@article{ref1,
title="Development and evaluation of an automated fall risk assessment system",
journal="International journal for quality in health care",
year="2016",
author="Lee, Ju Young and Jin, Yinji and Piao, Jinshi and Lee, Sun-Mi",
volume="28",
number="2",
pages="175-182",
abstract="BACKGROUND AND OBJECTIVE: Fall risk assessment is the first step toward prevention, and a risk assessment tool with high validity should be used. This study aimed to develop and validate an automated fall risk assessment system (Auto-FallRAS) to assess fall risks based on electronic medical records (EMRs) without additional data collected or entered by nurses. <br><br>METHODS: This study was conducted in a 1335-bed university hospital in Seoul, South Korea. The Auto-FallRAS was developed using 4211 fall-related clinical data extracted from EMRs. Participants included fall patients and non-fall patients (868 and 3472 for the development study; 752 and 3008 for the validation study; and 58 and 232 for validation after clinical application, respectively). The system was evaluated for predictive validity and concurrent validity. <br><br>RESULTS: The final 10 predictors were included in the logistic regression model for the risk-scoring algorithm. The results of the Auto-FallRAS were shown as high/moderate/low risk on the EMR screen. The predictive validity analyzed after clinical application of the Auto-FallRAS was as follows: sensitivity = 0.95, NPV = 0.97 and Youden index = 0.44. The validity of the Morse Fall Scale assessed by nurses was as follows: sensitivity = 0.68, NPV = 0.88 and Youden index = 0.28. <br><br>CONCLUSION: This study found that the Auto-FallRAS results were better than were the nurses' predictions. The advantage of the Auto-FallRAS is that it automatically analyzes information and shows patients' fall risk assessment results without requiring additional time from nurses.<p /> <p>Language: en</p>",
language="en",
issn="1353-4505",
doi="10.1093/intqhc/mzv122",
url="http://dx.doi.org/10.1093/intqhc/mzv122"
}