Year: 2026
Type: review
Study design: Systematic Review
Abstract: Clustering can organize heterogeneous healthcare data into clinically or operationally meaningful groups, but evidence of real decision-support use remains uneven. This systematic review examines healthcare domains, data types, clustering methods, validation practices, and implementation gaps in clustering-based Healthcare Decision Support Systems (DSS). Following PRISMA 2020, Scopus records published from 2015 to 2025 were screened using predefined eligibility criteria. Eighty-four records were identified; 36 remained after the open-access filter, 19 full-text reports were assessed, and 10 primary empirical studies were included. The studies covered fraud auditing, ICU profiling, chronic-disease risk and transition analysis, gestational-diabetes support, trauma pathways, radiology text, patient segmentation, and smart-home activity recognition. K-Means was the most frequently reported method, followed by Markov-based approaches, while other clustering techniques appeared less often. Most studies relied on retrospective, internal, or expert-based validation; only one reported prospective evaluation of a deployed web-based clinical DSS. Overall, clustering shows clear analytical value, but evidence for routine clinical deployment and patient-outcome improvement remains limited.
Institutions: Diponegoro University, University of Bunda Mulia
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7223363943