Data-driven decision-making in healthcare: unveiling the potential of digital transformation in healthcare organizations

The digital revolution, accelerated by the global COVID-19 pandemic, has brought a significant transformation in decision-making across various industries. This paradigm shift, driven by the proliferation of data, presents unprecedented opportunities to enhance decision-making in uncertain and risky environments. However, the application of data-driven decision-making in the healthcare sector remains in its early stages despite its potential to revolutionize the industry. The healthcare industry has embraced technological breakthroughs, including cloud computing, big data, and the Internet of Things (IoT), ushering in the era of "Healthcare 4.0". Digital technologies, such as wearables, telemedicine, and electronic health records, have redefined the delivery of healthcare services. The COVID-19 pandemic has further accelerated the integration of digital technologies in healthcare, but decision-making in healthcare organizations remains complex and multifaceted, with far-reaching implications. This dissertation aims to explore the role of digital transformation and data-driven decision-making in healthcare by investigating how data can empower decision-making in this sector. The research is organized into five chapters. The first chapter involves a systematic review of existing literature on data utilization in healthcare decision-making, revealing research gaps and foundational premises for further investigation. The second and third chapters focus on collecting insights from healthcare professionals regarding the role of big data analytics and risk management practices in decision-making. The researches highlights respectively the significance of investing in big data analytics capabilities to enhance the quality of healthcare services and examine how risk management practices contribute to this improvement. In the fourth chapter, the research investigates the potential of a decision-support system model based on the exploitation of data through business intelligence to outperform traditional experience-driven practices in managing processes within the oncology domain. This analysis aims to demonstrate the practical implications of data-driven decision-making in a specific healthcare context, shedding light on the benefits and effectiveness of data-driven decision-making in healthcare. Finally, the fifth chapter investigates the potential of data to support healthcare decision-making, with a specific focus on the oncology domain and the utilization of national cancer screening programs. These investigations contribute valuable theoretical and practical insights into the practical applications of data-driven decision-making in healthcare, aiming for a more informed, data-driven future in healthcare, ultimately improving the quality of healthcare services.