Graph-Based Recommendation System for Prior Authorization
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Solution Overview
Problem
Existing decision-making systems for prior authorization cases are inefficient and subjective, requiring manual analysis and relying on rule-based methods that are time-consuming and prone to inconsistencies, often limited to examining case features in isolation.
Innovation Solution
The system generates decision recommendations by traversing a graph-based data structure to analyze historical case cohorts, dynamically selecting a subgraph-based data structure that identifies similar historical cases, allowing for automated, consistent, and self-learning recommendations based on historical decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and rule-based methods are used for decision-making, then decision accuracy can be maintained through human judgment, but decision-making time is excessively long and productivity is low
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated graph-based computational system. The system traverses graph data structures to automatically analyze historical cases and generate recommendations, eliminating the need for manual rule-based decision-making while maintaining consistency and speed.
Solution Approach 2:
The patent creates a computational model that copies and analyzes historical decision patterns through graph traversals. By representing historical cases as graph data structures and traversing them systematically, the system replicates expert decision-making logic at machine speed without requiring actual human judgment for each new case.
2Stability of the object's composition
If rule-based methods are used for decision-making, then consistency can be maintained through predefined rules, but the system becomes rigid and cannot adapt to new situations
Solution Approach 1:
The patent implements a dynamic system where the graph data structures and traversal paths are generated adaptively based on the specific characteristics of each input case. The system dynamically selects which historical cases to retrieve and how to traverse the graph, allowing it to maintain consistency through systematic analysis while adapting to new situations through flexible query generation.
Solution Approach 2:
The patent segments the decision-making process into distinct graph traversal operations. By breaking down the analysis into manageable graph traversal steps (node selection, edge traversal, result aggregation), the system maintains methodological consistency while being able to adapt the traversal depth and scope to different case complexities.
3Measurement precision
If comprehensive historical case analysis is performed, then recommendation accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent extracts only the relevant portions of historical cases needed for comparison by selectively traversing specific paths in the graph data structure. Instead of analyzing all historical cases comprehensively, the system extracts and compares only the features and patterns relevant to the current input case, reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary organization of historical cases into graph data structures during off-processing periods. By pre-building the graph representations of historical cases and their relationships, the system reduces the complexity of real-time analysis, as the heavy lifting of structuring the data has already been completed in advance.
4Adaptability or versatility
If manual decision-making processes are used, then subjective judgment can be applied to complex cases, but subjectivity leads to inconsistencies and lack of reliability
Solution Approach 1:
The patent replaces subjective human judgment with objective graph-based computational analysis. The system systematically traverses graph data structures representing historical cases and applies consistent comparison logic, eliminating subjective biases while maintaining the ability to handle complex cases through comprehensive pattern recognition.
Data Source
AI summary
Various embodiments of the present disclosure provide methods, apparatus, systems, computing entities, and/or the like, for providing a recommendation of a decision for an input case. According to some aspects, a method is provided. The method generally comprises receiving an input case data object; generating an input feature data object based at least in part on input case features of the input case data object; selecting a subgraph-based data structure from a graph-based data structure, the subgraph-based data structure identifying a subset of historical case data objects; generating a feature data model based at least in part on traversing the subgraph-based data structure; selecting a historical case cohort based at least in part on comparing the feature data model with the input feature data object; and providing a recommendation data object based at least in part on the historical case cohort.