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 (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
The European Joint Programme on Rare Diseases (EJP RD) is a programme aiming to create an effective rare diseases research ecosystem for progress, innovation and for the benefit of everyone with a rare disease. We support rare diseases stakeholders by funding research, bringing together data resources & tools, providing dedicated training courses, and translating high quality research into effective treatments.
Research line: Reusable health data
Staff: Nirupama Benis, Ronald Cornet, Shuxin Zhang
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
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
PaLaDIN aims to accelerate the development of effective treatments and to establish best-practice diagnosis and care for neuromuscular disease patients worldwide by harnessing patient input and patient-generated health data to improve decision making, outcomes and healthcare solutions. To this end, PaLaDIn will develop and operate a collaborative, inclusive system that collects patient-reported outcome and experience measures, and aligns these with clinical data to advance treatments, diagnosis, and care by providing holistic, patient-level data.
Research line: Reusable health data
Staff: Nirupama Benis, Ronald Cornet, Martijn Kersloot
Staff involved
