Buy this Article for $10.95

Have a coupon or promotional code? Enter it here:

When you buy this you'll get access to the ePub version, a downloadable PDF, and the ability to print the full article.

Keywords

data mining, Finland, missing data, patient satisfaction, self-organizing map

 

Authors

  1. Voutilainen, Ari
  2. Kvist, Tarja
  3. Sherwood, Paula R.
  4. Vehvilainen-Julkunen, Katri

Abstract

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.