Department / Institute: Emergency Department

The AKTIN-Notaufnahmeregister is a standardized electronic infrastructure that makes electronically collected data from emergency admissions available for health reporting, quality assurance, and health services research. The special features of the registry are the use of routine data without additional effort for the treating staff and the decentralized infrastructure, which allows the data to be stored in the individual clinics and thus in the context of the treatment.

The aim of the AKTIN-Notaufnahmeregister is to optimize quality management in emergency departments and to improve/accelerate data availability for health reporting and health services research in acute and emergency medicine.

As part of the NUM 3.0 research project (infrastructure line) the ongoing operation of the network is funded at the participating locations.

More information can be found on the website: 

https://aktin.org/

Department / Institute: PLRI for Medical Informatics

Medical Data Integration Centers (MeDICs) have been established at all university hospitals across Germany. The primary goal of the MeDICs is to consolidate data from numerous diverse systems—such as physician letters, laboratory results, X-ray images, genomic sequencing data, and more—into a unified, structured data repository at each site. Access to patient data requires explicit informed consent from individuals.

The established MeDICs have harmonized their IT infrastructures, services, processes, governance frameworks, and oversight bodies in alignment with the recommendations and guidelines of the Medical Informatics Initiative (MII), ensuring interoperability with the national infrastructure. This is demonstrated by their ability to connect to the German Research Data Portal for Health (FDPG), enabling nationwide feasibility assessments and data access requests.

The future objective is to establish a platform for "Pandemic Preparedness." Additionally, the centers are continuously integrating further data systems and expanding data access for researchers.

More information can be found on the website: 

https://www.netzwerk-universitaetsmedizin.de/en/platforms/num-diz

Department / Institute: Hannover Unified Biobank

The NUM-MB will provide all researchers and infrastructures in the NUM with comprehensive, easily accessible and needs-based support on methodology, biospecimens and specific aspects of regulation throughout the entire course of the study.

Starting with (I) study planning, the NUM-MB offers methodological advice on data and biosamples, including their preparation and utilisation. We also advise on the presentation of data protection and infrastructural aspects as well as NUM governance in patient records in the respective regulatory context.

As part of (II) study implementation , the NUM-MB ensures rapid support from a central helpdesk. In addition, training and counselling is offered, e.g. on amendments and training on SOPs. The BCU managers also carry out quality assurance audits at the NUM sites that collect biosamples.

As a member of the (III) (dynamic) Use and Access Committee (UAC), NUM-MB supports the release process, e.g. of biosamples, and advises applicants on the UAC.

More information can be found on the website:

https://www.netzwerk-universitaetsmedizin.de/en/platforms/num-mb

Department / Institute: Institute of Medical Microbiology and Hospital Epidemiology

The goal of NUM-SAR is to structurally secure the capacities of university hospitals and make them more readily available to the public health system and the Robert Koch Institute (RKI). NUM-SAR is an efficient network of expert laboratories for various pathogen and outbreak situations, complementing the laboratories of the public health service.

The sub-project MuSE is a university hospital monitoring infrastructure for collecting and evaluating parameters related to care, quality and infection. MuSE also provides this data to the Robert Koch Institute as a sentinel surveillance system. The data includes infection medicine parameters related to hospital-acquired infection epidemiology, care and patient safety, staff health and workload, as well as inpatient care quality and resources. The definition and implementation of suitable adaptive parameters, their scientific application and coordinated processing are realized within MuSE.

Department / Institute: Institute of Diagnostic and Interventional Radiology

RACOON is a nationwide research infrastructure within the Network University Medicine (NUM). RACOON was established during the COVID-19 pandemic in the first NUM funding phase to systematically collect radiological imaging data from German university hospitals and make them available for research projects. Centrally managed imaging data can be analyzed with regard to a wide range of research questions, both statistically and using artificial intelligence. 

The third NUM funding phase enables further development and optimization of RACOON based on experiences from the first funding phases, like the RACOON-RESCUE project, while simultaneously laying foundation for additional cross-university, interdisciplinary research projects. With currently around 600 staff members from 38 university hospitals, RACOON shows steady growth. In addition, German Cancer Research Center (DKFZ), Fraunhofer Institute for Digital Medicine (MEVIS), Mint Medical GmbH, ImFusion GmbH, and Institute for Artificial Intelligence in Medicine (IKIM) in Essen are permanent research and development partners in RACOON.

More information can be found on the website:

https://racoon.network/

Department / Institute: Institute of Diagnostic and Interventional Radiology

Hepatocellular carcinoma (HCC), a common form of liver cancer, is often not detected until it has reached an advanced stage. Currently, early detection in high-risk patients is usually performed using ultrasound examinations and blood tests, which are widely available but offer only limited accuracy. Magnetic resonance imaging (MRI) can detect tumors with greater precision, but is more complex and has therefore not yet been incorporated into routine screening.

The RACOON MARDER project investigates how MRI data, combined with clinical information, can be used to identify patients at particularly high risk of developing liver cancer at an early stage. To this end, biomarkers are being analyzed and both statistical and AI-based models are being developed to enable more accurate risk assessment.

In the long term, the goal is to develop a personalized screening approach in which high-risk patients are specifically identified using MRI. The aim is to detect liver cancer earlier and thereby open up better treatment options.

More information can be found on the website:

https://www.netzwerk-universitaetsmedizin.de/en/research/all-num-projects