Securing FHIR Servers: Security Evaluation and AI-Driven Medical Data Analysis and Simulations for Enhanced Healthcare Services
Security Evaluation and AI-Driven Medical Data Analysis and Simulations for Enhanced ...
Summary
A joint project between universities aims to enhance healthcare security and utilize AI-driven data analysis techniques using the HL7 FHIR standard. The focus includes identifying vulnerabilities in FHIR servers, improving treatment pathways via process mining, and performing automatic mapping to codes like SNOMED-CT.
Details
This project is a collaboration between Goce Delcev University (UGD) and the University of Applied Sciences Upper Austria (UASUA). It has a dual focus: enhancing healthcare security and utilizing AI for advanced medical data analysis, based on the HL7 FHIR standard. Technically, the team will conduct a security evaluation through stenographic analysis of the FHIR standard to identify server vulnerabilities. Furthermore, they plan to apply AI algorithms to analyze medical data for improving treatment pathways and data quality. Specific methods include using Process Mining on logs to identify and validate clinical pathways, and performing automatic matching between free text and machine-readable codes (e.g., SNOMED-CT). The project aims to create mathematical models and perform simulations based on clinical guidelines and demographic data from both North Macedonia and Austria to verify these goals. This comprehensive approach combines security measures with advanced AI analysis to strengthen healthcare systems.
Original content copyright by respective publishers