Making the Radiology Workflow Visible in Order to Inform Optimization Strategies.

Area of Science:

• Radiology
• Medical Imaging
• Health Informatics

Background:

• Medical imaging generates vast amounts of data, influenced by AI advancements.
• Radiology service providers must adapt workflows to meet these evolving challenges.
• Understanding real-world process and resource interactions is crucial for workflow optimization.

Purpose of the Study:

• To present an approach for optimizing radiology workflows.
• To demonstrate how merging diverse data sources can inform operational strategies.
• To explore interactive data visualization techniques for radiology data.

Main Methods:

• Integrated various data sources into a unified data model.
• Enabled efficient interactive queries on the consolidated data.
• Developed interactive visualizations to explore the data model.

Main Results:

• Demonstrated patient flow animation through the radiology workflow.
• Utilized energy consumption patterns to characterize operational modalities.
• Showcased the utility of the data model and visualizations for workflow analysis.

Conclusions:

• The proposed data model and visualization approach facilitate a deeper understanding of radiology operations.
• This method supports informed decision-making for adapting and optimizing radiology workflows.
• Interactive data exploration is key to managing the complexities of modern medical imaging services.