Automating Assessment of Lifestyle Counseling in Electronic Health Records
Automating Assessment of Lifestyle Counseling in Electronic Health Records
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DOI：
10.1016/j.amepre.2014.01.001
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发表时间：
2014-05-01
影响因子：
5.5
通讯作者：
Steiner, John F.
中科院分区：
文献类型：
--
作者：
Hazlehurst, Brian L.;Lawrence, Jean M.;Steiner, John F.
背景资料：许多基于人群的调查表明，超重和肥胖患者可以受益于日常临床护理期间的生活方式咨询。目的：为了确定自然语言处理（NLP）是否可以应用于电子健康记录（EHR）中的信息，以自动评估临床healthcare encounters.Methods提供的体重管理相关的咨询：MediClass系统与NLP功能被用来确定EHR中的体重管理咨询。NLP应用的知识来源于行为咨询的5As框架：询问（评估体重和相关疾病），建议高危患者减肥，评估患者改变行为的准备程度，通过讨论减肥方法和计划提供帮助，并安排后续工作，包括转诊。使用2007年1月1日至2011年3月31日期间来自两个卫生系统的EHR数据样本，在600名妊娠期糖尿病（GDM）妇女的产后访视中，与作为金标准的手动病历审查相比，评价了MediClass处理器识别这些咨询元素的准确性。结果：与金标准相比，5个A的平均灵敏度和特异性均在85%或以上，但辅助系统的灵敏度除外，两个卫生系统的灵敏度分别为40%和60%。自动化的方法确定了许多有效的辅助情况下，没有确定在金standard.Conclusions：MediClass处理器的性能足够类似于人类的抽象，允许产后遇到记录的体重减轻咨询的自动评估能力。(C)2014年美国预防医学杂志
Background: Numerous population-based surveys indicate that overweight and obese patients can benefit from lifestyle counseling during routine clinical care. Purpose: To determine if natural language processing (NLP) could be applied to information in the electronic health record (EHR) to automatically assess delivery of weight management-related counseling in clinical healthcare encounters.Methods: The MediClass system with NLP capabilities was used to identify weight-management counseling in EHRs. Knowledge for the NLP application was derived from the 5As framework for behavior counseling: Ask (evaluate weight and related disease), Advise at-risk patients to lose weight, Assess patients' readiness to change behavior, Assist through discussion of weight-loss methods and programs, and Arrange follow-up efforts including referral. Using samples of EHR data between January 1, 2007, and March 31, 2011, from two health systems, the accuracy of the MediClass processor for identifying these counseling elements was evaluated in postpartum visits of 600 women with gestational diabetes mellitus (GDM) compared to manual chart review as the gold standard. Data were analyzed in 2013.Results: Mean sensitivity and specificity for each of the 5As compared to the gold standard was at or above 85%, with the exception of sensitivity for Assist, which was 40% and 60% for each of the two health systems. The automated method identified many valid Assist cases not identified in the gold standard.Conclusions: The MediClass processor has performance capability sufficiently similar to human abstractors to permit automated assessment of counseling for weight loss in postpartum encounter records. (C) 2014 American Journal of Preventive Medicine