AI Patient Drug Label Update System
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Solution Overview
Problem
The manual process of updating Patient Drug Labels from Scientific Drug Labels is time-consuming and inefficient, requiring multiple rounds of verification and approval by Subject Matter Experts.
Innovation Solution
A system and method that automatically identifies updates in Scientific Drug Labels relevant to patients, converts complex medical language into simplified patient-friendly language, and incorporates these updates into Patient Drug Labels, while also requesting and validating user feedback to improve engine training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual process is used to update Patient Drug Labels from Scientific Drug Labels, then accuracy and verification can be maintained, but time consumption and inefficiency increase
Solution Approach 1:
The system enables self-service by automatically updating Patient Drug Labels using AI-driven processes. The automated label generator retrieves updated scientific drug label information, processes it through natural language understanding, and generates updated patient labels without requiring manual intervention for each update step, thereby reducing time while maintaining accuracy through systematic verification protocols
Solution Approach 2:
The patent replaces the mechanical manual process with an automated AI-based system. The processor automatically compares updated scientific labels with existing patient labels, identifies changes, and generates updated patient labels through computational processes, eliminating the need for manual comparison and typing while maintaining verification standards
2Reliability
If manual process is used to update Patient Drug Labels, then verification can be performed, but the number of rounds of verification and approval increases
Solution Approach 1:
The system incorporates feedback mechanisms where the automated generator produces draft labels that are then reviewed by subject matter experts. The feedback from these reviews is used to refine and retrain the AI models, creating a continuous improvement loop that reduces the number of verification rounds needed while maintaining high reliability through iterative refinement
Solution Approach 2:
The system performs preliminary action by pre-processing and pre-verification of label updates before they reach the final approval stage. The automated system conducts initial verification and generates preliminary labels that require fewer rounds of expert review, as the bulk of verification work is completed automatically in advance
3Ease of operation
If complex medical language is converted to simplified patient-friendly language, then patient understanding improves, but information accuracy may be compromised
Solution Approach 1:
The system uses an intermediary approach where the AI processor acts as a mediator between the complex scientific drug label language and the simplified patient-friendly language. The processor translates and simplifies the language while preserving critical information accuracy through structured processing and validation protocols, ensuring that the simplified version remains faithful to the original scientific content
Solution Approach 2:
The patent applies parameter changes by modifying the language parameters (simplicity, readability) while maintaining the information parameters (accuracy, completeness). The automated generator adjusts linguistic parameters to make the label patient-friendly while using validation mechanisms to ensure that the semantic meaning and critical information remain accurate to the original scientific label
Data Source
AI summary
In an approach for automatically identifying one or more updates in a Scientific Drug Label (SL) relevant to a patient and incorporating the one or more updates into a Patient Drug Label (PL), a processor receives a pair of documents, wherein the pair of documents include the SL and the PL. A processor converts a complex medical language of the SL into a simplified patient friendly language. A processor identifies one or more words, one or more phrases, or one or more sentences that have been modified, inserted, or deleted. A processor searches for a location in the PL that closely maps to the one or more words, the one or more phrases, or the one or more sentences to the SL. A processor incorporates the one or more words, the one or more phrases, or the one or more sentences in a mapped location of the PL.