Use of EHR Data to Identify Factors Affecting the Time to Fall

机译：使用EHR数据来识别影响跌倒时间的因素

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Although there are many studies of falls occurring in a hospital setting, research on factors affecting time to fall after admission is scarce. It is important for nurses to identify factors contributing to an early fall so that they can pay particular attention to patients with such factors. In this study, patients who sustained a fall were extracted from an adverse event reporting system and narrative nursing records of those hospitalized between January 2015 and May 2016. We used the electronic health records of ten different data sources to extract fall-related variables; the data were integrated according to normalization criteria. Univariate and multiple linear regression analyses were used to identify factors influencing the time to fall from admission. About 49% of fallersfell within the first week after admission. A walking disorder, comor-bid disease, intravenous therapy, and arterial lines were related to early falls.

机译：虽然在医院环境中发生了许多瀑布的研究，但在入场后影响时间落下的因素研究是稀缺的。识别对早期跌倒的因素是重要的，因此他们可以特别注意具有此类因素的患者。在这项研究中，持续下降的患者是从2015年1月和2016年1月至2016年1月期间住院的不良事件报告系统和叙述护理记录中提取。我们使用了十个不同的数据来源的电子健康记录来提取与坠落有关的变量;根据标准化标准整合数据。单变量和多元线性回归分析用于识别影响入院时间的时间的因素。在入学后的第一周内约有49％的堕落。步行障碍，科博疾病，静脉治疗和动脉线与初期有关。

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《MEDINFO》|2017年|xxxvi 679-1403 p.|共5页
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Hyesil Jung; Hyeoun-Ae Park;
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1. Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach [J] . Lindberg David S., Prosperi Mattia, Bjarnadottir Ragnhildur I., International journal of medical informatics . 2020,第Nova期

机译：识别住院病风险预测模型中的重要因素，以提高使用EHR和电子行政数据的照顾质量：一种机器学习方法
2. A data-driven and practice-based approach to identify risk factors associated with hospital-acquired falls: Applying manual and semi- and fully-automated methods [J] . Lucero Robert James, Lindberg David S., Fehlberg Elizabeth A., International journal of medical informatics . 2019,第FEBa期

机译：一种基于数据和基于实践的方法来识别与医院获得的跌倒相关的风险因素：应用手动，半自动和全自动方法
3. Timed up and go test combined with self-rated multifactorial questionnaire on falls risk and sociodemographic factors predicts falls among community-dwelling older adults better than the timed up and go test on its own [J] . Azianah Ibrahim, Devinder Kaur Ajit Singh, Suzana Shahar, Journal of Multidisciplinary Healthcare . 2017,第1期

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4. Use of EHR Data to Identify Factors Affecting the Time to Fall [C] . Hyesil Jung, Hyeoun-Ae Park MEDINFO . 2017

机译：使用EHR数据来识别影响跌倒时间的因素
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Use of EHR Data to Identify Factors Affecting the Time to Fall

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