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The purpose of this research is to analyze the causes of potential failure, failure modes and effects models in the operational data system. We use failure mode and effect analysis theory based on qualitative data analysis for an assessment from in-depth interviews with experts from the healthcare organization utilizing non-probability random sampling and purposive sampling The data operating system process in healthcare organization, according to the research findings, consists of 4 factors includes databases, data management, staff, and data utilization. As a result, potential failure, failure modes and effects models were 19 variables identified when considering the values S, O, and D, as well as the risk priority number. Then we analyze the outcomes of the factors obtained by identifying solutions and taking actions to improve the data operational system of the healthcare organization by displaying the results: 1) The database by setting up a database to manage the data the center collects. A database system exists that enables multiple users to work simultaneously. 2) Data management includes managing data structures for input and output data in the input-processoutput pattern, where there is a relationship between the information and a clear connection between various pieces of information. 3) Staff should be the framework, scope, and division of the work must all be clearly stated. However, the operation needs a mentor to help it work and guide activities on the right path including training employees talents and acquiring their experience in the following duties. 4) Data utilization development of people operating systems has modernized the use of information. Create a data operating system so that data may be examined, distilled, and grouped before use. To suit user needs, provide a system with staff that can input and export data. Along with the use of technology to generate data to import and export data consistently, and the systematic of the data by combining the necessary data to analyze the data. Furthermore, the study outcomes can be reinforced to assist organizations to see the whole link between the data operating system front end and back end processes. [ABSTRACT FROM AUTHOR]

The purpose of this research is to analyze the causes of potential failure, failure modes and effects models in the operational data system. We use failure mode and effect analysis theory based on qualitative data analysis for an assessment from in-depth interviews with experts from the healthcare organization utilizing non-probability random sampling and purposive sampling The data operating system process in healthcare organization, according to the research findings, consists of 4 factors includes databases, data management, staff, and data utilization. As a result, potential failure, failure modes and effects models were 19 variables identified when considering the values S, O, and D, as well as the risk priority number. Then we analyze the outcomes of the factors obtained by identifying solutions and taking actions to improve the data operational system of the healthcare organization by displaying the results: 1) The database by setting up a database to manage the data the center collects. A database system exists that enables multiple users to work simultaneously. 2) Data management includes managing data structures for input and output data in the input-processoutput pattern, where there is a relationship between the information and a clear connection between various pieces of information. 3) Staff should be the framework, scope, and division of the work must all be clearly stated. However, the operation needs a mentor to help it work and guide activities on the right path including training employees talents and acquiring their experience in the following duties. 4) Data utilization development of people operating systems has modernized the use of information. Create a data operating system so that data may be examined, distilled, and grouped before use. To suit user needs, provide a system with staff that can input and export data. Along with the use of technology to generate data to import and export data consistently, and the systematic of the data by combining the necessary data to analyze the data. Furthermore, the study outcomes can be reinforced to assist organizations to see the whole link between the data operating system front end and back end processes. [ABSTRACT FROM AUTHOR]