2571-1075ISSN (Online)
Volume 10 , Issue 1 , PP: 07-17, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Jacinth salome 1 * , Kowsalyadevi Krishnaraj 2 , Chandra Sekar P. 3 , Tatiraju V. Rajani Kanth 4
Doi: https://doi.org/10.54216/IJBES.100102
The increasing demand for personalized, efficient, and adaptive healthcare solutions has catalyzed the development of dynamic, learning-driven software ecosystems. This paper introduces a novel framework that leverages real-time data and machine learning algorithms to revolutionize healthcare services. The proposed system integrates continuous learning capabilities to enhance decision-making, optimize resource allocation, and enable precise diagnostics and treatment plans. By incorporating real-time data from patient monitoring systems, electronic health records, and IoT-enabled devices, the ecosystem offers adaptable healthcare solutions that evolve based on new data insights. The adaptability and scalability of the proposed framework ensure that healthcare providers can offer timely and personalized interventions while minimizing operational costs. Key features include dynamic learning models, predictive analytics, and seamless integration with existing healthcare infrastructures. Through extensive case studies, the paper demonstrates how these innovations can transform patient care, improve outcomes, and support proactive healthcare management.
Dynamic Learning, Software Ecosystems, Real-Time Adaptation, Healthcare Optimization, Predictive Analytics, Machine Learning in Healthcare, Personalized Healthcare, IoT in Healthcare, Adaptive Healthcare Systems, Data-Driven Decision Making
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