This project aims to improve care for people with type 2 diabetes by developing and testing a hybrid care pathway that combines traditional follow‑up with digital support, data sharing, and remote monitoring. Building on existing care structures, the project introduces connected measurement devices and a smartphone app that allow patients to monitor their health values themselves.
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The BE Patient Problemlist project aims to pave the way for a hospital‑wide, clinically usable SNOMED CT‑coded problem list that can be exchanged at the eHealth level via FHIR.
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The project addresses the critical challenges of hospital staffing and bed capacity management by enabling hospitals to monitor patient‑to‑nurse ratios, care intensity, and ward capacity in a transparent and standardised way. Using shared FHIR‑based data elements on patient profiles, staffing levels, competencies, and operational bed availability, hospitals can build dashboards that support real‑time follow‑up, benchmarking, and data‑driven adjustments to staffing in response to demand.
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The Datacapabilities MOB‑PSY BXL project aims primarily to transform the Belgian healthcare landscape by developing an integrated EHR platform based on FHIR standards. Its central objective is to ensure seamless interoperability between various institutions, such as hospitals and mobile psychiatric teams, by enabling secure and standardised sharing of health data.
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This project aims to transform geriatric patient care by leveraging innovative technologies and recognized standards: Data Standardization: Use of FHIR and SNOMED CT standards to harmonize care pathways and enhance interoperability across systems.
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SPECTRE-HD (Secure Processing ECosystem for Trusted REuse - Health Data) is one of the data capability initiatives and delivers a scalable, FAIR-compliant infrastructure for secondary use of health data and addresses the fundamental challenge that hospitals face in meeting EHDS regulatory requirements: how to make health data findable, accessible, interoperable, and reusable while maintaining the highest standards of privacy protection, security, and institutional data sovereignty.
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The objective of Mentalink is to fully centralize the mental‑health care demand in Belgium through an intuitive digital platform, ensuring that service users only need to tell their story once. This structured dossier is then automatically matched with the most appropriate care offering.
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The project's aim is to improve healthcare for children and young people (CYP) with non-communicable diseases (NCDs) demands innovative, integrated care models. As such, we aim to develop a next-generation integrated care trajectory to improve the outcome and enhance the cost-effectiveness of
moderate to severe asthma in children and adolescents with maintenance and reliever therapy (MART) with or without comorbidities, called ‘Next Generation Pediatric Asthma Patient Journey’.
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The project aims to implement a longitudinal monitoring tool (moveUP) and assess its usability for patients with chronic respiratory disorders (e.g. COPD, asthma, interstitial lung diseases). This tool enables decentralized patient follow-up and a streamlined integration of patient needs into their care pathways
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The project aims to modernize and strengthen how hospitals analyse and validate medical prescriptions. Using an innovative software platform (PharmIA), the project introduces AI‑supported decision‑making, a rule‑editing module, and a shared inter‑hospital library of expert‑validated rules.
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The SEPSIS‑Connect project aimed to enable the early detection and monitoring of sepsis in at‑risk patients through the use of innovative technologies.
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The Telemonitoring Prescription project (TMP) addresses the lack of standardized telemonitoring integration in Belgium’s healthcare system. It aims to create a scalable, interoperable framework that connects hospitals and telemonitoring providers via a unified data integration gateway.
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This project aims to support an amount of psychiatric hospitals of in their joint transformation towards fast and simple exchange of patient data from their EHRs using the FHIR standard and SNOMED CT–coded datasets.
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The objective of the DPIforINAH project is to take the INAH project to the next level by implementing a hospital EHR data extractor, based on natural language recognition, to feed the INAH data warehouse in OMOP format.
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The project aims to improve the information exchange between general practitioners and hospitals by moving from unstructured free‑text referral letters and result reports to structured, coded clinical communication.
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