Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.

ABSTRACT:

Background

While many falls are preventable, they remain a leading cause of injury and death in older adults. Primary care clinics largely rely on screening questionnaires to identify people at risk of falls. Limitations of standard fall risk screening questionnaires include suboptimal accuracy, missing data, and non-standard formats, which hinder early identification of risk and prevention of fall injury. We used machine learning methods to develop and evaluate electronic health record (EHR)-based tools to identify older adults at risk of fall-related injuries in a primary care population and compared this approach to standard fall screening questionnaires.

Methods

Using patient-level clinical data from an integrated healthcare system consisting of 16-member institutions, w

SUBMITTER: Song W 

PROVIDER: S-EPMC11018490 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature