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通过对患者非结构化评论和情绪进行自动分析开展实时满意度调查的可行性。

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

作者信息

Alemi Farrokh, Torii Manabu, Clementz Laura, Aron David C

机构信息

Department of Health Systems Administration, Georgetown University, Washington, District of Columbia 20007, USA.

出版信息

Qual Manag Health Care. 2012 Jan-Mar;21(1):9-19. doi: 10.1097/QMH.0b013e3182417fc4.

Abstract

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.

摘要

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。为提高参与度,实时调查必须极度简短。可能最短的调查就是意见卡。患者的意见可以在为临床护理评级而设立的网站上、电子邮件中、医院投诉登记处找到,或者通过诸如“分钟调查”这样的简化满意度调查获取。情感分析利用词之间的模式将一条意见分类为投诉或赞扬。它还将投诉进一步分类为具体的不满原因,类似于在诸如《医疗服务提供者和系统消费者评估》等较长调查中发现的宽泛类别。通过这种方式,情感分析使人们能够根据一系列意见重新创建对较长满意度调查的回应。为进行演示,本文对RateMDs.com网站上995条在线意见中表达的情感进行了分析。我们关注的是哥伦比亚特区、马里兰州和弗吉尼亚州的儿科医生以及妇产科医生。我们能够对患者的不满原因进行分类,并且该分析提供了有关医疗实践如何改进其护理的信息。本文报告了意见分类的准确性。随着收到的意见数量增加,准确性将会提高。此外,我们使用下次投诉时间的概念对医生进行排名。使用时间间隔控制图来评估下次投诉时间是否超过历史模式,从而表明偏离了规范。这些发现表明:(1)患者的意见很容易获取;(2)情感分析可以将这些意见分类为投诉/赞扬;(3)下次投诉时间可以将这些分类转化为数值基准,从而能够追踪随着时间推移改进措施的影响。本文中描述的程序表明实时满意度调查是可行的。

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