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首页> 外文期刊>Nursing research >A new look at patient satisfaction: Learning from self-organizing maps
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A new look at patient satisfaction: Learning from self-organizing maps

机译：病人满意度的新外观：从自组织地图中学习

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BACKGROUND: To some extent, results always depend on the methods used, and the complete picture of the phenomenon of interest can be drawn only by combining results of different data processing techniques. This emphasizes the use of a wide arsenal of methods for processing and analyzing patient satisfaction surveys. OBJECTIVE: The purpose of this study was to introduce the self-organizing map (SOM) to nursing science and to illustrate the use of the SOM with patient satisfaction data. The SOM is a widely used artificial neural network suitable for clustering and exploring all kind of data sets. METHODS: The study was partly a secondary analysis of data collected for the Attractive and Safe Hospital Study from four Finnish hospitals in 2008 and 2010 using the Revised Humane Caring Scale. The sample consisted of 5,283 adult patients. The SOM was used to cluster the data set according to (a) respondents and (b) questionnaire items. The SOM was also used as a preprocessor for multinomial logistic regression. An analysis of missing data was carried out to improve the data interpretation. RESULTS: Combining results of the two SOMs and the logistic regression revealed associations between the level of satisfaction, different components of satisfaction, and item nonresponse. The common conception that the relationship between patient satisfaction and age is positive may partly be due to positive association between the tendency of item nonresponse and age. DISCUSSION: The SOM proved to be a useful method for clustering a questionnaire data set even when the data set was low dimensional per se. Inclusion of empty responses in analyses may help to detect possible misleading noncausative relationships.
机译：背景技术：在某种程度上，结果始终取决于所使用的方法，并且只有结合不同数据处理技术的结果，才能得出感兴趣现象的完整图片。这强调了使用大量方法来处理和分析患者满意度调查。目的：本研究的目的是将自组织图（SOM）引入护理科学领域，并通过患者满意度数据说明SOM的使用。 SOM是一种广泛使用的人工神经网络，适用于聚类和探索各种数据集。方法：本研究部分使用修订后的人文关怀量表，对2008年和2010年从芬兰四家医院进行的吸引力和安全医院研究收集的数据进行了二次分析。样本包括5,283名成年患者。 SOM用于根据（a）受访者和（b）问卷调查项目对数据集进行聚类。 SOM还用作多项逻辑回归的预处理器。对丢失的数据进行了分析，以改善数据解释。结果：两种SOM的结果和Logistic回归相结合，揭示了满意度水平，满意度的不同组成部分和项目无响应之间的关联。患者满意度和年龄之间呈正相关关系的普遍观念可能部分归因于项目无反应趋势和年龄之间呈正相关关系。讨论：即使数据集本身是低维的，SOM也被证明是用于对问卷数据集进行聚类的一种有用方法。分析中包括空响应可能有助于检测可能引起误解的非因果关系。

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