Objective:To explore the price formation method and price standard in the big data diagnosis-intervention packet.Methods:The expenditure data and income data of 95 medical institutions in Shanghai in 2018 were used for analysis, including 33 municipal hospitals and 62 district hospitals. After using the standardized data of disease score, the medical institutions in the region were divided into four quadrants with the regional average of unit price per index and cost per index as the coordinate axis. The best quadrant of income and cost was found out, namely the high quality range. The geometric center was calculated in the high quality range, and the unit price per index of the geometric center was taken as the cost standard.Results:For the district hospitals, there were 20 hospitals in the first quadrant, 8 in the second quadrant, 24 in the third quadrant and 10 in the fourth quadrant; For the municipal hospitals, there were 7 hospitals in the first quadrant, 5 in the second quadrant, 12 in the third quadrant and 9 in the fourth quadrant. In the third quadrant, the average income and cost of medical institutions were lower than the average of the city, and the income could cover the cost. The third quadrant was the high quality range. The unit price per index of the third quadrant geometric center of district hospitals was 14 115.4 yuan, and that in municipal hospitals was 15 559.1 yuan, which could be used as the corresponding cost standard.Conclusions:The price discovery mechanism based on objective data and high-quality interval geometric center method can remove the impact of unreasonable charges or unreasonable behavior on medical income, and reflect the guidance of the standard price of medical insurance payment.

One of the challenges to diagnosis-intervention packet is how to detect and avoid the institutional behavior of pursuing a higher score group. Based on the analysis method of big data, the authors analyzed the objective distribution characteristics of the treatment methods corresponding to a diagnosis, and compared the distribution of diseases with high and low scores in the region to find out the selection trend of treatment methods for the same diagnosis in various hospitals. Combined with hospital positioning, the authors found out whether there was a tendency of pursuing a higher score group. Scientific support will be provided for the reasonable payment of medical insurance expenses and the development planning of hospitals.