Approaches to Using Artificial Intelligence and Big Data to Improve the Efficiency of Public Health Institutions

Authors: Andreea Maria Arginteanu, Daniela Ioana Manea, Anca Costin Belean

Abstract

This paper is dedicated to exploring the use of artificial intelligence and big data to enhance the efficiencies of public health. In this era of growing healthcare demands with limited resources, governments and public health institutions are looking increasingly towards digital innovation. AI technologies such as predictive analytics, machine learning and natural language processing apply across various aspects of public health when combined with large health data sources. Given the significance of such techniques, the article outlines several key approaches: predictive models used to predict disease outbreaks and patient suffering (early warning systems); AI-powered systems for allocation of hospital resources; and natural language processing tools which can pull out meaning from unstructured clinical data. Also discussed are the roles of AI involving virtual assistants or chatbots; and Big Data policies for public health in general. Case studies from countries like South Korea, the United Kingdom and Romania illustrate the real-life impact of AI and big data in handling crises such as the COVID-19 outbreak or everyday care improvement needs. However, the paper also highlights the pressing problems that must be faced: data security, algorithm bias, infrastructure constraints and the need for a clear regulatory framework. The paper concludes by recommending the strategic and ethical integration of AI and big data technologies in public health, emphasizing the necessity to invest in digital infrastructure, personnel training and considerate management. Such integration has the potential to dramatically modernise public health's functions and make healthcare delivery more genuinely effective and fair.