DIGITAL HEALTH TECHNOLOGIES AND PREDICTIVE ANALYTICS FOR ENHANCING PATIENT OUTCOMES IN MODERN HEALTHCARE SYSTEMS

Abstract

Rapid developments of digital technologies have sparked a transformation of healthcare systems and enabled the creation of more interconnected and data-driven patient-centric methods of delivering healthcare. Digital health technologies, such as electronic health records (EHRs), telemedicine solutions, wearables, IoMT platforms and even artificial intelligence (AI) applications, have changed healthcare in terms of accessibility, disease surveillance and decision making in an enormous way. Additionally, predictive analytics became a very helpful technique for gaining useful insights from the complicated healthcare data in order to detect diseases, stratify patients' risks, develop personalized treatment, and optimize healthcare processes. The purpose of this review is to discuss the principles of digital health technologies and predictive analytics, and how they are used in the current healthcare system. The review also briefly addresses the effects that the use of these technologies will have on patient safety, disease management, health care delivery efficiency and population health management. In addition, the significance of data privacy, cybersecurity, interoperability, algorithmic bias and governance are also assessed. Future trends in the innovation of the healthcare sector, including generative artificial intelligence, digital twins, federated learning, blockchain data sharing and multi-omics are also discussed. In conclusion, the combination of digital health technologies and predictive analytics promises a lot in terms of improving the quality of health care and developing sustainable health care systems.