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

Abstract

BackgroundData-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.MethodsWe 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.ResultsWe 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.ConclusionsData-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.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0584-9) contains supplementary material, which is available to authorized users.

Identifying subgroups of high-need, high-cost, chronically ill patients in primary care: A latent class analysis

“…The BIC and BLRT were considered most important in deciding on the best model as these outperform other statistical indicators [43]. Besides statistical indicators, clinical relevance of the model was a key factor, as the model should support daily clinical practice [15]. Also, the size of the classes within the model was taken into account (also reffered to as substantiality) [15].…”

“…This population with a disproportionately high care use is also referred to as the high-need, high-cost (HNHC) population [10,12]. The identification of the HNHC population, as a subgroup of the total population, is embedded in the approach of population segmentation, which is defined as the division of a specific population into homogeneous subgroups with distinct needs and (health) characteristics [13][14][15]. A closely related concept in which principles of segmentation are applied, pertains to the concept of 'population (health) management' (PM), [16] as a way to promote 'population health' [17,18].…”

Can cluster analyses of linked healthcare data identify unique population segments in a General Practice-registered population?

“…In traditional (or expert-driven) approaches, a population is segmented on a-priori, expert-defined criteria informed by literature review and consensus. (6) For example, the Suicide and Self Harm Prevention Strategy for Wales 2015-2020 highlights the need to focus preventative efforts towards men aged 15-44 years. (7) In England, the London Health Commission segmented the population of London based on morbidity and age group.…”

Identifying optimal indicators and purposes of population segmentation through engagement of key stakeholders: a qualitative study

“…Segmentation is a concept typically used to group patients and healthy people into segments with relatively similar needs or characteristics. It is a construct used widely to gauge who might benefit from receiving a certain combination of interventions [5][6][7]. Research shows that segmentation facilitates the development of an integrated care package of services by implementing tailored care models for each segment [8,9].…”