QualiDIP project examines whether socioeconomic disparities in usage and quality of primary care translate to respective disparities in outcomes of specialised care in major public health issues of acute coronary syndrome, stroke, and hip fracture. Moreover, it uses novel artificial intelligence based Natural Language Processing (NLP) models to identify patient circumstances from free text in electronic health records to analyse the extent to which such translation is driven by individuals' disease severity and adherence to treatment. This topic offers novel insights for health equity research and helps to identify structural characteristics within health care through which unjust disparities may be reduced. The project uses NLP models and state-of-the-art statistical models with free text and routinely collected register data of the adult populations in Scotland and Finland, and will be carried out in the Finnish Institute for Health and Welfare and in the University of Edinburgh.

2025

2029