AI Model Detects Sepsis 6 Hours Early
An AI model is being used in intensive care units to detect sepsis six hours earlier than traditional diagnostic methods. The predictive tool analyzes patient data to identify early signs of the life-threatening condition. This advance highlights the potential for AI to revolutionize critical care by enabling earlier intervention.
- The AI model, named the Targeted Real-Time Early Warning System (TREWS), was developed by researchers at Johns Hopkins University. It analyzes a patient's medical history, current symptoms, and lab results to identify those at risk for sepsis. - In a study involving 590,000 patients and over 4,000 clinicians, the use of TREWS was associated with a nearly 20% reduction in patient mortality from sepsis. - Traditional sepsis diagnosis is challenging because early symptoms like fever and confusion are common in many other conditions, often leading to delays in treatment. There is no single definitive test for sepsis; diagnosis relies on clinical judgment based on a combination of factors such as vital signs, blood cultures, and imaging. - The TREWS system was implemented across five hospitals and integrated with the two largest electronic health record (EHR) systems, Epic and Cerner, to ensure broad applicability. A company spun off from Johns Hopkins, Bayesian Health, managed the deployment. - While several AI models for sepsis exist, a key challenge is a high rate of false alarms, which can lead to "alert fatigue" among clinicians. The TREWS model was accurate in 82% of sepsis cases, a significant improvement over previous electronic tools that were accurate only 2% to 5% of the time. - In April 2024, the FDA authorized the first AI-powered diagnostic tool for sepsis, the Sepsis ImmunoScore from the company Prenosis. This signals a move towards regulatory acceptance of AI in critical care diagnostics, a key area for digital health startups. - Investment in digital health startups focusing on AI is growing, with U.S. firms raising $14.2 billion in 2025. Companies with AI-enabled products commanded a 19% premium on average deal size compared to non-AI companies, indicating strong investor interest in this technology. - The development of consumer-facing symptom checker apps like Ada and Buoy Health, which use AI to provide preliminary health assessments, reflects a broader trend of using AI for early risk detection outside of hospital settings. These platforms empower users to monitor their health, a core principle for consumer health startups focused on chronic conditions and wellness.
