Transformative AI in Health Information Exchange for Predictive Analytics Enhancing Patient Outcomes
Rubia, Aasif Shah, Jamshaid Iqbal Janjua
Abstract
Artificial Intelligence (AI) has recently become a driving factor in healthcare. This tendency was significantly boosted through the implementation of Health Information Exchange (HIE) systems that promote the simultaneous sharing of patient data among various healthcare organizations. The present research probes into the influence of the AI-powered predictive model on the outcomes and conclusions made by healthcare physicians utilizing the HIE system. Through the development of complex machine learning (ML) algorithms based on diverse and comprehensive data sets, the given study intends to increase the credibility and reliability of predictive modeling applied in clinical practice and, therefore, boost the level of care provided and mitigate the expenses associated with it. This was accomplished by extracting data from HIE systems, pre-processing and cleaning it to improve its quality, and analyzing it with AI approaches such as neural networks, decision trees, and ensemble techniques. Extensive validation and testing of the models resulted in analytical consistency. The findings imply that AI-based predictive models produce improved patient outcomes in clinical settings. From a healthcare perspective, these findings have far-reaching implications. AI has the curious potential to work in tandem with an HIE layer to forecast and anticipate ailment progression, allowing treatment programs to be more customized. This study emphasizes AI's potential to enhance health care and demonstrates the necessity for additional investigation in this field