Summary AI integration in clinical workflows has proven to be effective in improving efficiency and patient outcomes. Lahey Hospital & Medical Center implemented AI algorithms into their radiology department's workflow, resulting in improved case prioritization, faster reading times, and enhanced patient care. By integrating six AI algorithms over two years, the hospital achieved sensitivity and specificity between 90-95% for intracranial hemorrhage detection. The AI workflows enable positive findings to be flagged and displayed clearly, ensuring critical cases are prioritized. With visual indicators and processing status badges, radiologists can efficiently manage and interpret imaging studies. This successful case study demonstrates how AI workflows can be seamlessly integrated into existing systems, providing tangible benefits to healthcare organizations.
AI workflows can be effectively integrated into clinical settings
Key Features • Case prioritization for potentially critical findings • Visual indicators for clear display of AI findings • Processing status indication for ongoing algorithm processing
Use Cases • Implement AI algorithms to assist in diagnosing and triaging imaging studies for potentially critical findings. • Improve case prioritization by leveraging AI-flagged cases with positive findings. • Enhance visual indicators with a badging system to clearly display AI findings. • Streamline processing status by adding badges to indicate when an algorithm is still processing a study. • Achieve higher sensitivity and specificity for detecting intracranial hemorrhage using AI algorithms. • Improve efficiency and reading times for radiologists. • Enhance patient care by prioritizing potentially critical cases. • Revolutionize team collaboration by automating tasks and enhancing decision-making. • Inspire innovation in the healthcare industry. • Improve overall productivity and achieve better results.