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Journal Article


Jung H, Park HA, Hwang H. Comput. Inform. Nurs. 2019; ePub(ePub): ePub.


Author Affiliations: College of Nursing and Research Institute of Nursing Science, Seoul National University, Seoul (Ms Jung and Dr Park); and Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam (Dr Hwang), South Korea.


(Copyright © 2019, Lippincott Williams and Wilkins)






Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future.

Language: en


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