ArticleHealth information science and systems2026
Healthcare utilization and chronic condition clusters in multimorbidity patients using weighted k-means: a register-based study in Denmark.
Article in Health information science and systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Patterns of multimorbidity, their outcomes and analytical methods for their determination: a systematic review.BMC public health · 2026Pooled it
- Dental Treatment Needs and Cost Burden Among Older Adults: A K-Means Cluster Analysis to Inform Oral Health Policies.International journal of environmental research and public health · 2026Article
- Detecting comorbidity patterns in rare disease patients with machine learning.Frontiers in epidemiology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The growing burden of multimorbidity challenges the healthcare system due to increased healthcare utilization and uncoordinated care. Identifying patients with multimorbidity who have high healthcare utilization is essential to improve management and reduce pressure on the healthcare system. Objective: This study aims to identify and characterize clusters of patients with multimorbidity based on both their chronic conditions and healthcare utilization patterns. Methods: A weighted K-means method was applied to a population of 1,184,334 individuals with two or more out of 33 chronic conditions, defined using diagnostic algorithms based on ICD-10 and ATC codes. Sociodemographic variables were applied to describe the identified clusters. Results: Four clusters were identified based on chronic conditions and healthcare utilization. Cluster 1 had the highest healthcare utilization and a high burden of both somatic and mental conditions, combined with low social status. Cluster 2, consisting primarily of younger women with mental conditions, showed high use of psychological services, and few somatic conditions. The largest cluster, cluster 3, had low healthcare utilization and consisted of individuals with common, manageable conditions, and relatively high social status. Cluster 4 was defined by older individuals with complex somatic conditions requiring frequent contact with general practitioners and specialists. Conclusion: The identified clusters showed varying chronic condition patterns and levels of healthcare utilization. The findings underscore the importance of tailored strategies, particularly for multimorbidity patients with mental conditions taking social status into account, in order to improve care and manage resource use more effectively.
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