Topaz Maxim, Lai Kenneth, Dowding Dawn, Lei Victor J, Zisberg Anna, Bowles Kathryn H, Zhou Li
Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Brigham and Women's Hospital, Boston, MA, USA.
Int J Nurs Stud. 2016 Dec;64:25-31. doi: 10.1016/j.ijnurstu.2016.09.013. Epub 2016 Sep 19.
Electronic health records are being increasingly used by nurses with up to 80% of the health data recorded as free text. However, only a few studies have developed nursing-relevant tools that help busy clinicians to identify information they need at the point of care.
This study developed and validated one of the first automated natural language processing applications to extract wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes.
First, two human annotators manually reviewed a purposeful training sample (n=360) and random test sample (n=1100) of clinical notes (including 50% discharge summaries and 50% outpatient notes), identified wound cases, and created a gold standard dataset. We then trained and tested our natural language processing system (known as MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard. We also compared the prevalence of wound cases identified from free-text data with coded diagnoses in the structured data.
The testing dataset included 101 notes (9.2%) with wound information. The overall system performance was good (F-measure is a compiled measure of system's accuracy=92.7%), with best results for wound treatment (F-measure=95.7%) and poorest results for wound size (F-measure=81.9%). Only 46.5% of wound notes had a structured code for a wound diagnosis.
The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application's information coverage to other relevant wound factors and validation of the model with external data.
电子健康记录正越来越多地被护士使用，高达80%的健康数据以自由文本形式记录。然而，只有少数研究开发了与护理相关的工具，以帮助忙碌的临床医生在护理点识别他们所需的信息。
本研究开发并验证了首个用于从自由文本临床记录中提取伤口信息（伤口类型、压疮分期、伤口大小、解剖位置和伤口治疗）的自动化自然语言处理应用程序之一。
首先，两名人工注释者手动审查了有目的的训练样本（n = 360）和随机测试样本（n = 1100）的临床记录（包括50%的出院小结和50%的门诊记录），识别伤口病例，并创建了一个金标准数据集。然后，我们训练并测试了我们的自然语言处理系统（称为MTERMS）来处理伤口信息。最后，我们通过将系统生成的结果与金标准进行比较来评估我们的自动化方法。我们还比较了从自由文本数据中识别出的伤口病例的患病率与结构化数据中的编码诊断。
测试数据集包括101份（9.2%）带有伤口信息的记录。总体系统性能良好（F值是系统准确性的综合度量 = 92.7%），伤口治疗方面结果最佳（F值 = 95.7%），伤口大小方面结果最差（F值 = 81.9%）。只有46.5%的伤口记录有伤口诊断的结构化代码。
自然语言处理系统在随机选择的出院小结和门诊记录子集中表现良好。在超过一半的伤口记录中，没有编码的伤口诊断，这突出了使用自然语言处理来丰富临床决策的重要性。我们未来的步骤将包括将应用程序的信息覆盖范围扩展到其他相关伤口因素，并使用外部数据对模型进行验证。
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