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Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System

Rodriguez-Aguilar, R. and Marmolejo-Saucedo, J.A. and Vasant, P. (2019) Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System. Advances in Intelligent Systems and Computing, 866 . pp. 560-572.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

To propose a satisfaction indicator of users of health services affiliated to the Social Protection System in Health (SPSS). Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A machine-learning model based on Logistic Models and Principal Components was developed to estimate a satisfaction index. The survey data collected for the �SPSS 2014 User�s Satisfaction Study� was used, considering a sample of 28,290 users. The proposed model shows, in general, the positive perception of quality of health services (national average 0.0756). There are factors statistically significant that influence these results, the good perception of infrastructure (OR:2.12; CI 95:1.9�2.36); the gratuity of the service provided (OR:1.98; CI 95: 1.42�2.76); and full medicines supply (OR:1.81; CI 95:1.91�2.36). The proposed index can be used as an indicator for improving health care quality of the population covered by the SPSS. © 2019, Springer Nature Switzerland AG.

Item Type:Article
Impact Factor:cited By 0; Conference of International Conference on Intelligent Computing and Optimization, ICO 2018 ; Conference Date: 4 October 2018 Through 5 October 2018; Conference Code:219079
Uncontrolled Keywords:Intelligent computing; Machine components; Surveys, Health surveys; Logistic models; Me-xico; Principal Components; Quality indicators; Satisfaction, Learning systems
ID Code:22268
Deposited By: Ahmad Suhairi
Deposited On:28 Feb 2019 02:48
Last Modified:28 Feb 2019 02:48

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