data model to be explored in heterogeneous daily living environments. GK pilots are used to illustrate the impact of the approach, benefit value, and scalability, suggesting ways to further accelerate progress.

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service—Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer’s Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.