Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

ABSTRACT:

Background

Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, providing a unique opportunity to monitor changes in physical activity, a key factor related to the risk of falls in hospitals. This study is aimed at utilizing RTLS data to capture dynamic patient movements, integrating it with clinical information through a machine learning approach to enhance in-hospital fall predictions.

Methods

This retrospective study developed and compared three models: clinical data only, RTLS data only and a combined data model. It included 22 201 patients from Yongin Severance Hospital, South Korea, from Ma

SUBMITTER: Kim DW 

PROVIDER: S-EPMC11850291 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature