BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Health Innovation Program, University of Wisconsin-Madison, Madison, WI, 53705, USA.
BMC Med Inform Decis Mak. 2019 Jul 22;19(1):138. doi: 10.1186/s12911-019-0843-7.
Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification.
In this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results.
The NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders.
Our pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance.
老年人跌倒不仅是急诊科就诊的常见原因，也是发病率和死亡率的主要原因。为了提供适当的护理和转诊，在急诊科遇到时快速、可靠地识别跌倒患者至关重要。不幸的是，没有手动图表审查，许多应用程序（包括监测和质量报告）都无法识别跌倒，这是一个耗时的过程。在这里，我们描述了一种实用的自然语言处理方法来实现跌倒识别自动化。
在这项单中心回顾性研究中，随机选择了 500 名老年患者（年龄在 65 岁及以上）的急诊科医生记录进行分析。开发了一种简单的、基于规则的自然语言处理算法来识别跌倒事件，并在 1084 份记录的开发集上进行了评估，然后与经过训练的盲于自然语言处理结果的摘要者的共识识别进行了比较。
与人工编码员的黄金标准手动摘要相比，该自然语言处理管道在识别急诊科就诊记录中的跌倒事件方面具有 95.8%的召回率（灵敏度）、97.4%的特异性、92.0%的精度和 0.939 的 F1 分数。
我们的实用自然语言处理算法能够以出色的精度和召回率识别 ED 记录中的跌倒，与更耗时的手动摘要相当。这一发现不仅为改进研究方法提供了希望，而且还为有针对性的干预、质量测量开发和流行病学监测确定患者提供了可能。
