Identifying high-risk patient clusters for falls using unsupervised machine learning on linked primary and secondary care electronic health record data
File(s)
Article in press
Author(s)
Type
Journal Article
Abstract
Falls risk is multifactorial, involving a combination of clinical and sociodemographic factors. Although guidelines acknowledge this complexity, most research has focused on individual risk factors, leaving the combined impact of comorbidities relatively understudied. This population-wide study used electronic health records (EHR) linked across primary and secondary care to identify falls risk profiles in the North West London (NWL) population and to stratify patients by their likelihood of requiring falls-related hospital care using unsupervised clustering. We conducted cluster analysis on patients from NWL General Practice records using coded falls risk factors. Cluster membership was compared against the risk of falls-related hospital encounters. Among four identified clusters, two groups of older, multimorbid patients were 11 times more likely to have a fall-related hospital encounter (RR 11.45, 95% CI 10.14–12.92 and RR 11.63, 95% CI 10.30–13.13) and had significantly longer mean length of stay compared with younger, fitter patients. Between two younger clusters, patients with higher deprivation levels were 29% more likely to have a fall-related hospital encounter (RR 1.29, 95% CI 1.12–1.49). These findings demonstrate that clustering routinely collected EHR data can identify population segments at highest risk of falls-related hospital use, supporting more targeted, multifactorial risk assessment and prevention strategies.
Date Issued
2026-07-06
Date Acceptance
2026-05-29
Citation
Scientific Reports, 2026
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Copyright Statement
© The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s41598-026-55527-4
Subjects
Falls
Risk Factors
Population Health
Machine Learning
Electronic Health Records
Publication Status
Published online
Date Publish Online
2026-07-06