MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641087267 A) filed by Hyderabad Institute Of Technology And Management on July 16, 2026, for System And Method For Dynamic Digital Twin-Based Prediction And Prevention Of Hospital-Acquired Infections Using Multi-Source Artificial Intelligence.
Inventors include Nethani Shivakumar; Chindala Tharun Kumar; Shaik Meer Subhani Ali; P. Sravya; K. Krishna Jyothi; Bhaskar Das; T Naga Praveena; T. Sunitha; and Desu Manikantha.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The disclosure herein pertains to a computer-implemented system and method for predicting and preventing hospital-acquired infections using a continuously synchronized digital twin in conjunction with multi-source artificial intelligence. The disclosed system includes a Multi-Source Data Acquisition Engine that handles the acquisition of diverse healthcare information from Electronic Health Records (EHR), Hospital Information Systems (HIS), Laboratory Information Systems (LIS), Internet of Medical Things (IoMT) devices, wearable sensors, environmental monitoring systems, medical equipment, and hospital operational databases. A Data Integration and Harmonization Engine will preprocess and normalize acquired data to build a Dynamic Digital Twin, which will be the representation of the physical and operational condition of a healthcare facility. A Dynamic Knowledge Graph Generator models the relationship between patients, health care workers, medical devices, environmental conditions and health care operational events. An Artificial Intelligence Prediction Engine estimates the probabilities of infections and an Explainable Artificial Intelligence Engine is responsible for creating interpretable risk explanations and determining likely routes of infection. Based on the availability of resources, an Intelligent Intervention Recommendation Engine provides optimized preventive interventions: Patient Isolation, Environmental Disinfection, Ventilation Management, Reallocation of Healthcare Workers, Sterilization of Medical Equipment, Resource Allocation. A Hospital Risk Visualization Dashboard provides real-time data on infection risk, dynamically maps it out and offers suggested interventions for authorized healthcare workers. A Continuous Learning Engine learns and optimizes prediction models based on real-world infections observed and feedback from interventions to enhance future prediction accuracy. The disclosed invention provides proactive infection prevention, patient safety, optimized utilization of healthcare resources, evidence-based clinical decision making and smart deployment in smart healthcare environments with interoperability with the existing hospital information systems.
Disclaimer: Curated by HT Syndication.
