Medical Insurance Statistical Analysis Project for a Provincial Medical Insuranc - case

Client Profile

The provincial medical insurance institute actively responds to national policies and is committed to solving medical livelihood issues, especially the phenomenon of over-treating minor illnesses and the medical expenditure issues of impoverished patients. Their goal is to adjust the policy on charging items, plan medical resources rationally, ensure the effective use of resources, and enhance the effectiveness of medical services.

Business Challenges

Reasonable allocation of medical resources: How to ensure that medical resources are used for patients in need and avoid waste.

Control of medical service costs: How to control medical service costs while reducing the rate of drug markups to avoid overburdening patients.

Balancing medical quality and cost: How to ensure the quality of medical services while reducing costs as the cost of diagnosis and treatment services increases.

Rational formulation of medical insurance policies: How to formulate medical insurance policies reasonably based on changes in medical resources and costs.

Solution

Establish a dynamic monitoring mechanism for price indices:
Use SASVA to construct a medical insurance price index, analyze trends in the price changes of medical service costs, and provide a basis for medical insurance strategies.

Management of classification of diseases:
Classify diseases using a cumulative algorithm to optimize the allocation of medical insurance resources.

Data-driven decision support:
Combine data standardization and machine learning technology to provide comprehensive statistical analysis to support decision-making and resource optimization.

Solution Advantages

Dynamic price monitoring: Establish a medical insurance price index model to monitor changes in medical service costs in real-time, providing data support for medical insurance policies.
Hierarchical management of diseases: Use advanced classification algorithms to achieve disease clustering, optimize the allocation of medical insurance resources, and improve the efficiency of resource utilization.
Data-Driven Decision-Making: Combining data modeling and machine learning techniques to provide comprehensive statistical analysis, supporting scientific and efficient decision-making.

Client Value

Optimization of medical resources:

Ensure that medical resources are more effectively served to patients in need through reasonable medical insurance policies and resource allocation.

Improvement of medical service quality:

Encourage hospitals to strengthen the management of medical quality, improve service quality, and promote the development of the medical industry towards a more scientific and efficient direction.

Control of medical costs:

Reduce medical costs and alleviate the burden on patients by optimizing resource allocation and improving the efficiency of medical services.

Scientific formulation of medical insurance policies:

Provide a scientific basis for the formulation of medical insurance policies based on in-depth data analysis and mining, addressing the evaluation criteria for medical efficiency and benefits.