Using Artificial Intelligence With Rapid Response Teams

I am writing in response to the article “Integration of Rapid Response Teams and Early Warning Systems to Reduce Cardiac Arrests and Intensive Care Unit Readmissions.”1 Having a dedicated rapid response team for emergent situations is invaluable; however, this article demonstrated the benefits of integrating an early warning system that can identify signs of deterioration before a detrimental event occurs. Early clinical intervention is essential to achieving the best outcomes for patients. Unfortunately, most rapid response teams are activated retrospectively—after a patient’s condition has already declined—rather than proactively.In this quality improvement project, the rapid response team used an early warning system derived from artificial intelligence (AI) to identify patients who were clinically deteriorating and in need of intervention. In a meta-analysis, Yuan et al2 concluded that using AI-based early warning systems decreased hospital length of stay and 30-day mortality but increased intensive care unit length of stay. According to the authors, this finding may be attributed to earlier recognition of deterioration and more timely transfer to the intensive care unit. Using AI can help facilitate prompt intevention, which in turn can decrease patient mortality and hospital length of stay, thereby improving patient outcomes.At my hospital, we have an Acute Changes Team (ACT) that responds to all calls throughout the hospital when a patient experiences a sudden decline in status. The ACT nurse on duty also participates in proactive rounding by meeting with the team leader on each unit during every shift to discuss patients who may require ACT involvement. Unfortunately, we do not currently have a reliable early warning system in place. I believe that implementing an AI-driven system would help generate a more accurate list of patients to prioritize during proactive rounds, allowing earlier intervention before a sudden clinical change occurs. Such proactive measures could reduce the number of ACT calls and decrease mortality through earlier detection and intervention.Overall, the use of AI as an early warning system has great potential and is gradually gaining traction within hospital detection systems. It will be interesting to see whether further quality improvement projects emerge as AI implementation expands among rapid response teams, further promoting proactive, data-driven patient care.