REAL-TIME PATIENT RISK IDENTIFICATION USING DEEP LEARNING
Authors: R Akshay Krishna , Venkateswaran Dhanya , T Mala , D Narasimhan , Mahalakshmi Ms. 
Published: June 2024

In order to ensure high-quality treatment and patient safety, the nursing staff must have the required information present exactly when it is needed. This means immediate access to vital information related to the patient’s care. The information must also be adequately comprehensive, unambiguous and ready to use. Ensuring patient safety requires a common effort and strong cooperation between different parties and professionals, both within the hospital as well as between the patient and the entire healthcare system. An uninterrupted flow of real-time information is the cornerstone of patient safety. Patient information is genuinely real-time if it can be entered and viewed immediately during an event related to the patient’s treatment at the point of care, and the information is made available to all parties involved in the treatment process simultaneously. The Real-Time Patient Risk Identification project aims to develop an innovative system for real-time identification of patient risks in healthcare settings. By leveraging advanced technologies and data analysis techniques, the project seeks to enhance patient safety and improve healthcare outcomes. The project focuses on continuous monitoring of patient data, including vital signs, electronic health records, medication records, and other relevant information. Real-time data streams are processed using sophisticated algorithms and predictive models to identify potential risks faced by patients. The system integrates machine learning and data analytics to analyze patterns and trends in the data, allowing for early detection of risks. By applying predictive modeling techniques, the project can proactively identify patients at high risk for adverse events, such as infections, medication errors, or deteriorating health conditions. Healthcare providers are alerted in real-time when a risk is identified, enabling prompt interventions and preventive measures. The project also incorporates decision support tools, providing healthcare professionals with actionable insights and recommendations to guide their clinical decision-making. The benefits of real-time patient risk identification are significant. It allows for early intervention, reducing the likelihood of adverse events and improving patient safety. By detecting risks in real-time, healthcare providers can implement targeted interventions and personalized care plans, leading to better healthcare outcomes. Furthermore, the project contributes to optimized resource allocation by prioritizing high-risk patients and ensuring efficient use of healthcare resources. It also facilitates data-driven research and quality improvement initiatives by generating valuable insights from the continuous monitoring data. In conclusion, the Real-Time Patient Risk Identification project offers a transformative solution for timely identification of patient risks in healthcare. By leveraging real-time data analysis and advanced technologies, the project has the potential to significantly improve patient safety, enhance clinical outcomes, and optimize healthcare delivery. This initiative represents a crucial step towards proactive and personalized care in the healthcare industry.