MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641098760 A) filed by Sathyabama Institute Of Science And Technology on August 14, 2026, for Ai-Driven Predictive Nursing Dashboard For Early Detection Of Inpatient Clinical Deterioration.
Inventors include Ms. Bhuvaneswari S.; and Ms. Nancy Priscilla G..
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: ABSTRACT AI-Driven Predictive Nursing Dashboard for Early Detection of Inpatient Clinical Deterioration The present invention relates to a multi-modal artificial intelligence (AI)-driven predictive nursing dashboard for the early detection and proactive management of clinical deterioration in hospitalized patients. The system integrates real-time physiological data acquired from bedside monitoring devices with structured and unstructured clinical information obtained from electronic health records (EHR), including nursing documentation. A multi-modal AI analytical engine comprising a temporal neural network for time-series physiological analysis, a transformer-based natural language processing (NLP) module for clinical text analysis, and an attention-based fusion mechanism processes the heterogeneous data to generate a continuously updated Deterioration Risk Index (DRI) for each patient. The generated risk scores are displayed through an intelligent nursing dashboard that prioritizes patients according to the likelihood of clinical deterioration while providing explainable AI outputs identifying the principal physiological and clinical factors contributing to each prediction. The dashboard further supports clinical decision-making by presenting evidence-based intervention recommendations, thereby enabling timely nursing assessment and escalation of care. By combining numerical physiological trends with qualitative nursing observations, the invention provides more accurate and context-aware risk prediction than conventional threshold-based early warning systems. The invention improves patient safety, reduces preventable "failure-to-rescue" events, enhances workflow efficiency for frontline nursing staff, and facilitates proactive intervention in acute healthcare environments.
Disclaimer: Curated by HT Syndication.
