AI-DRIVEN ELECTRONIC HEALTH RECORD (EHR) SUMMARIZATION | IJCT Volume 13 – Issue 2 | IJCT-V13I2P28
International Journal of Computer Techniques
ISSN 2394-2231
Author
Shaik. Afroz, Shaik. Sudheer Basha, T. Lakshmi Narendra, MS.B. Sasi
Abstract
AI-driven Electronic Health Record (EHR) summarization systems are a critical component of modern healthcare information management, as they directly influence clinical efficiency, decision accuracy, and patient safety. Their primary objective is to reduce information overload while ensuring rapid access to clinically relevant patient data throughout the care lifecycle. This paper discusses current technical approaches to EHR summarization, focusing on the application of artificial intelligence, natural language processing, and machine learning techniques to analyze large volumes of unstructured medical records. The relationship between automated summarization and intelligent clinical decision support is also examined, highlighting its growing importance in next-generation healthcare systems. Limitations in existing manual and rule-based summarization methods are identified based on observations from recent healthcare data management practices. These findings emphasize the need for innovative AI-based solutions that enhance accuracy, efficiency, and interpretability while complementing existing clinical workflows rather than replacing established healthcare processes
Keywords
EHR, Natural language Processing, Clinical text summarization, Medical Decision support, Automated reporting.
Conclusion
AI-driven Electronic Health Record (EHR) summarization systems are essential for ensuring efficiency, accuracy, and reliability in modern healthcare environments. These systems continuously analyze large volumes of clinical data, identify critical medical information, and support timely clinical decision-making by leveraging advanced natural language processing, data analytics, and machine learning techniques Real-Time Monitoring: Constant data collection from a range of sensors provide real-time information about the condition of spacecraft components, enabling the quick identification of anomalies.
1.Predictive Maintenance: By utilizing machine learning and predictive analytics, it is possible to anticipate probable faults before they happen, which facilitates prompt maintenance and lowers the possibility of mission-critical problems.
2.Autonomous Operations: * State-of-the- art health management systems can facilitate autonomous decision-making, which is essential for deep space missions as it enables spacecraft to respond to problems without human interference.
3.The integration of data from many subsystems into a centralized health management system improves situational awareness and offers a holistic perspective of the spacecraft’s health. This is known as data integration.
4.Enhanced Mission Safety: These systems greatly increase the overall safety and success rate of space missions by detecting and reducing dangers early on.
5.Cost Efficiency: Early problem identification and resolution lowers mission failure and repair costs, increasing the cost- effectiveness of space missions.
To sum up, in order to ensure that spacecraft can function safely and effectively in the hostile environment of space, advanced health monitoring and management systems must be developed and put into place for current space missions to succeed.
References
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How to Cite This Paper
Shaik. Afroz, Shaik. Sudheer Basha, T. Lakshmi Narendra, MS.B. Sasi (2026). AI-DRIVEN ELECTRONIC HEALTH RECORD (EHR) SUMMARIZATION. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
