Can we accurately describe pathways through care for patients with long-term conditions by linking data from different health care sources?


In England, more than 15 million people have a long-term condition such as diabetes or atrial fibrillation, accounting for 70% of health and social care spending. This figure is set to increase, particularly for those people who have three or more conditions simultaneously (multimorbidity), and who require care and support from multiple agencies across several sectors. We want to know if linking data from different sources could give a clearer picture of these patients’ pathways through care, for example, to see which factors determine the extent to which patients with long-term conditions are treated within primary or secondary care.

We are using coding algorithms to identify long-term conditions recorded in different databases, and to examine the consistency in recording long term conditions within and between databases. We are identifying cohorts of patients with different long-term conditions to look at the relationship between indicators of primary care performance and variation in use of different types of health care. Atrial fibrillation is being used as an example and we have identified a cohort of patients with a first recorded inpatient diagnosis of atrial fibrillation in 2010/11.

Principal investigator: Professor Jan van der Meulen, London School of Hygiene & Tropical Medicine

Start date: January 2014

Partners and collaborators involved: London School of Hygiene & Tropical Medicine; University College London, Farr Institute; Queen Mary University of London