PURPOSE Malnutrition related to undernutrition in pediatric oncology patients is associated with worse outcomes including increased morbidity and mortality. At a tertiary pediatric center, traditional malnutrition screening practices were ineffective at identifying cancer patients at risk for undernutrition and needing nutrition consultation. METHODS To efficiently identify undernourished patients, an automated malnutrition screen using anthropometric data in the electronic health record (EHR) was implemented. The screen utilized pediatric malnutrition (undernutrition) indicators from the 2014 Consensus Statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition with corresponding structured EHR elements. The time periods before (January 2016-August 2017) and after (September 2017-August 2018) screen implementation were compared. Process metrics including nutrition consults, timeliness of nutrition assessments, and malnutrition diagnoses documentation were assessed using statistical process control charts. Outcome metrics including change in nutritional status at least 3 months after positive malnutrition screen were assessed with the Cochran-Armitage trend test. RESULTS After automated malnutrition screen implementation, all process metrics demonstrated center line shifts indicating special cause variation. For patient admissions with a positive screen for malnutrition of any severity level, no significant improvement in status of malnutrition was observed after 3 months (P = .13). Sub-analysis of patient admissions with screen-identified severe malnutrition noted improvement in degree of malnutrition after 3 months (P = .02). CONCLUSIONS Select 2014 Consensus Statement indicators for pediatric malnutrition can be implemented as an automated screen using structured EHR data. The automated screen efficiently identifies oncology patients at risk of malnutrition and may improve clinical outcomes.

中文翻译：

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使用电子健康记录中的人体测量值评估自动儿科营养不良屏幕：质量改进计划。

目的与小儿肿瘤患者营养不良相关的营养不良与不良后果相关，包括发病率和死亡率增加。在三级儿科中心，传统的营养不良筛查方法无法有效识别有营养不良风险并需要营养咨询的癌症患者。方法为了有效地识别营养不良的患者，使用了电子健康记录（EHR）中的人体测量数据对营养不良进行了自动筛查。该屏幕使用了2014年营养与营养学会/美国肠胃外和肠内营养学会共识声明中的儿科营养不良（营养不良）指标以及相应的结构化EHR元素。比较了屏幕实施之前（2016年1月至2017年8月）和之后（2017年9月至2018年8月）的时间段。使用统计过程控制图评估了过程指标，包括营养咨询，营养评估的及时性和营养不良诊断文档。营养不良筛查后至少3个月，包括Cochran-Armitage趋势测试评估结果指标，包括营养状况变化。结果实施自动营养不良筛查后，所有过程指标均显示出中心线偏移，表明特殊原因引起的变化。对于任何严重程度的营养不良筛查呈阳性的患者，在3个月后均未观察到营养不良状况的显着改善（P = .13）。对经过筛查确定为严重营养不良的患者入院进行的亚分析显示，三个月后营养不良程度有所改善（P = .02）。结论可以使用结构化的EHR数据，将2014年小儿营养不良的《 2014年共识声明》指标实现为自动筛选。自动筛查可以有效地识别出有营养不良风险的肿瘤患者，并可以改善临床结果。