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| Research on the forecast of income in large comprehensive public hospital based on grey prediction and artificial neural network model |
| Zhang Zhongwen,Wang Jiu, Sun Hongwei, Meng Jinliang |
| School of Public Health and Management, Binzhou Medical University, Yantai 264003, China |
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Abstract Objective To comprehensively analyze the influencing factors of hospital business income, and accurately forecast hospital revenues, thus, to provide references for the hospital to compile budget, formulate long-term development strategy, carry out infrastructure construction plan, and promote the development of medical and health undertakings.Methods The related data of a regional medical center and statistical yearbook of shandong province was used, and the influencing factors of in-patient income in public hospitals were selected by using the Lasso variable selection method. The influences of the hospital's own development index and the related indexes of national economy and social development on the income of hospital were analyzed, and grey prediction and artificial neural network model were used to predict the inpatient income of large public hospitals.Results According to the variable selection, the indicators included in the forecast model of hospital income were regional GDP, number of people at the end of the year, number of hospital admissions, number of hospital operations and number of bed turnover. The increase of regional GDP, number of people at the end of the year, number of hospital admissions, and number of hospital operations helped improve the hospital income. The decrease of bed turnover was beneficial to the increase of the income of large public hospitals. According to the grey prediction, the relative accuracy of each prediction index was above 90%, and the predicted value of hospital inpatient income in 2016 was 245 003 by the artificial neural network.Conclusion The combined model of grey prediction and artificial neural network can well predict the income of inpatient in public hospitals. This model takes into account several influencing factors of the predicted variables, therefore, compared with the simple grey prediction method, it is more robust.
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Received: 28 August 2018
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