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Painting galleries typically provide a wealth of data composed of several data types. Those multivariate data are too complex for laymen like museum visitors to first, get an overview about all paintings and to look for specific categories. Finally, the goal is to guide the visitor to a specific painting that he wishes to have a more closer look on. In this paper we describe an interactive visualization tool that first provides such an overview and lets people experiment with the more than 41,000 paintings collected in the web gallery of art. To generate such an interactive tool, our technique is composed of different steps like data handling, algorithmic transformations, visualizations, interactions, and the human user working with the tool with the goal to detect insights in the provided data. We illustrate the usefulness of the visualization tool by applying it to such characteristic data and show how one can get from an overview about all paintings to specific paintings.
In der Medizin existieren verschiedene Reifegradmodelle, die die Digitalisierung von Krankenhäusern unterstützen können. Die Anforderungen an ein Reifegradmodell für diesen Zweck umfassen Aspekte aus allgemeinen und spezifischen Bereichen des Krankenhauses. Die Analyse der Reifegradmodelle HIN, CCMM, EMRAM und O-EMRAM zeigt große Lücken im Bereich des OP sowie fehlende Aspekte in der Notaufnahme auf. Ein umfassendes Reifegradmodell wurde nicht gefunden. Durch eine Kombination aus HIN und CCMM könnten fast alle Bereiche ausreichend abgedeckt werden. Zusätzliche Ergänzungen durch spezialisierte Reifegradmodelle oder sogar die Entwicklung eines umfassenden Reifegradmodells wären sinnvoll.
Purpose: Medical processes can be modeled using different methods and notations.Currently used modeling systems like Business Process Model and Notation (BPMN) are not capable of describing the highly flexible and variable medical processes in sufficient detail.
Methods: We combined two modeling systems, Business Process Management (BPM) and Adaptive Case Management (ACM), to be able to model non-deterministic medical processes. We used the new Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN).
Results: First, we explain how CMMN, DMN and BPMN could be used to model non-deterministic medical processes. We applied this methodology to model 79 cataract operations provided by University Hospital Leipzig, Germany, and four cataract operations provided by University Eye Hospital Tuebingen, Germany. Our model consists of 85 tasks and about 20 decisions in BPMN. We were able to expand the system with more complex situations that might appear during an intervention.
Conclusion: An effective modeling of the cataract intervention is possible using the combination of BPM and ACM. The combination gives the possibility to depict complex processes with complex decisions. This combination allows a significant advantage for modeling perioperative processes.