Leveraging Artificial Intelligence and Big Data Analytics for Improved Management and Research of Multimorbidity

Abstract

Multimorbidity denotes the simultaneous presence of two or more chronic illnesses inside an individual. Consequently, individuals with multimorbidity possess diverse and specific treatment requirements. In reality, fulfilling these objectives is challenging due to the fact that the organisational procedures of contemporary healthcare systems are mostly designed for a singular condition. A transformative shift in the problem-solving methodology for medical research and therapy is essential to enhance clinical decision-making and patient care in multimorbidity. Alongside the conventional reductionist methodology, we advocate for participatory research bolstered by artificial intelligence (AI) and sophisticated big data analytics. This research methodology, when used with data typically gathered in healthcare environments, offers a cohesive framework for investigating multimorbidity-related research problems. This may include, for instance, prediction, correlation, and classification challenges derived from various interaction aspects. To actualise the concept of this paradigm change in multimorbidity research, it is essential to optimise, standardise, and, most critically, integrate electronic health data into a unified national and worldwide research infrastructure. Ultimately, there is a need for the integration and use of effective AI methodologies, especially deep learning, into clinical practice directly into the workflows of healthcare professionals.