Affiliations: [a] School of Public Health, Fudan University, Shanghai, China | [b] Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China | [c] School of Computer Science and Technology, Fudan University, Shanghai, China | [d] School of Software, Fudan University, Shanghai, China
Correspondence:
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Corresponding author: Li Luo, School of Public Health, Fudan University, Shanghai, China. E-mail: schuster_ter@163.com.
Note: [1] These authors are co-first authors of the article.
Abstract: BACKGROUND: To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgement model by designing a virtuous cycle of inpatient cost supervision information system and exploring a complete set of inpatient cost supervision methods. OBJECTIVE: To lay the foundation for applying artificial intelligence (AI) technology in medical insurance cost control supervision and provide feasible paths and available tools for medical insurance cost control managers. METHODS: By way of collecting and cleaning electronic medical record (EMR) data from 2016 to 2018 of a city in East China, focusing on basic patient information and cost information, and using a combination of machine learning modeling and information system construction, the study tries to form a feasible inpatient cost supervision method and operation path. RESULTS: The set of the regulatory method, applied in nursing homes of a city in East China, is compelling. The accuracy rates of rationality judgement in different main diseases are stable up to 80%, the false positive rate is steady within 10%, and rehabilitation fee days of hospitalization, and the number of complications are important factors affecting the rationality of the inpatient cost. CONCLUSION: The model construction and optimization method combining machine learning and information system can make practical cost rationality judgement on medical institution’s inpatient cost data, which can directly reflect the key influencing factors of relevant inpatient costs, and achieve the effect of guiding medical behavior and improving the efficiency of medical insurance fund use.
Keywords: EMR, cost supervision, medical insurance fund, machine learning, information system
