Artificial intelligence (AI) is proving to be a promising tool in addressing the challenges faced by patients with multiple long-term conditions (MLTC). In a session at the Digital Health AI and Data conference, four UK research groups discussed how they are using AI and data analytics to analyze population health data and improve the management of chronic diseases. Simon Fraser, a public health professor, highlighted the importance of understanding the development and foundations of chronic conditions across a person’s lifetime. The researchers are exploring the use of birth cohorts, which provide extensive social data, and ordinary healthcare information to gain a comprehensive understanding of the risk factors for developing long-term conditions.
One of the key challenges in utilizing AI for population health is integrating different types of data. Birth cohorts are rich in social data and determinants, while routine healthcare records provide information on long-term conditions. The researchers are working on creating data links between these two sources to gain insights into the onset of chronic diseases and identify opportunities for prevention or delay. However, ensuring the reliability and security of these integrated datasets remains a challenge for the future.
The Digital Health AI and Data conference, organized by the market-leading event Digital Health Rewired, aims to explore the potential of AI and data analytics in healthcare. The sessions focus on two key themes: AI and Analytics, and Data and Research. The conference is free for the NHS, public sector, start-ups, charities, education, and research, with commercial tickets available for purchase. It is an opportunity for healthcare professionals to learn about the latest advancements in AI and data-driven healthcare.
