A collaboration between Johns Hopkins researchers and software company HUINNO could advance the early prediction of clinical deterioration in hospital patients using AI technology.
The collaborators are working on AI-based models that aim to predict the early risk factor of clinical deterioration.
Kunihiro Matsushita, MD, PhD, a faculty member from the Johns Hopkins University’s Bloomberg School of Public Health and School of Medicine, has teamed up with HUINNO Co., Ltd., a software company specializing in artificial intelligence for healthcare, to advance the early prediction of clinical deterioration in hospital patients.
Matsushita is a professor of epidemiology, international health, and cardiology, with expertise in risk prediction and electronic health record (EHR) research. He and a multidisciplinary team assembled from across Johns Hopkins include skills covering AI research in health, electrophysiology, EHR research, precision medicine, and human factors engineering.
HUINNO’s CEO Yeongjoon Gil, PhD, believes that combining HUINNO AI technology with the university’s research expertise can significantly improve the early detection of patient deterioration in hospital settings.
General ward hospital patients often experience unpredictable physiological deterioration, yet monitoring frequency is typically only every 6–8 hours, potentially creating significant delays in recognizing critical changes. Existing early warning scores may have limited predictive performance and can contribute to alert burden and alarm fatigue. The collaboration aims to address these challenges through AI models that aim to enable earlier intervention and improve patient outcomes.
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