Navigating the fusion of AI and Big Data in healthcare organizations : a literature review

Abstract

The intersection of Big Data Analytics (BDA) and Artificial Intelligence (AI) with the healthcare sector represents a pivotal shift towards data-driven decision-making and optimization of healthcare services. With healthcare organizations worldwide facing economic pressure and aging populations, the imperative to reduce costs while enhancing care quality has never been more critical. This thesis explores the transformative potential of BDA and AI in healthcare, examining how these technologies can address the sector's challenges. The aim of the thesis is to research and analyze the topic in the form of a literature review and find out how BDA and AI can benefit the healthcare sector as per the literature. The text also addresses the main challenges associated with BDA and AI in the literature. As a result, it was determined that BDA and AI present significant advantages for the healthcare sector. From improving diagnostic accuracy and early detection of disease markers to enabling personalized medicine through mapping the human genome and bringing major savings due to better resource allocation and planning. However, the implementation of AI-BDA solutions in healthcare organizations seems to be very limited and still very much in the development phase. This means that organizations currently don’t have a predefined and existing “gold standard” model to AI-BDA solutions. These limitations restrict the widespread adoption and implementation of BDA and AI solutions in healthcare settings. Given these findings, it is recommended that further empirical research in the form of case studies be conducted to evaluate the real-world impacts and benefits of BDA and AI in healthcare.