Economics & Sociology

ISSN: 2071-789X eISSN: 2306-3459 DOI: 10.14254/2071-789X
Index PUBMS: f5512f57-a601-11e7-8f0e-080027f4daa0
Article information
Title: Evaluation of selected indicators of patient satisfaction and economic indices in OECD country
Issue: Vol. 12, No 4, 2019
Published date: 12-2019 (print) / 12-2019 (online)
Journal: Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459
Authors: Robert Stefko
University of Prešov in Prešov, Presov, Slovakia

Beata Gavurova
Technical University of Kosice, Kosice, Slovakia

Martin Rigelsky
Technical University of Kosice, Kosice, Slovakia

Viera Ivankova
Technical University of Kosice, Kosice, Slovakia
Keywords: quality of healthcare, patient satisfaction, OECD, global innovation index, human development index, relations analysis, cluster analysis
DOI: 10.14254/2071-789X.2019/12-4/9
Index PUBMS: 5c6076f4-3712-11ea-b360-fa163e0fa1a0
Language: English
Pages: 149-165 (17)
JEL classification: I14, I15
This work is supported by the Scientific Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic and the Slovak Academy Sciences as part of the research project VEGA 1/0846/18: Evaluation of the Efficiency of Public Procurement of Selected Commodities in Healthcare facilities in the Slovak Republic.

The primary aim of the research is to estimate the relationship between healthcare indicators and economic indices in the selected OECD countries. The goal was met in three main procedures. Firstly, a cluster of the selected countries was identified, then a relationship analysis was realized on eight variables that determined satisfaction and economic indices (Global Innovation Index - GII, Human Development Index - HDI) and at the end, regression analysis which calculates the impact of innovation activity (GII) on healthcare satisfaction was used (the mean of 8 healthcare satisfaction variables – MeanHC) as well as the impact of this variable on maturity (HDI) in the selected OECD countries (the sample of 33 countries). The cluster was estimated by using the method based on Euclidean distance, and the number of clusters was defined by Silhouette method. Two clusters were computed. Also, the coefficients such as Spearman ρ and Pearson r were used in analysis of this relationship. A significant relationship is not evident only in the variable that determined cost satisfaction. The differences between clusters in the individual satisfaction variables were confirmed by t-test, Welch test and Wilcoxon test. A significant difference between the clusters is in satisfaction with staff friendliness. Regression analysis was computed by means of using a method based on Least Trimmed Squares Robust Regression. Significant impact was reflected in both assumptions (GII to MeanHC and also MeanHC to HDI).


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