Applying a data-driven population segmentation approach in German claims data

BMC Health Services Research

Research

Hendrikje Lantzsch¹  Carolina Pioch¹  Reinhard Busse²  Cornelia Henschke²  Verena Vogt³ 

[1] Department of Health Care Management, Technical University of Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany;Department of Health Care Management, Technical University of Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany;Berlin Centre of Health Economics Research (BerlinHECOR), Technical University of Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany;Department of Health Care Management, Technical University of Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany;Institute of General Practice and Family Medicine, Jena University Hospital, Friedrich Schiller University, Bachstraße 18, 07743, Jena, Germany;

关键词: Population segmentation;    Healthcare utilisation;    Population health;    Cluster analysis;    Claims data;   

DOI  :  10.1186/s12913-023-09620-3

 received in 2022-10-17, accepted in 2023-05-30,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundSegmenting the population into homogenous groups according to their healthcare needs may help to understand the population’s demand for healthcare services and thus support health systems to properly allocate healthcare resources and plan interventions. It may also help to reduce the fragmented provision of healthcare services. The aim of this study was to apply a data-driven utilisation-based cluster analysis to segment a defined population in the south of Germany.MethodsBased on claims data of one big German health insurance a two-stage clustering approach was applied to group the population into segments. A hierarchical method (Ward's linkage) was performed to determine the optimal number of clusters, followed by a k-means cluster analysis using age and healthcare utilisation data in 2019. The resulting segments were described in terms of their morbidity, costs and demographic characteristics.ResultsThe 126,046 patients were divided into six distinct population segments. Healthcare utilisation, morbidity and demographic characteristics differed significantly across the segments. The segment “High overall care use” comprised the smallest share of patients (2.03%) but accounted for 24.04% of total cost. The overall utilisation of services was higher than the population average. In contrast, the segment “Low overall care use” included 42.89% of the study population, accounting for 9.94% of total cost. Utilisation of services by patients in this segment was lower than population average.ConclusionPopulation segmentation offers the opportunity to identify patient groups with similar healthcare utilisation patterns, patient demographics and morbidity. Thereby, healthcare services could be tailored for groups of patients with similar healthcare needs.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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