Design a Predictive Analytics Model of Hospital Accreditation Continuity from Employee Readiness based on Artificial Intelligence

Alimunir Gultom, Ermi Girsang, Sri Lestari R. Nasution

2020

Abstract

Hospitals are required to have an accreditation in an effort to improve the quality of health services. Some employees feel accreditation is a workload and they are not ready to carry out the sustainability of hospital accreditation. The purpose of this study is to design a predictive analytics model of hospital accreditation continuity from employee readiness using support vector machine (SVM) method. The data were obtained from a population of 230 employee with sample of 70 respondents. Pembagian kuisioner?. Statistically, measurement data from the questionnaire was processed using univariate, bivariate with chi-square tests, and multivariate with multiple logistic regression at a 95% confidence level (α = 0.05). For hospital application needs, measurement data are modeled by the SVM method. The results showed that there was a relationship between readiness to change, management support, and self-benefits to the sustainability of hospital accreditation p <0.05. The variable that has the greatest relationship with the sustainability of hospital accreditation is management support with the value Exp (B) / OR = 18.978. The results of predictive model between input and output variables show significant success with an accuracy rate of 88.6%.

Paper Citation

in Harvard Style

Gultom A., Girsang E. and R. Nasution S. (2020). Design a Predictive Analytics Model of Hospital Accreditation Continuity from Employee Readiness based on Artificial Intelligence.In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP, ISBN 978-989-758-500-5, pages 96-103. DOI: 10.5220/0010289300960103

in Bibtex Style

@conference{himbep20,
author={Alimunir Gultom and Ermi Girsang and Sri Lestari R. Nasution},
title={Design a Predictive Analytics Model of Hospital Accreditation Continuity from Employee Readiness based on Artificial Intelligence},
booktitle={Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP,},
year={2020},
pages={96-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010289300960103},
isbn={978-989-758-500-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical - Volume 1: HIMBEP,
TI - Design a Predictive Analytics Model of Hospital Accreditation Continuity from Employee Readiness based on Artificial Intelligence
SN - 978-989-758-500-5
AU - Gultom A.
AU - Girsang E.
AU - R. Nasution S.
PY - 2020
SP - 96
EP - 103
DO - 10.5220/0010289300960103