Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018

Multiple chronic diseases in patients are a major burden on the health service system. Currently, diseases are mostly treated separately without paying sufficient attention to their relationships, which results in the fragmentation of the care process. The better integration of services can lead to the more effective organization of the overall health care system.

This study aimed to analyze the connections between diseases based on their co-occurrences to support decision-makers in better organizing health care services.

We performed a cluster analysis of diagnoses by using data from the Finnish Health Care Registers for primary and specialized health care visits and inpatient care. The target population of this study comprised those 3.8 million individuals (3,835,531/5,487,308, 69.90% of the whole population) aged ≥18 years who used health care services from the years 2015 to 2018. They had a total of 58 million visits. Clustering was performed based on the co-occurrence of diagnoses. The more the same pair of diagnoses appeared in the records of the same patients, the more the diagnoses correlated with each other. On the basis of the co-occurrences, we calculated the relative risk of each pair of diagnoses and clustered the data by using a graph-based clustering algorithm called the M-algorithm-a variant of k-means.

The results revealed multimorbidity clusters, of which some were expected (eg, one representing hypertensive and cardiovascular diseases). Other clusters were more unexpected, such as the cluster containing lower respiratory tract diseases and systemic connective tissue disorders. The annual cost of all clusters was €10.0 billion, and the costliest cluster was cardiovascular and metabolic problems, costing €2.3 billion.

The method and the achieved results provide new insights into identifying key multimorbidity groups, especially those resulting in burden and costs in health care services.