Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study

Abstract

Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi-view ensemble learning and model-based missing data imputation method. As input, over two thousand inpatient fall patients’ demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi-view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross-validated AUC of 0.713 (95% CI, 0.701–0.725), an AUC of 0.808 (95% CI, 0.740–0.876) on the separate testing set. Our studies show the efficacy of integrative machine-learning based classifier model in dealing with multi-source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients.

Development of an algorithm for assessing fall risk in a Japanese inpatient population

“…In addition, falls cause the injuries that require treatment 16 , and can result in serious complications 6 , 17 , 18 . For instance, traumatic cerebral hemorrhage cases due to fall accidents account for two-thirds of deaths, and serious injuries due to falls cause a loss of disability-adjusted life years 17 – 19 .…”

Fall Risk Assessment and Ranking using Survival Analysis

“…In this section, we will focus solely on studies that use data from EHR systems. Wang et al proposed the "MELMV" classifier in 2019 for fall prevention [15]. It was trained on over 2000 observations of fall patients' demographic characteristics, diagnoses, procedural data, and bone density measurements, which achieved a cross-validated area under the curve (AUC) of 0.808 (95% CI: 0.740-0.876) on a separate test set.…”

Development of a predictive inpatient falls risk model using machine learning

“…To date, several studies have employed ML to detect fall risks or fall risks with injury in hospitals. Most of these studies make use of the data available in the EMR to develop their models (Marschollek et al, 2012; Cho et al, 2019; Yokota et al, 2017; Lindberg et al, 2020; Nakatani et al, 2020, Wang et al, 2019), although we can also find studies that use a motion tracking system with cameras that capture patients' physical performance tests (Eichler et al, 2022). Models based on EMR variables typically include the following types of variables: Demographic characteristics, admission information, assessment information, clinical data, and organizational characteristics.…”