使用电子病历中叙述性医师笔记文本的不遵守和不出席的计算分析。

Alexander Turchin, Nikheel S Kolatkar, Merri L Pendergrass, Isaac S Kohane
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引用次数: 8

摘要

不遵守医生的建议是常见的,被认为会导致不良的临床结果。然而,目前还没有对这一现象进行大规模评估的技术。我们评估了一种计算方法,通过对医生笔记文本的分析来量化患者的不依从性。不依从性指数(INA)是根据在医生记录中检测到的不依从性单词标签的数量来计算的。通过将结果与单个句子和患者水平的手动患者记录审查进行比较来评估INA。确定了INA与急诊科就诊频率之间的关系。个别不依从词标签的阳性预测值为93.3%。通过手工检查,INA与记录的不依从性病例数之间的Pearson相关系数为0.62。在最高四分位数(最不坚持)的INA患者中,ED就诊频率是最低四分位数(最坚持)的INA患者的两倍多(p < 0.0001)。我们描述了一种新方法的设计和评估,该方法可以通过分析医生笔记来量化患者不遵守医生建议的情况。该方法已在多个层面得到验证,并被证明与临床结果相关。
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Computational analysis of non-adherence and non-attendance using the text of narrative physician notes in the electronic medical record.

Non-adherence to physician recommendations is common and is thought to lead to poor clinical outcomes. However, no techniques exist for a large-scale assessment of this phenomenon. We evaluated a computational approach that quantifies patient non-adherence from an analysis of the text of physician notes. Index of non-adherence (INA) was computed based on the number of non-adherence word tags detected in physician notes. INA was evaluated by comparing the results to a manual patient record review at the individual sentence and patient level. The relationship between INA and frequency of Emergency Department visits was determined. The positive predictive value of identification of individual non-adherence word tags was 93.3%. The Pearson correlation coefficient between the INA and the number of documented instances of non-adherence identified by manual review was 0.62. The frequency of ED visits was more than twice as high for patients with INA in the highest quartile (least adherent) than for patients with INA in the lowest (most adherent) quartile (p < 0.0001). We have described the design and evaluation of a novel approach that allows quantification of patient non-adherence with physician recommendations through an analysis of physician notes. This approach has been validated at several levels and demonstrated to correlate with clinical outcomes.

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