An Open-Source Monitoring Framework for Data Exploration and Progress Tracking in Multi-Center Radiology Studies

Researchers have proposed an open-source monitoring framework for multi-center radiology studies, designed to replace outdated manual tracking with automated, privacy-preserving dashboards. The system, built on the Grafana-Prometheus stack, was deployed across all 38 German university clinics in the RACOON consortium [1]. The framework, described in a paper submitted on 15 June 2026, collects aggregated monitoring metrics from distributed study sites and visualizes them through configurable dashboards [1]. It is integrated into Kaapana, an existing medical imaging platform, and its source code has been made publicly available [1]. The submission file size is 4,323 KB [1]. The project is led by Markus Ralf Bujotzek [1]. Multi-center studies are essential for advancing radiological research, but their coordination often relies on manual communication and shared tables that quickly become outdated [2]. The new architecture addresses this by providing transparent and up-to-date insights into study progress without exposing individual patient data [2]. The technical foundation rests on two widely adopted open-source tools. Grafana is an analytics and visualization application that connects to time-series databases and supports data sources including Prometheus, Elasticsearch, and PostgreSQL [6]. Prometheus, a free software application for event monitoring and alerting, records metrics in a time-series database using an HTTP pull model and is commonly paired with Grafana for dashboard visualization [7]. Both projects are licensed under open-source terms—Grafana under the AGPLv3 since 2021 and Prometheus under the Apache 2.0 License [6][7]. The deployment within the RACOON consortium demonstrates the framework's ability to enable privacy-preserving data exploration across all 38 German university clinics [2]. The system supports transparent coordination of distributed research activities, which the authors argue can facilitate more efficient management of large-scale multi-center studies [2]. Broader research has identified the absence of ongoing performance monitoring as a primary barrier preventing clinically validated AI tools from reaching routine use beyond their originating institutions [3]. Inadequate implementation infrastructure and limited workflow integration further slow adoption of new technologies in healthcare [3]. The proposed framework directly targets these gaps by embedding monitoring into the research workflow itself.