Key result
Data-driven population segmentation is widely applied across clinical settings, most commonly using latent class analysis.
Population
216 peer-reviewed articles applying data-driven population segmentation analysis on empirical health data
Design
Systematic_review
Authors
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Optimal segmentation requires analytics plus clinical expertise; confirms latent class analysis dominance across 216 studies.
Systematic Review (n=216)
Data-driven population segmentation is widely applied in clinical contexts, with latent class analysis being the most common method, and requires both data analytics and subject matter expertise for optimal evaluation.
Kwan et al. (2018) conducted a systematic review in Various (general population and specific diseases) (n=216). Data-driven population segmentation analysis was evaluated on Clinical settings, segmentation methods, and segmentation outcomes. Data-driven population segmentation analysis is widely applied across various clinical contexts, with latent class analysis being the most common method among the 216 reviewed studies.
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