CODEX Meets RACOON – A Concept for Collaborative Documentation of Clinical and Radiological COVID-19 Data

Abstract

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the “Electronic Data Capture” (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

Research collaboration data platform ensuring general data protection

“…Continuing from the MII, the Network of University Medicine (NUM), established in 2020, 24,25 contributes through its coordinated efforts and platforms to better prepare German health research and, consequently, the healthcare system as a whole for future pandemics and other crises. NUM, started as part of crisis management against COVID-19, coordinating clinical COVID-19 research across all university hospital sites, fostering collaboration among researchers for practical, patient-centric outcomes and better management of public health crises.The sub-project Radiological Cooperative Network (RACOON) is the first of its kind to bring together all university departments of a medical discipline and establish a nationwide platform for collaborative analysis of radiological image data [26][27][28] . This platform supports clinical and clinical-epidemiological studies as well as the training of AI models.…”

Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology

“…An example is the German Radiological Cooperative Network (RACOON) initiative [19][20][21][22], aimed to connect the radiology departments of all university hospitals in Germany to provide easy access to a large amount of curated data for multicentric studies. This kind of infrastructure can be used for federated data analysis and training of machine learning algorithms in medical diagnostics and research [6,18,20].…”