Semi-supervised Graph Learning for Physician Quality Assessment
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Solution Overview
Problem
Current methods for evaluating the quality of professionals, such as physicians, rely on limited and biased data, often relying on academic credentials and subjective reviews, which are unreliable and do not provide comprehensive insights into a physician's quality, especially when insurance compatibility and patient feedback are considered.
Innovation Solution
A data-driven approach using semi-supervised machine learning and electrostatic modeling to analyze vast amounts of data from various sources, including claims, prescriptions, and patient feedback, to identify good and bad professionals by creating a graph-based system that weights similarities and influences among professionals, allowing for unbiased quality determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to categorize data points, then categorization accuracy is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs preliminary automated categorization using machine learning models to pre-classify data points before manual review. This preliminary action handles the majority of easily classifiable data points, reserving manual intervention only for ambiguous cases, thereby significantly reducing time consumption while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical categorization with automated machine learning systems. The ML models process and categorize data points algorithmically, substituting human analysts for the majority of categorization tasks, thus dramatically reducing time and resource requirements while maintaining consistent accuracy standards.
2Loss of information
If more data sources are integrated into the analysis system, then conclusion comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple data sources (claims data, prescription data, patient feedback, academic credentials) through a single integrated machine learning system. This multi-functional architecture processes diverse data types uniformly, achieving comprehensive conclusions without proportionally increasing system complexity.
Solution Approach 2:
The system introduces intermediate processing layers including data normalization modules, feature extraction components, and standardized data representation formats. These intermediaries translate various data sources into a unified structure, enabling comprehensive analysis while managing complexity through modular design and standardized interfaces.
Data Source
AI summary
Systems and methods are provided for data driven analysis, modeling, and semi-supervised machine learning for qualitative and quantitative determinations. The systems and methods include obtaining data associated with individuals, and determining features associated with the individuals based on the data and similarities among the individuals based on the features. The systems and methods can label some individuals as exemplary, generate a graph wherein nodes of the graph represent individuals, edges of the graph represent similarity among the individuals, and nodes associated labeled individuals are weighted. The disclosed system and methods can apply a weight to unweighted nodes of the graph based on propagating the labels through the graph where the propagation is based on influence exerted by the weighted nodes on the unweighted nodes. The disclosed systems and methods can provide output associated with the individuals represented on the graph and the associated weights.