SynthesisBMC medical research methodology2018
A systematic review of the clinical application of data-driven population segmentation analysis.
Synthesis in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Semantic Terrain Segmentation in the Navigation Vision of Planetary Rovers-A Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Population segmentation based on healthcare needs: a systematic review.Systematic reviews · 2019Pooled it
- Clustering analysis of acute mountain sickness susceptibility among young adults during rapid ascent using low-altitude step test data.Physiological reports · 2026Article
- Introducing and testing the maternal vulnerability segmentation tool (MVST) for essential health services use: demonstrating its use in Oromia, Ethiopia.BMJ global health · 2026Article
- The health diplomacy spiral: a conceptual framework linking health system integration and public health diplomacy.Public health reviews · 2026Review
- A Novel Primary Care Planning Informatics Tool Informed by Data-Driven Multimorbidity Grouping: User-Centered Design and Feasibility Testing.JMIR formative research · 2025Article
- Systemic Population Segmentation Based on the Unified Care Model: An Approach to Health System Transformation.Healthcare (Basel, Switzerland) · 2025Article
- Uncovering Four Types of Care Coordination in Primary Care.Journal of the American Board of Family Medicine : JABFM · 2025Observational
- The PopGrouper as a tool for morbidity adjustment in regional comparisons of health care: an analytical framework.Research in health services & regions · 2025Article
- De-prescribing opioids among Medicaid patients with long-term opioid use.Journal of substance use and addiction treatment · 2025Article
- A data-driven approach to detect support strategies for children living in households receiving public assistance in Japan: a mixed methods study to establish tailor-made health and welfare care.International journal for equity in health · 2025Article
- Identifying households with children who have complex needs: a segmentation model for integrated care systems.BMC health services research · 2025Article
- Data-driven segmentation of type 2 diabetes mellitus patients: an observational study on health care utilisation prior to an emergency department visit in Germany.Frontiers in medicine · 2025Article
- Learning Care Pathways Framework: A New Method to Implement, Learn, Replicate, and Scale up Care Pathways for and With the Patient.International journal of health policy and management · 2025Article
- Machine Learning-Based Identification of High-Risk Patterns in Atrial Fibrillation Ablation Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- Impact of family doctor system on diabetic patients with distinct service utilisation patterns: a difference-in-differences analysis based on group-based trajectory modelling.BMJ global health · 2024Article
- Understanding the daily life needs of older public assistance recipient subgroups in Japan: A qualitative study.Global health & medicine · 2024Article
- Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- How predictive of future healthcare utilisation and mortality is data-driven population segmentation based on healthcare utilisation and chronic condition comorbidity?BMC public health · 2024Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.