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BMC Medical Research Methodology

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Open Access 01-12-2018 | Research article

A systematic review of the clinical application of data-driven population segmentation analysis

Authors: Shi Yan, Yu Heng Kwan, Chuen Seng Tan, Julian Thumboo, Lian Leng Low

Published in: BMC Medical Research Methodology | Issue 1/2018

Abstract

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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Title: A systematic review of the clinical application of data-driven population segmentation analysis
Authors: Shi Yan
Yu Heng Kwan
Chuen Seng Tan
Julian Thumboo
Lian Leng Low
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: BMC Medical Research Methodology / Issue 1/2018
Electronic ISSN: 1471-2288
DOI: https://doi.org/10.1186/s12874-018-0584-9

At a glance: The STEP trials

Springer Medicine

A systematic review of the clinical application of data-driven population segmentation analysis

Abstract

Background

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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