Towards A Patient Satisfaction Based Hospital Recommendation System

Surveys are used by hospitals to evaluate patient satisfaction and to improve operation. Collected satisfaction data is usually represented to the hospital administration using statistical charts and graphs. Although this statistical data and visualization is helpful, but because of the size and dimension of the dataset, it is very difficult if not impossible, to identify important factors that could be evaluated and improved for better patient satisfaction. This work presents an unsupervised data-driven methodology that discovers the specific issues reflected by the dataset from the patients' point of view. The goal of the introduced exploratory data analysis methodology is to determine hidden patterns in the dataset and to identify the main causes of dissatisfaction. To this end, two layers of data analysis is performed. In the first layer, the analysis is only performed on the satisfaction questions. The analysis consists of handling the high dimensionality using self-organizing maps, grouping similar patients using clustering methods and labeling each cluster according to their salient features. In the second layer, demographic data of patients of each cluster is fed to the same analysis process. Putting the salient features of a cluster and its sub-clusters together, one can extract correlations. The correlations are validated using multiple statistical methods applied to the dataset. In a following work, the correlations extracted using this methodology will be ranked and converted to recommendations that can be used by healthcare providers as well as patients.