Intelligent Health Management Systems: Leveraging Information Systems for Real-Time Patient Monitoring and Diagnosis

Abstract

Managing patients in modern healthcare is getting harder and harder, mostly because more people are getting chronic diseases and the world's population is getting older. To deal with these problems, Intelligent Health Management Systems (IHMS) are being built into the healthcare system. These systems promise to make tracking and diagnosing patients much better. This essay looks at how IHMS could change real-time patient care by using cutting-edge information systems technologies like blockchain, artificial intelligence (AI), and machine learning (ML). IHMS really works because they can constantly collect and look at huge amounts of health data from many places, like personal sensors, electronic health records, and what patients say about their results. With this combination, a full picture of a patient's health can be seen at any time. AI and ML algorithms are very important because they can understand large datasets and find patterns to find trends, predict health events, and suggest specific treatment plans. The use of blockchain technology protects the data's stability, security, and privacy, easing major worries about data leaks and illegal access in healthcare. This paper discuss about several examples of how IHMS has used predictive analytics and quick actions to improve patient results and make diagnoses more accurate. Problems like how systems can talk to each other, data safety issues, and the need for strong legal frameworks are also looked at. The results show that using IHMS in healthcare can improve patient care, make better use of resources, and eventually lead to better health outcomes, even though there are some problems to solve. In the end, we suggest a strategy for integrating these technologies into current healthcare systems that will work well. We stress how important it is for engineers, healthcare workers, and lawmakers to work together.