AI Master Formulation Generation for Drug Compounding
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Solution Overview
Problem
Conventional approaches to generating Master Formulation Records (MFRs) in drug compounding are inefficient and prone to errors, leading to potential patient harm due to incomplete procedures and lack of expertise in managing and maintaining MFRs, especially for rarely compounded medications or those requiring complex compounding steps.
Innovation Solution
A system utilizing two AI engines to receive and compare recipe data, generate MFRs for compounded preparations or drug products, and continuously improve the AI engine's accuracy through feedback loops, including a database of synonyms and validated recipe-formulation sets, to ensure compliance and reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used to generate Master Formulation Records, then manual expertise and resources are required, but this leads to incomplete procedures, errors, and potential patient harm
Solution Approach 1:
The patent replaces manual expert review and maintenance of Master Formulation Records with an artificial intelligence system. The AI engine automatically generates, validates, and maintains MFRs by analyzing recipe data against a database of synonyms and validated formulations, eliminating the need for manual expertise while ensuring procedural completeness and accuracy
Solution Approach 2:
The AI engine performs self-learning and self-improvement by continuously analyzing validated recipe-formulation sets and feedback from users. The system automatically updates its knowledge base and refines its generation capabilities without requiring manual reconfiguration, enabling it to maintain high reliability while adapting to new compounds and procedures
2Productivity
If AI engines are used to generate MFRs, then efficiency and accuracy improve, but continuous validation and retraining are required
Solution Approach 1:
The system performs preliminary validation by comparing generated MFRs against a pre-established database of validated recipe-formulation sets and synonyms before finalization. This preliminary check ensures accuracy is verified in advance, reducing the need for time-consuming post-generation validation and retraining cycles
Solution Approach 2:
The patent implements a feedback mechanism where user corrections and validations of generated MFRs are fed back into the AI engine's training data. This continuous feedback loop allows the system to learn from real-world usage and improve its accuracy over time, reducing the frequency and duration of explicit retraining events while maintaining high productivity
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for generating missing master formulation records. An embodiment operates by receiving recipe data for a compounded preparation or drug product. The embodiment compares using a first artificial intelligence (AI) engine the recipe data with stored recipe data to determine whether a master formulation record corresponding to the recipe data is available. The embodiment generates, using a second AI engine, the master formulation record for the compounded preparation or drug product based on a determination that the master formulation record corresponding to the recipe data is not available. The embodiment receives an input comprising an approval or a correction for the generated master formulation record. The embodiment retrains the second AI engine based on the input. The embodiment finally generates, using the retrained second AI engine, a missing master formulation record for another recipe data.