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

Background Segmenting 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 frag...

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Published in:BMC health services research Vol. 23; no. 1; pp. 591 - 11
Main Authors: Pioch, Carolina, Henschke, Cornelia, Lantzsch, Hendrikje, Busse, Reinhard, Vogt, Verena
Format: Journal Article
Language:English
Published: London BioMed Central 08.06.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Age
Ambulatory care
Analysis
Care and treatment
Claims data
Cluster analysis
Datasets
Disease
Emergency medical care
Family physicians
Health Administration
Health Informatics
Health insurance
Health services
Health services utilization
Healthcare utilisation
Hospitals
Long term health care
Medical care
Medical care, Cost of
Medicine
Medicine & Public Health
Methods
Nursing Research
Patients
Population health
Population segmentation
Primary care
Public Health
Utilization
Variables
Germany
United Kingdom > UK
England
Cluster analysis
Population segmentation
Claims data
Healthcare utilisation
Population health
ISSN:1472-6963, 1472-6963
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Summary:Background Segmenting 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. Methods Based 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. Results The 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. Conclusion Population 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.
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ISSN:1472-6963
1472-6963
DOI:10.1186/s12913-023-09620-3