TAILORING ONLINE HEALTH INFORMATION RETRIEVAL SYSTEMS TO DIVERSE USER NEEDS: A PERSONALIZATION PERSPECTIVE A PERSONALIZATION PERSPECTIVE Section Articles

Abstract

The digital revolution has transformed access to health information, driving the development

of online health information retrieval systems that cater to individual user needs through

personalization. This paper explores the growing reliance on these systems and the necessity

for customization to enhance user experience and health outcomes. Personalization involves

tailoring information based on user demographics, health literacy, cultural and linguistic

backgrounds, health conditions, psychographics, and accessibility needs. Technologies like AI

and machine learning play a critical role in advancing personalization by analysing user data

to predict and deliver relevant content. Despite the benefits, challenges such as privacy

concerns, algorithmic biases, and technical scalability issues persist. Evaluating the

effectiveness of these systems through metrics like user engagement and health outcomes, and

incorporating user feedback, ensures continuous improvement. The future of personalized

health information retrieval lies in integrating emerging technologies, ensuring data privacy

and ethical use, and adapting systems to diverse global contexts to achieve equitable health

information access.