Key result
Data-driven population segmentation is widely applied across clinical settings, most commonly using latent class analysis.
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.
Answers are grounded in this paper and cite their sources.
Optimal segmentation requires analytics plus clinical expertise; confirms latent class analysis dominance across 216 studies.
BACKGROUND: Data-driven population segmentation analysis utilizes data analytics to divide a heterogeneous population into parsimonious and relatively homogenous groups with similar healthcare characteristics. It is a promising patient-centric analysis that enables effective integrated healthcare interventions specific for each segment. Although widely applied, there is no systematic review on the clinical application of data-driven population segmentation analysis. METHODS: We carried out a systematic literature search using PubMed, Embase and Web of Science following PRISMA criteria. We included English peer-reviewed articles that applied data-driven population segmentation analysis on empirical health data. We summarized the clinical settings in which segmentation analysis was applied, compared and contrasted strengths, limitations, and practical considerations of different segmentation methods, and assessed the segmentation outcome of all included studies. The studies were assessed by two independent reviewers. RESULTS: We retrieved 14,514 articles and included 216 articles. Data-driven population segmentation analysis was widely used in different clinical contexts. 163 studies examined the general population while 53 focused on specific population with certain diseases or conditions, including psychological, oncological, respiratory, cardiovascular, and gastrointestinal conditions. Variables used for segmentation in the studies are heterogeneous. Most studies (n = 170) utilized secondary data in community settings (n = 185). The most common segmentation method was latent class/profile/transition/growth analysis (n = 96) followed by K-means cluster analysis (n = 60) and hierarchical analysis (n = 50), each having its advantages, disadvantages, and practical considerations. We also identified key criteria to evaluate a segmentation framework: internal validity, external validity, identifiability/interpretability, substantiality, stability, actionability/accessibility, and parsimony. CONCLUSIONS: Data-driven population segmentation has been widely applied and holds great potential in managing population health. The evaluations of segmentation outcome require the interplay of data analytics and subject matter expertise. The optimal framework for segmentation requires further research.
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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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