Researchers at Johns Hopkins developed an AI system that reduces the risk of patient death due to sepsis. By detecting sepsis symptoms earlier, patients are 20% less likely to die from it.Sepsis is one of the most fatal causes of patient death in hospitals.

Figure 1: AI system that reduces the risk of patient death.

Figure 1 shows thatSepsis occurs when the body develops an extreme response to an infection, causing inflammation that can lead to blood clots and blood vessel leakage and potentially resulting in organ failure. The earlier sepsis is detected, the better the chances for recovery. [1]

researchers developed the Targeted Real-Time Early Warning System. Combining a patient's medical history with current symptoms and lab results, the machine-learning system shows clinicians when someone is at risk for sepsis and suggests treatment protocols, such as starting antibiotics.

The AI tracks patients from when they arrive in the hospital through discharge, ensuring that critical information isn't overlooked even if staff changes or a patient moves to a different department.

During the study, more than 4,000 clinicians from five hospitals used the AI in treating 590,000 patients. The system also reviewed 173,931 previous patient cases.In 82% of sepsis cases, the AI was accurate nearly 40% of the time.

Previous attempts to use electronic tools to detect sepsis caught less than half that many cases and were accurate 2% to 5% of the time. All sepsis cases are eventually caught, but with the current standard of care, the condition kills 30% of the people who develop it. [2]

In the most severe sepsis cases where an hour delay is the difference between life and death, the AI detected it an average of nearly six hours earlier than traditional methods. "This is a breakthrough in many ways," said Albert Wu.

The team also partnered with the two largest electronic health record system providers, Epic and Cerner, to ensure that the tool can be implemented at other hospitals.

The team has adapted the technology to identify patients at risk for pressure injuries, commonly known as bed sores, and those at risk for sudden deterioration caused by bleeding, acute respiratory failure, and cardiac arrest. [3]

References:

1. https://aibusiness.com/document.asp?doc_id=779316
2. https://www.technologynetworks.com/diagnostics/news/artificial-intelligence-speeds-up-sepsis-detection-363983
3. https://www.sciencedaily.com/releases/2022/07/220721132009.htm

Cite this article:

Sri Vasagi K (2022), AI System used to Detect Sepsis Symptoms Earlier, Anatechmaz, pp.165