Medication Classification Scoring Using Extracted Predictor Variables
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Solution Overview
Problem
Current systems lack a standardized metric to objectively classify specialty medications and emerging therapies, leading to inconsistent and time-consuming decision-making processes for health plan members, increased costs, and variability in coverage and policy implementation.
Innovation Solution
A system utilizing machine learning, agentic AI, and advanced decisioning to automatically determine medication classifications based on extracted predictor variables, providing a score indicating the degree or probability of association with specialty or clinical complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual research and monitoring is conducted by users within each organization, then individual organizational control is maintained, but the process becomes time-consuming and produces biased, incomplete, and inconsistent information
Solution Approach 1:
The patent implements a centralized medication classification system that serves multiple health plans and organizations simultaneously. The system extracts predictor variables from diverse data sources, applies standardized machine learning models, and generates consistent classifications across different organizations, eliminating the need for each organization to conduct separate manual research while maintaining universal applicability.
Solution Approach 2:
The patent replaces manual mechanical processes of research, data collection, and classification with an automated computer-based system. The system uses machine learning models to automatically extract predictor variables from databases, compute classification scores, and generate standardized medication classifications, substituting human effort with computational processes that are faster and more consistent.
2Measurement precision
If standardized medication classification systems are implemented, then consistency and accuracy improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the medication classification process into distinct modular components: data extraction from multiple databases, predictor variable identification, machine learning model application, score computation, and classification generation. Each component is independently implemented and can be maintained separately, reducing overall system complexity while maintaining high measurement precision through standardized interfaces between modules.
3Ease of manufacture
If multiple separate systems are maintained by different organizations, then organizational autonomy is preserved, but duplication of efforts increases costs and variability
Solution Approach 1:
The patent creates a universal classification system that can be accessed by multiple health plans and organizations simultaneously. The system maintains organizational flexibility by allowing each organization to query the system for their specific medications and integrate results into their own workflows, while eliminating duplication of classification efforts across organizations, thereby reducing costs without sacrificing adaptability.
Data Source
AI summary
Systems and methods for automatically scoring and classifying characteristics of complex medications and emerging therapies are disclosed. In some embodiments, a disclosed method includes obtaining a request for classification determination of a medication, extracting, based on variables of a scoring model, relevant data of the medication from at least one database, and computing, using the scoring model, a score for the medication based on the relevant data. The method further includes generating, based on the score, a classification, and transmitting at least one of the classification or the medication score to a user.