Volume 4, Issue 4 (3-2022)
Iran J Health Insur 2022, 4(4): 269-279 |
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1- Faculty of Progress Engineering, University of Science and Technology, Tehran, Iran
2- Faculty of Progress Engineering, University of Science and Technology, Tehran, Iran , fathian@iust.ac.ir
2- Faculty of Progress Engineering, University of Science and Technology, Tehran, Iran , fathian@iust.ac.ir
Abstract: (1610 Views)
Introduction: Accurate funding in order to better manage costs is one of the main concerns of managers. The Health Insurance Organization of Iran, as one of the largest basic insurance organizations, is no exception to this and certainly needs to identify and accurately predict the costs of treatment in order to provide financial resources and obtain the necessary funds in its field of treatment. Using machine learning methods to create a model for predicting treatment costs can be a great help in accurately financing.
Methods: This study has provided a model and method for predicting the costs of the organization by using the cost data available in the medical documentation systems of the provinces of the organization during the years 2007 to 2020 and using the SARIMAX and LSTM methods. This method can help to more accurately predict the costs of the organization.
Results: Determining the method with better performance based on the MAPE index alone did not meet the desired model; therefore, by creating a combined method and using the criterion of percentage of realization of the forecast, the optimal model for cost forecasting is presented.
Conclusion: Due to the need for a scientific method to more accurately predict the costs of the organization, the proposed method and model was able to predict the costs of the organization with minimal errors compared to the errors accepted in manual processes.
Methods: This study has provided a model and method for predicting the costs of the organization by using the cost data available in the medical documentation systems of the provinces of the organization during the years 2007 to 2020 and using the SARIMAX and LSTM methods. This method can help to more accurately predict the costs of the organization.
Results: Determining the method with better performance based on the MAPE index alone did not meet the desired model; therefore, by creating a combined method and using the criterion of percentage of realization of the forecast, the optimal model for cost forecasting is presented.
Conclusion: Due to the need for a scientific method to more accurately predict the costs of the organization, the proposed method and model was able to predict the costs of the organization with minimal errors compared to the errors accepted in manual processes.
Keywords: Iran Health Insurance Organization, Medical Expenditure, Forecast, Machine Learning, Time Series
Type of Study: Research |
Subject:
Special
Received: 2021/02/13 | Revised: 2022/07/2 | Accepted: 2021/02/14 | ePublished: 2022/03/9
Received: 2021/02/13 | Revised: 2022/07/2 | Accepted: 2021/02/14 | ePublished: 2022/03/9
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