Leveraging Endeavour AI to Eliminate Redundant CT Studies Performed
Technology
National Healthcare Innovation and Productivity Medals
National University Health System
31 December 2024
To leverage AI technology to identify and reduce redundant CT scan requests. AI-driven vetting systems significantly reduce redundant imaging, enhance efficiency, and save valuable radiology resources.
Year Submitted: 2024
Published Date: 31 December 2024
Tags: Technology, Digital Health, Data Analytics, Artificial Intelligence
About this Content
Aims
To leverage AI technology to identify and reduce redundant CT scan requests.
Background
Manual vetting of CT requests was time-consuming and prone to redundancy.
Methods
Developed an AI tool (Endeavour AI) integrated with EHR to detect redundant CT studies and prioritize actionable requests.
Results
Sensitivity of AI system improved to 0.91; time spent on vetting reduced by 90%; 8 redundant CT forms flagged daily, improving efficiency.
Conclusion
AI-driven vetting systems significantly reduce redundant imaging, enhance efficiency, and save valuable radiology resources.
Lessons Learnt
AI implementation requires multidisciplinary collaboration and flexibility during integration phases.
Additional Information
Successfully deployed AI solution; scalable for other diagnostic workflows.
Keywords
Endeavour AI, Redundant Imaging, Workflow Efficiency, Diagnostic Technology, Automation
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | National University Health System |
Organization(s) Involved | National University Hospital |
Platform(s) | National Healthcare Innovation and Productivity Medals |
Healthcare Professional Group(s) | Allied Health |
Applicable Specialty or Discipline | Diagnostic Radiography |
Project Lead(s) | Ang Xu Kai |
Project Member(s) | Lim Shi Wei Desmond |
Connect with this contributor!
Ang Xu Kai - xu_kai_ang@nuhs.edu.sg
Project Attachment
400_NUH_NHIP_2024_Leveraging_Endeavour_AI_To_Eliminate_Redundant_CT_Studies_Performed.pdf