###### Abstract  
Surveys are used by hospitals to evaluate patient satisfaction and to improve general hospital operations. Collected satisfaction data is usually represented to the hospital administration by using statistical charts and graphs. Although such visualization is helpful, typically no deeper data analysis is performed to identify important factors which contribute to patient satisfaction. This work presents an unsupervised data-driven methodology for analyzing patient satisfaction survey data. The goal of the proposed exploratory data analysis is to identify patient communities with similar satisfaction levels and the major factors, which contribute to their satisfaction. This type of data analysis will help hospitals to pinpoint the prevalence of certain satisfaction factors in specific patient communities or clusters of individuals and to implement more proactive measures to improve patient experience and care. To this end, two layers of data analysis is performed. In the first layer, patients are clustered based on their responses to the survey questions. Each cluster is then labeled according to its salient features. In the second layer, the clusters of first layer are divided into sub-clusters based on patient demographic data. Associations are derived between the salient features of each cluster and its sub-clusters. Such associations are ranked and validated by using standard statistical tests. The associations derived by this methodology are turned into comments and recommendations for healthcare providers and patients. Having applied this method on patient and survey data of a hospital resulted in 19 recommendations where 10 of them were statistically significant with chi-square test's \(p\)-value less than 0.5 and an odds ratio z-test's \(p\)-value of more than 2 or less than \(-2\). These associations not only are statistically significant but seems rational too. health data analytics; survey analysis; HCAHPS; hospital consumer assessment of healthcare providers and systems; unsupervised learning +
Footnote †: journal: Medical  
percentage of the patients that have rated each hospital's services similarly or at most provide some correlations between specific groups of patients and their answers to a specific satisfaction question. For improving patient satisfaction, issues of health care provided at the hospital level and the factors that originate those issues from patients' point of view should be discovered. Therefore, survey data should be either manually analyzed by examining each possible pattern in the data set using conventional methods or an unsupervised methodology is needed to do the analysis with least amount of human interaction. Such methodology should get the satisfaction survey data, find patterns that are repeated among patients' demographics and their satisfaction level in different fields, validate the patterns and compile them into a set of recommendations to help hospitals improve satisfaction within various patient communities. To this end, a new hybrid methodology is proposed that differs from these conventional approaches in that it is not bound to a single outcome or dependent variable. The focus of this approach is to find patterns in patients' responses to all satisfaction questions and relate them to patients' demographics. The proposed methodology is focused on discovering issues of the health care provided at the hospital level and the factors that originate those issues from the patients' point of view. This methodology is a hybrid unsupervised clustering-labeling method, which finds associations between various levels of patients' satisfaction and demographics. The associations are validated by using standard statistical models and turned into useful recommendations for hospitals in order to improve patients' experience, save cost, and build long-term patient loyalty. The methodology can be generalized to any complex multi-level survey analysis. The article is organized into eight sections.