Machine Learning Bid Optimization for Medicare Advantage Plans
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Solution Overview
Problem
Medicare Advantage Organizations (MAOs) face challenges in generating accurate and optimized bids for Medicare Advantage plans due to the complexity of factors involved, such as drug costs, administrative fees, and member enrollment, within a limited time frame, which can lead to inaccurate predictions and resource inefficiencies.
Innovation Solution
The implementation of machine-learning techniques that allow for iterative testing of multiple scenarios and assumptions, compartmentalizing the bid calculation process, and identifying independent and dependent variables to optimize bid components, thereby determining the impact of various assumptions on bid submissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bid calculation methods are used with multiple assumptions about member health, premiums, and costs, then comprehensive bid submissions can be generated, but accuracy and reliability of predictions deteriorate due to the complexity and limited testing of assumptions
Solution Approach 1:
The patent segments the bid calculation process into distinct components: assumption generation, scenario creation, machine learning model training, and bid optimization. Each component is handled separately with dedicated computational resources and algorithms, allowing complex bid calculations to be broken down into manageable segments that can be tested and validated independently, thereby improving prediction accuracy without overwhelming system complexity
Solution Approach 2:
The patent replaces traditional mechanical bid calculation methods with machine learning models. Instead of relying on manual assumption testing and deterministic calculations, the system uses trained ML models that have learned patterns from historical data, substituting the mechanical calculation process with intelligent prediction systems that handle complexity more efficiently while improving reliability
2Measurement precision
If extensive testing of multiple assumptions and scenarios is performed, then bid accuracy improves, but time consumption and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on historical bid data and assumption outcomes before the actual bid submission process. This advance preparation allows the system to quickly evaluate new assumptions during bid generation without performing extensive testing from scratch, thereby improving accuracy while reducing time loss during the critical bid submission window
Solution Approach 2:
The patent changes parameters by using machine learning models to evaluate multiple assumption scenarios in parallel rather than sequentially testing each assumption. The system varies assumption parameters (member health, premiums, costs) simultaneously across many scenarios using the trained model, achieving comprehensive accuracy assessment without the linear time cost of traditional sequential testing
3Productivity
If machine learning techniques are used to test multiple scenarios and optimize bid components, then bid submission accuracy and efficiency improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies universality by designing a multi-functional machine learning system that performs multiple bid optimization tasks: generating assumptions, creating scenarios, training models, evaluating bids, and identifying key影响因素. This single unified system handles diverse bid-related functions that would otherwise require separate tools and processes, improving productivity while managing system complexity through consolidation rather than proliferation of separate systems
Data Source
AI summary
A method including determining a first bid for a healthcare plan and a second bid for the healthcare plan. A bid-pricing tool is configured to utilize one or more first components to determine the first bid and one or more second components to determine the second bid, respectively. A bid difference between the first bid and the second bid includes at least a monetary difference in premiums paid by members of the first bid for the healthcare plan and members of the second bid for the healthcare plan. One or more third components are determined based at least in part on the bid difference to determine a third bid for the healthcare plan. A second bid difference is determined between at least one of the first bid and the third bid or the second bid and the third bid.