How AI can boost clinical decision support in emergency medicine
The use of artificial intelligence for clinical decision support at the point of care is still in its infancy. Despite media attention and the increase in AI research, translation into clinical practice is not commonplace.
Little evidence exists on best practices for deployment, particularly in emergency medicine. Scott Levin knows all about this. He is senior director of research and innovation at Beckman Coulter and professor of emergency medicine at Johns Hopkins University School of Medicine.
Two use cases discussed
Levin is expected to give a presentation HIMSS24 in an educational session titled “Deploying Artificial Intelligence for Clinical Decision Support in Emergency Medicine.” In this session, two use cases of AI clinical decision support will be implemented in multiple emergency departments through the success phases of systems engineering: problem analysis, design, development, implementation, and impact analyses.
“Emphasis will be placed on the final phases of implementation,” Levin said. “The AI tools address challenges in emergency department decision-making and decision-making; important decisions that can be associated with high variability, bias and limited prognostic validity.”
A key learning objective for those attending the session is to identify the Agency for Healthcare Research in Quality’s (AHRQ) five phases of success in systems engineering related to pragmatic AI. examples of clinical decision support in the emergency department, he noted.
“It is essential for healthcare to have a framework for how AI tools address challenges, are developed, implemented and evaluated for impact,” he said. “This includes studying how doctors interact with these tools and how this can change their decision-making behavior.
“It is still unusual for AI tools to survive this full cycle, especially when it comes to tools that function at the point of care,” he continued. “The more examples the healthcare community can make visible, the greater the opportunities to realize benefits for patients.”
Reducing bias in AI
Another goal will be to illustrate ways to study and reduce bias using AI.
“This includes evaluating both AI algorithms for biases and status quo decision-making structures of physicians that may also be biased,” Levin explains. “When the latter is present and measurable, AI offers a unique opportunity to address the challenges directly at the point of care.
“This is very important for today’s healthcare as the community strives to eliminate disparities in care,” he concluded.
The session, “Deploying Artificial Intelligence for Clinical Decision Support in Emergency Medicine,” is scheduled for March 12, 1:15-1:45 p.m. in room W307A at HIMSS24 in Orlando. More information and registration.
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