AI-Driven Big Data Solutions for Personalized Healthcare: Analyzing Patient Data to Improve Treatment Outcomes - Ageng Setiani Rafika; Adam Faturahman; Bintang Nandana Henry; Firdaus Dwi Yulian; Mohammed Hassan (Pandawan Sejahtera Indonesia, 2025)
The advent of AI-driven big data solutions has transformed personalized healthcare by enabling the analysis of vast and complex patient datasets to optimize treatment outcomes. This study aims to evaluate the effectiveness of AI models in improving healthcare delivery through enhanced diagnostic accuracy, reduced processing times, and personalized treatment plans. The research utilizes AI models to process extensive patient data from electronic health records, wearable devices, and genetic information. The results show an impressive accuracy rate of 93%, a 25% reduction in diagnostic errors, and significant improvements in patient outcomes, including 72% of patients receiving more accurate diagnoses and 65% experiencing faster recovery. A comparison with traditional methods highlights the advantages of AI in scalability, efficiency, and reliability, offering a clear improvement over existing healthcare approaches. However, challenges such as data bias, ethical concerns, and scalability need to be addressed to en- sure the responsible application of AI in healthcare systems. In conclusion, this research provides valuable insights for healthcare organizations that aim to implement AI-driven solutions, fostering the advancement of patient care and encouraging innovation in the industry. The findings suggest that AI-powered big data solutions have the potential to revolutionize healthcare, improving diagnostic precision and treatment personalization, ultimately enhancing patient satisfaction and outcomes.