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Feasibility of real-time satisfaction surveys through automated analysis of patients′ unstructured comments and sentiments

机译:通过自动分析患者的非结构化注释和情绪来进行实时满意度调查的可行性

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This article shows how sentiment analysis (an artificial intelligence procedure that classifies opinions expressed within the text) can be used to design real-time satisfaction surveys. To improve participation, real-time surveys must be radically short. The shortest possible survey is a comment card. Patients′ comments can be found online at sites organized for rating clinical care, within e-mails, in hospital complaint registries, or through simplified satisfaction surveys such as "Minute Survey." Sentiment analysis uses patterns among words to classify a comment into a complaint, or praise. It further classifies complaints into specific reasons for dissatisfaction, similar to broad categories found in longer surveys such as Consumer Assessment of Healthcare Providers and Systems. In this manner, sentiment analysis allows one to re-create responses to longer satisfaction surveys from a list of comments. To demonstrate, this article provides an analysis of sentiments expressed in 995 online comments made at the RateMDs.com Web site. We focused on pediatrician and obstetrician/gynecologist physicians in District of Columbia, Maryland, and Virginia. We were able to classify patients′ reasons for dissatisfaction and the analysis provided information on how practices can improve their care. This article reports the accuracy of classifications of comments. Accuracy will improve as the number of comments received increases. In addition, we ranked physicians using the concept of time-to-next complaint. A time-between control chart was used to assess whether time-to-next complaint exceeded historical patterns and therefore suggested a departure from norms. These findings suggest that (1) patients′ comments are easily available, (2) sentiment analysis can classify these comments into complaints/praise, and (3) time-to-next complaint can turn these classifications into numerical benchmarks that can trace impact of improvements over time. The procedures described in the article show that real-time satisfaction surveys are possible.
机译:本文展示了情感分析(一种对文本中表达的观点进行分类的人工智能程序)如何用于设计实时满意度调查。为了提高参与度,实时调查必须绝对简短。可能最短的调查是评论卡。可以在组织评估临床护理的网站,电子邮件,医院投诉注册表或简化的满意度调查(例如“分钟调查”)中在线找到患者的评论。情感分析使用单词之间的模式将评论分类为抱怨或称赞。它将投诉进一步分为不满意的具体原因,类似于较长的调查中发现的广泛类别,例如对医疗保健提供者和系统的消费者评估。以这种方式,情感分析允许人们从评论列表中重新创建对更长满意度调查的响应。为了演示,本文对在RateMDs.com网站上进行的995个在线评论中表达的情感进行了分析。我们专注于哥伦比亚特区,马里兰州和弗吉尼亚州的儿科医生和妇产科医生。我们能够对患者不满意的原因进行分类,分析结果提供了有关实践如何改善他们的护理的信息。本文报告注释分类的准确性。随着收到评论的数量增加,准确性也会提高。此外,我们使用下一次投诉的时间对医生进行排名。使用时间间隔控制图来评估下一次投诉时间是否超过了历史模式,因此建议偏离规范。这些发现表明(1)患者的评论很容易获得,(2)情绪分析可以将这些评论分类为抱怨/赞美,(3)下一次诉状可以将这些分类转变为数字基准,从而可以追踪患者的影响。随着时间的推移而有所改善。本文中描述的过程表明可以进行实时满意度调查。

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