A FHIR Native Radiology Informatics Platform

Area of Science:

• Health Informatics
• Medical Imaging
• Artificial Intelligence

Background:

• The healthcare industry is increasingly adopting the Fast Healthcare Interoperability Resources (FHIR) standard.
• Radiology informatics platforms (RIS) are crucial for managing medical imaging data and workflows.
• Integrating advanced analytics, such as AI, into RIS can improve diagnostic accuracy and efficiency.

Purpose of the Study:

• To describe and implement a novel Radiology Informatics Platform (RIS) that is natively built on the FHIR standard.
• To incorporate Artificial Intelligence (AI) capabilities within the RIS for aiding in the interpretation of medical scans.
• To design a flexible and expandable platform leveraging FHIR workflow patterns and microservices.

Main Methods:

• Development of a FHIR-native Radiology Informatics Platform (RIS).
• Implementation of a microservices architecture for modularity and scalability.
• Integration of AI algorithms through FHIR workflow patterns acting as an application programming interface (API).

Main Results:

• A functional RIS has been implemented that adheres to the FHIR standard.
• The platform successfully integrates AI algorithms for scan interpretation assistance.
• The microservices-based design ensures flexibility and expandability of the RIS.

Conclusions:

• The developed FHIR-native RIS provides a robust and adaptable solution for modern radiology departments.
• Integration of AI within a FHIR-based RIS framework enhances diagnostic capabilities.
• The platform's architecture supports future growth and integration of new functionalities.