Rodríguez Aguilar, Román
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Preferred name
Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
Rodríguez-Aguilar, Román
Rodríguez-Aguilar, R.
Aguilar, R.R.
Rodriguez-Aguiler, R.
Rodríguez, R.
Roman, R.A.
Main Affiliation
ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

Research Output
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Optimization of public resources through an ensemble-learning model to measure quality perception in the social protection system in health of Mexico

2018 , Rodríguez Aguilar, Román , Rivera-Peña, Gustavo

In order to optimize the use of public resources, a model of ensemble learning was proposed to measure the perception of quality in the medical care granted to the people affiliated to the social protection in health system of Mexico. Which allows a more efficient allocation of resources based on the main areas of opportunity identified in the measurement of service quality. Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A satisfaction index was built using an ensemble model using principal component analysis, logistic model and bagging meta-estimator, to identify the effect of a group of factors in the perception of quality of health services and monitor the perceived quality of users in real time. The survey data collected for the “Social Protection System in Health-SPSS 2014” was used, considering a sample of 28,290 users. The proposed index shows, in general, the positive perception of quality of health services, the national average index was of 0.0756, 95% CI [− 9.714 to 2.027]. There are factors statistically significant (P < 0.05) that influence these results, among the most important that can be highlighted is 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 key factors identified that determine the perception of quality allow to define focused strategies and lines of action to improve service quality as well as better allocation of resources. ©Springer, The Authors.

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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

2018 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian

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.

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