Research Group for Optimization and Management of Medical Operations
Optimizing urgent surgical care pathways through health data by combining medical sciences, care pathway engineering, health economics, and the social and human sciences.
Learn moreANR DOSE
A multidisciplinary data-driven approach to optimize the care pathways of patients undergoing emergency surgery, both at the patient level and across an entire hospital center.
PEPR NETSURG
A cooperative digital twin of a hospital network designed to improve the management of urgent surgical care pathways at the patient, hospital, and regional levels.
GRO2M
GRO2M develops innovative approaches to improve the management of patients undergoing emergency surgery, from hospital admission to discharge and return home. Our work relies on healthcare data from health data warehouses to model and optimise perioperative surgical care pathways. The outcome of our research is the development of a digital twin of urgent surgical care pathways, implementable in healthcare departments to support medical decision-making and the rational use of resources.
Our Rationale
Hospital systems face growing pressure from rising emergency care demands, scarce medical and paramedical resources, and mounting financial constraints. Unplanned surgical pathways represent a major challenge in quality, safety, and efficiency — prolonged access delays to the operating theatre are directly linked to increased morbidity, mortality, and length of hospital stay.
Yet the organisation of these pathways remains largely based on empirical regulation, with little support from robust predictive tools. Advances in hospital data warehouses, national health databases, and AI methods now make it possible to model care trajectories dynamically — enabling better understanding of organisational and clinical delays, and supporting optimised prioritisation and resource allocation strategies.
It is within this interdisciplinary ambition that GRO2M was founded — to produce scientific knowledge and concrete solutions that sustainably transform the organisation of medical operations, serving patients, healthcare professionals, and the long-term sustainability of health systems alike.
What We Aim to Achieve
Translational Research
Bridge the gap between fundamental science and clinical practice through close collaboration between research laboratories and hospital departments.
AI for Healthcare
Develop intelligent machine-learning tools trained on real hospital data to support clinical decision-making, predict care delays, and improve patient outcomes in emergency settings.
Process Optimisation
Optimise surgical workflows, operating theatre scheduling, and hospital resource allocation using operations research methods and data-driven prioritisation strategies.
Digital Twins
Build virtual replicas of hospital care pathways to simulate saturation scenarios, anticipate bottlenecks, and support real-time decision-making at both institutional and regional levels.
Our Institutions
GRO2M brings together medical, scientific, and engineering teams from leading French institutions around a shared objective: improving the organisation of emergency surgical care.
GRO2M Members
GRO2M brings together surgeons, engineers, data scientists, and clinicians around a shared ambition: making surgery smarter, safer, and more precise.
Institutional Support
Our research is made possible through funding from major French and European research agencies.
Our Contributions
GRO2M contributes to structuring national research on the optimization of urgent surgical care pathways. Its work focuses in particular on the definition of quality indicators, pathway modeling, the development of prioritization methods, medico-economic analysis, quality of work life, and the design of hospital digital twins.
News & events
Find here the group’s main scientific, institutional, and operational updates: funding awards, regulatory advances, publications, conference presentations, recruitment, thesis defenses, and key milestones of the DOSE and NETSURG projects.
DOSE – A multidisciplinary Data-driven approach for care pathway Optimization of non-elective SurgEry patients
DOSE aims to develop a multidisciplinary data-driven approach to optimize the care pathways of patients undergoing emergency surgery at the scale of a hospital center. The project relies on data from the AP-HP Health Data Warehouse and the French National Health Data System (SNDS) to better understand the determinants of care pathways, identify relevant quality indicators, and develop decision-support tools for the organization of emergency operating rooms.
Scientific Structure
Scientific Progress
The DOSE project is funded for a scientific duration of 48 months, with a planned start in April 2025 and completion in 2029.
The initial phases focus on data structuring, data quality improvement, linkage with the French National Health Data System (SNDS), and the development of the first care pathway indicators.
NETSURG — A Cooperative Hospital Network Digital Twin for the Management of Non-Elective Surgery Care Pathways
NETSURG aims to develop a multi-scale digital twin to optimize urgent surgical care pathways within hospital networks. The project models care pathways at three levels: the patient, the hospital, and the regional hospital network. It integrates clinical, medico-economic, social, patient-centered, and healthcare professional-centered criteria. The project relies on data from the AP-HP, Nantes, and Lille health data warehouses.
Scientific Structure
Scientific Progress
NETSURG builds on the preliminary results of the DOSE project. AP-HP data have already been structured for an initial large cohort, with planned expansion to Nantes and Lille. The project targets approximately 340,000 patients from major French hospital data warehouses. The official launch of the project is scheduled for May 28, 2026.
NETSURG aims to develop a multi-scale digital twin to optimize urgent surgical care pathways within hospital networks. The project models care pathways at three levels: the patient, the hospital, and the regional hospital network. It integrates clinical, medico-economic, social, patient-centered, and healthcare professional-centered criteria. The project relies on data from the AP-HP, Nantes, and Lille health data warehouses.
PhD & Masters Theses
Contact us
Our team is open to academic, hospital, institutional, and methodological collaborations in the fields of care pathways, perioperative medicine, health data, hospital engineering, health economics, and the social and human sciences.
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