Business background

In recent years, with the continuous deepening of online medical procedures, electronic medical records, and hospital informatization, smart medical care based on big data has gradually become possible. If medical data can be used properly, it will not only improve the level of medical research, but also It can improve the level of public health services and make up for the shortcomings of primary diagnosis and treatment capabilities.

However, medical data has the characteristics of extremely strong privacy, and the safety of medical data is often related to the safety of people's lives and properties. At present, massive amounts of medical data are scattered in isolated medical institutions. Under traditional methods, data security and privacy protection requirements make it impossible to aggregate these data, and the data sample size of a single medical institution is not enough to support large-scale models. Training, it is difficult to realize the value of data.

Customer demand

• Support multiple privacy computing modes between medical institutions (horizontal) and multi-industry (vertical)
• Convenient maintenance and batch deployment

Solutions

Joint Modeling Solution for Medical Institutions

Using privacy computing technology, it is possible to realize joint modeling of data from various medical institutions without leaving the database. For example, patients often undergo some standardized tests before they are diagnosed, and the test results are less affected by the operators. They can rely on equipment and standard procedures for standardization, so as to obtain standardized medical data. The introduction of privacy computing technology can further realize the joint modeling of laboratory examination data of different medical institutions. Through joint modeling and sharing of models, even a hospital with a small number of cases can obtain a better model. The model can be used to assist doctors in diagnosis, which can help doctors improve the accuracy of diagnosis while reducing the workload of doctors.

Health insurance underwriting scheme based on privacy calculation

The insurance company needs to obtain the insured's diagnosis and treatment data from the Health Commission, the Medical Insurance Bureau or the hospital, calculate the insured's risk score based on expert rules and underwriting models, and make a conclusion of "rejection, extension, fee increase, and exclusion". RongAn Privacy Gateway converts insurance company and medical institution data into ciphertext, with built-in common underwriting rules and models, supports seamless switching to ciphertext calculation, effectively prevents data abuse risks, and protects personal privacy data.

Value

• Provide privacy rule engine, support industry expert rule configuration
• Provide hardware deployment methods to reduce deployment and maintenance costs