European hospitals will undergo far-reaching changes over the next five years owing to several factors beginning with austerity, according to a presentation at European Health Forum Gastein (EHFG) held last week in Austria.
Austerity budgets following the economic crisis have increased the pressures that already existed due to technology innovations, the aging of the population, and the increase in chronic conditions needing care, said Dr. Eric de Roodenbeke, general director of the International Hospital Federation. Recession has finally reached the hospitals and efforts to mitigate its effects will likely play out in different ways depending on the locale and the type of conditions being treated, he said.
"The shift that is needed is not happening fast enough because the players were not prepared for this fast pace of change in a highly complex system," he said in a statement accompanying his presentation.
De Roodenbeke cited five major trends that will force hospitals to reinvent themselves:
- Focus on the patient as a whole instead of on individual organs
- The multidisciplinary approach to care
- Continuous care and system integration
- The search for interventions that are highly effective but require only minimal resources
The future belongs to greater continuity in healthcare, in collaboration of different specialties, and on the integration of many systems to provide low-cost services, he said.
"The hospitals that make progress with such measures will fare the best in the long run, whereas those whose areas of specialization continue to work on parallel tracks without cooperating will suffer from the changes," he said.










![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






