Trends in healthcare data analytics: a topic modelling approach to decision-making, efficiency, and outcomes

ABSTRACT While data analytics (DA) can enhance healthcare performance to new levels, its application in healthcare faces numerous challenges, ranging from data and methodological issues to organisational-related barriers. Moreover, the research on the application of DA in healthcare is simultaneously developed in several disciplines, which on some occasions has resulted in the formation of disintegrated siloes of knowledge. We employ various text mining techniques, including topic modelling and co-occurrence analysis to analyse 14919 published studies and describe the healthcare DA literature, providing an integrative view of the literature across various disciplines. We identify the main areas of research, including “optimising technology utilisation in healthcare” and “empowering public healthcare systems” and discuss key tools used to tackle problems associated with each area. Our analysis shows that “advancing digital health and telemedicine,” “empowering patients for self-care,” and “enhancing patient outcomes through personalized care” are among the emerging research areas.