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

Abstract

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

Multimorbidity transitions and the associated healthcare cost among the Finnish adult population during a two-year follow-up

“…Previously, Fränti et al constructed a multimorbidity network covering all Finnish primary and specialised care services patient visits in 2015–2018 and related diagnoses represented as blocks of ICD-10 codes. 12 , 13 Using the network data, we ensured that relevant diseases were comprehensively identified and grouped to assess multimorbidity. Furthermore, a multimorbidity measure was created based on these 34 disease groups and used for the analysis to understand the impact of multimorbidity on healthcare utilisation and costs in Finland.…”

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

“…[ 20 ] Previous studies also used unsupervised learning and network visualization of big data for chronic disease diagnoses. [ 24 ] Machine learning techniques were used in several research about renal problems such as CKD early detection in the EHR database or prediction for acute kidney injury. Several studies have been conducted on the use of artificial intelligence (AI) in ESRD and CKD.…”

Qluster: An easy-to-implement generic workflow for robust clustering of health data

“…In health data analysis, clustering methods are a primary tool, for finding pockets of homogeneity within a heterogeneous population, to uncover different disease phenotypes, stages of a disease, or variations in disease outcomes (Fränti et al, 2022 ). A precise understanding of the clusters of patients suffering from a disease ultimately allows for the overall improvement of their care (Windgassen et al, 2018 ).…”