Historical Message Modeling for Prescription Adherence Pricing
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Solution Overview
Problem
Existing systems struggle to effectively estimate target quantitative measures for prescription drug adherence and profit margins in a network environment, particularly when using cash discount systems, leading to potential non-adherence and reduced pharmacy profit margins.
Innovation Solution
A system and method that utilizes historical electronic messages to partition and model data by geographic areas and demographic indicators, estimating a target quantitative measure for prescription drug adherence while adjusting for profit-related criteria, thereby optimizing pricing to balance adherence and profit margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pricing is adjusted to improve prescription adherence rates, then adherence improves, but pharmacy profit margins may deteriorate
Solution Approach 1:
The system applies different pricing strategies to different geographic areas and demographic groups based on their specific characteristics. By partitioning data by geographic area and modeling adherence based on local demographic factors, the system optimizes pricing locally rather than applying uniform pricing, thus improving adherence without uniformly reducing profits across all locations.
Solution Approach 2:
The system dynamically adjusts pricing parameters based on modeled adherence data and demographic characteristics. By changing price parameters according to predicted adherence responses in different populations, the system finds optimal price points that maximize both adherence and profit rather than using fixed pricing.
2Measurement precision
If historical electronic message data is extensively processed and modeled by geographic areas and demographic indicators, then estimation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments historical electronic message data by geographic areas and further partitions by demographic indicators. This segmentation allows the system to process large volumes of data in manageable groups, improving estimation accuracy for each segment while keeping the overall processing complexity controlled through systematic organization.
Solution Approach 2:
The system uses a universal modeling approach that can handle multiple types of data (geographic, demographic, historical messages) through a single adherence prediction model. This multi-functional model processes diverse data types using consistent methods, reducing the need for separate complex processing systems for each data type.
Data Source
AI summary
A method, apparatus and computer program product are provided to estimate at least one target quantitative measure based upon historical electronic messages. The historical electronic messages may be partitioned based on geographic areas. The target quantitative measure may be estimated based upon respective binary categorical indicators, and respective quantitative measures of the historical electronic messages. The target quantitative measure may be adjusted dependent upon a quantitative criterion of an entity. The target quantitative measure may be used to price prescription drugs and/or the like, and may impact prescription adherence and/or pharmacy profit margins.