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Health insurance big data not only provide real-world evidence of unmet needs in actual clinical practice but also of breakthroughs in the medical industry which will shape the future of health care. Big data are also expected to transform the existing medical paradigm and provide a truly personalized medical age. However, questions about research through the collection and utilization of various big data in various fields have also been raised because quality limitations cannot be overlooked. Therefore, many challenges remain to be overcome in the use of big data research as a basis for changing medical practice. Intervention and interpretation by clinical medical experts are required in judging the scientific trustworthiness of the big data analysis process and the validity of the results. Therefore, healthcare big data research cannot achieve its goal by the efforts of researchers alone. Teams of data analysis scientists, epidemiologists, statistics experts, and clinical researchers are required to collaborate closely with team members, from the design phase to expert consultation, through regular meetings. In addition, it is necessary, in the creation of a healthier community, to cooperate with government agencies that provide data based on the whole nation or the world's population, as well as interest groups representing the people, and policy-making organizations. In this paper, we describe the knowledge, practical clinical applications, and future research directions and prospects for the next phase of health care, from the design of clinical research using health insurance big data to report writing. (Korean J Med 2019;94:463-470)

Generally, functional design of FPSO has been done by the engineering firm designated by ship owner. Main equipment such as topside facility is imported from abroad. But sometimes, OFE (Owner Furnished Equipment) does not satisfy the PND (Product Need Date) of each production stage because the delivery date of OFE is not scheduled to satisfy the PND. And sometimes many loose items and equipment are complex from engineering which does not consider pre-outfitting. Main objective of this study is process improvements by maximizing pre-outfitting rate in the stage of equipping STP (Submerged Turret Production), one of main equipment on FPSO. In this study, we analyzed the factors which obstructed pre-equipping STP using the past records of FPSO projects.