Reusable health data

Reusable health data

Data saves lives. In this research line, we strive to make health data Findable, Accessible, Interoperable and Reusable (FAIR). We develop and apply methods and policies regarding the input, storage and utilization of health data and knowledge. Additionally, we make agreements for when the data is understandable. One of our focus areas is the care for individuals with rare diseases.

Projects

C4C

c4c (conect4children) is a large collaborative European network that aims to facilitate the development of new drugs and other therapies for the entire paediatric population. It is a pioneering opportunity to build capacity for the implementation of multinational paediatric clinical trials whilst ensuring the needs of babies, children, young people and their families are met.

c4c is committed to meeting the needs of paediatric patients thanks to a novel collaboration between the academic and the private sectors, which includes 35 academic and 10 industry partners and around 500 affiliated partners. c4c endeavours to provide a sustainable, integrated platform for the efficient and swift delivery of high quality clinical trials in children and young people across all conditions and phases of the drug development process. The project strives to bring innovative processes to all stages of clinical development by generating a new model of organization and of the clinical development process.

Research line: Reusable health data

Staff: Nirupama Benis, Ronald Cornet

Website: conect4children

ERDERA

Today, around 7000 rare diseases affect more that 300 million people worldwide. The majority of rare and ultra rare diseases still lack a therapeutic option. To address these important issues, the European Rare Diseases Research Alliance (ERDERA) has been set up to build on the advancements made by former EU-funded projects.

ERDERA will continue developing a robust and comprehensive data and expertise infrastructure and innovative clinical research services -with a focus on advanced therapies-, funding new research projects, providing training and expediting translation of findings into tangible solutions for patients.

Research line: Reusable Health Data

Staff: Nirupama BenisRonald Cornet, Andra Waagmeester, Noah van Brummelen

Website: ERDERA - European Rare Diseases Research Alliance

HemaFAIR

The HemaFAIR project, established through international expert collaborations, aims to enhance the scientific profile of The Cyprus Institute of Neurology and Genetics (CING) and improve the position of Cypriot researchers in biomedical informatics and rare hematological diseases with a focus on the use of FAIR data and standards in research and education.

The project will involve a comprehensive training programme and capacity-building and networking activities such as summer schools, workshops, webinar and transferable skill lectures taught by experts in rare hematological diseases, biomedical informatics, ethics and regulatory issues. In addition, HemaFAIR will raise the profile of early-career researchers via the development of a mentoring programme, short term staff exchanges and collaborative research projects between EU partners and the CING.

Through a pilot study, it will demonstrate the FAIRification process on two platforms for haemoglobinopathies—rare hematological diseases particularly prevalent in Cyprus—maximizing the project's impact on the local community.

Research line: Reusable health data

Staff: Ronald Cornet, Martijn Kersloot

Website: HemaFAIR

NICE FAIR

This project studies the various ways to transform the NICE ICU quality registration data structure in order to lower the registration burden on intensivists, to increase data collection from monthly to daily, to make the NICE data reusable for researchers, and to facilitate federated analysis without having to share data directly. Various standardization solutions are explored, such as the data structure standard OMOP CDM and the data exchange standard HL7 FHIR. These are then evaluated in collaboration with international organizations such as European Health Data & Evidence Network (EHDEN), and the Critical Care Asia Africa (CCAA) registry network. By making following the Findable, Accessible, Interoperable and Reusable (FAIR) principles we hope to make the NICE data easier to share, use, and reuse.

Research lines: Quality of Care (IT Systems) & Reusable Health Care

Staff: Daniel Püttmann, Nicolette de Keizer, Ferishta Raiez, Ronald Cornet

NICE Federated Learning

Similar to the NICE FAIR project, in this project we test the feasibility of Federated Learning (FL), a decentralized machine learning technique. It is a potential solution to the question: "How can we analyze health data without needing to gather that data centrally?".

We will test FL in the context of NICE’s benchmarking activities, which compares mortality rates of all Dutch ICUs based on the APACHE IV case-mix adjustment. We will examine the performance of NICE's yearly APACHE IV recalibration when calculated centralized (current practice) versus decentralized (FL) and investigate its effect on the position of ICUs in funnel plots. This decentralization is recreated virtually, by creating partitions of the NICE's database, simulating separate hospital databases.

This project should result in a first assessment of the use of FL as a decentralized data analysis method for the NICE, opening the door for further research and benchmark opportunities.

Research lines: Quality of Care (IT Systems) Reusable Health Care

Staff: Daniel Püttmann,  Ferishta Raiez, Sebastian van der Voort,  Ronald Cornet, Nicolette de Keizer

Staff involved