Likelihood Estimation for Benefit Program Qualification
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Solution Overview
Problem
Current benefit analysis systems do not determine the likelihood of an individual qualifying for a benefit program in real-time, leading to inefficiencies and potential missed benefits, as they do not run profiles against completeness graphs using evaluation algorithms to assess satisfaction of benefit program criteria.
Innovation Solution
A computer-implemented method and system that obtain a profile corresponding to an individual, run it against a set of completeness graphs using an evaluation algorithm to determine likelihood scores, and present relevant benefit programs based on these scores, allowing for real-time determination of qualification and efficient program identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If benefit analysis systems process all benefit programs without likelihood estimation, then comprehensive benefit coverage is achieved, but system complexity and processing time increase significantly
Solution Approach 1:
The system performs preliminary likelihood estimation by running the profile against completeness graphs before full benefit program processing. This preliminary action identifies high-probability benefit programs, allowing the system to focus detailed analysis only on those cases, thereby reducing overall system complexity while maintaining accurate likelihood measurement.
Solution Approach 2:
The benefit analysis process is segmented into two stages: (1) rapid likelihood estimation using completeness graphs and evaluation algorithms, and (2) detailed processing only for high-likelihood programs. This segmentation separates the complex full processing from the initial screening, reducing the complexity burden on the main system while preserving measurement precision through the two-stage approach.
2Measurement precision
If benefit analysis systems evaluate all benefit programs in detail, then accurate benefit identification is achieved, but processing time increases
Solution Approach 1:
The system performs preliminary likelihood estimation using completeness graphs and evaluation algorithms before conducting detailed benefit program evaluation. This preliminary step quickly identifies high-probability matches, allowing the system to skip or reduce detailed processing for low-probability programs, thereby maintaining accurate benefit identification while significantly reducing overall processing time.
Solution Approach 2:
Instead of performing full detailed evaluation on all benefit programs, the system applies partial evaluation (likelihood estimation) to all programs and reserves excessive/detailed action only for high-likelihood cases. This selective application of processing intensity maintains accuracy for important cases while reducing total processing time across the entire benefit program set.
3Loss of information
If the system provides detailed information about all benefit programs, then complete information availability is achieved, but information overload occurs
Solution Approach 1:
The system provides detailed information selectively based on likelihood scores and user needs. High-likelihood benefit programs receive comprehensive detailed information, while low-likelihood programs receive summarized or omitted information. This local quality approach ensures information completeness for relevant cases while improving ease of operation by filtering out irrelevant information that would contribute to overload.
Solution Approach 2:
The system applies partial information provision strategy: complete detailed information is provided only for high-likelihood benefit programs, while partial or no information is provided for low-likelihood programs. This selective information delivery maintains information completeness where it matters most while eliminating information overload from irrelevant programs, thereby improving information usability.
Data Source
AI summary
A computer-implemented method for estimating a likelihood of an individual qualifying for a benefit program includes obtaining a profile corresponding to the individual. The method also includes determining respective likelihoods that the profile would satisfy each completeness graph in a set of completeness graphs by running the profile against each completeness graph using an evaluation algorithm. Each completeness graph corresponds to a respective benefit program.