TY - JOUR
T1 - Call to collaborate on data science for older people across Europe
T2 - an EuGMS Big Data Special Interest Group position paper
AU - EuGMS Big Data Special Interest Group
AU - Mitchell, Anneka
AU - Ogliari, Giulia
AU - Burton, Jennifer Kirsty
AU - Clegg, Andrew
AU - Todd, Oliver
AU - Lietzen, Lone Winther
AU - Wilkinson, Chris
AU - Walesby, Katherine E.
AU - Vincenzo, Gianturco
AU - Vardy, Emma R.L.C.
AU - van Oppen, James D.
AU - Soraci, Luca
AU - Skrypchenko, Nataliya
AU - Sokhal, Balamrit Singh
AU - Ryg, Jesper
AU - Richardson, Sarah Joanna
AU - Ornago, Alice Margherita
AU - Nishtala, Prasad S.
AU - Myint, Phyo Kyaw
AU - Masoli, Jane
AU - Knight, Matthew
AU - Keevil, Victoria L.
AU - Hjelholt, Thomas Johannesson
AU - Harada, Saori
AU - Gąsowski, Jerzy
AU - Eriksen, Alexander Viktor
AU - Collins, Jemima
AU - Cherubini, Antonio
AU - Bunn, Jonathan
AU - Bhanu, Cini
AU - Bellelli, Giuseppe
AU - Achterberg, Wilco P.
PY - 2025/10/31
Y1 - 2025/10/31
N2 - Data science and AI may improve outcomes for older people through enhanced prognostication, clinical trial design, and service evaluation using routinely collected electronic health data. Ageing research data science requires collaboration across the boundaries of multiple disciplines. A pan-European approach could examine variation in outcomes among older people, test common clinical decision aids and prediction tools, and evaluate different services and interventions for older people in a variety of contexts. Standardisation and shared learning across Europe are important to identify and share best practice for older people and advocate for this standard of care across the continent with the aim of improving equality and equity of health services. The EuGMS Big Data Special Interest Group aims to deliver harmonised, pan-European ageing research data science to transform health and care services for older people.
AB - Data science and AI may improve outcomes for older people through enhanced prognostication, clinical trial design, and service evaluation using routinely collected electronic health data. Ageing research data science requires collaboration across the boundaries of multiple disciplines. A pan-European approach could examine variation in outcomes among older people, test common clinical decision aids and prediction tools, and evaluate different services and interventions for older people in a variety of contexts. Standardisation and shared learning across Europe are important to identify and share best practice for older people and advocate for this standard of care across the continent with the aim of improving equality and equity of health services. The EuGMS Big Data Special Interest Group aims to deliver harmonised, pan-European ageing research data science to transform health and care services for older people.
UR - https://www.scopus.com/pages/publications/105018099976
U2 - 10.1007/s41999-025-01276-y
DO - 10.1007/s41999-025-01276-y
M3 - Editorial
C2 - 40968246
SN - 1878-7649
VL - 16
SP - 1561
EP - 1565
JO - European Geriatric Medicine
JF - European Geriatric Medicine
IS - 5
ER -