Machine Learning Sig Code Translation in Pharmacy Systems
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Solution Overview
Problem
Interpreting sig codes used in medical prescriptions is challenging due to their expression in natural language, lack of standardization, and ambiguity, leading to confusion and potential dangers, especially in digitized pharmacy systems where conventional programming approaches result in complex and error-prone codebases.
Innovation Solution
A pharmacy management system utilizing machine learning to train models that analyze sig code utterances, identify entities, and generate structured outputs, enabling automated translation and validation of sig codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit programming approaches (string matching and regular expressions) are used to interpret sig codes, then the system can process structured data, but the codebase becomes inefficient, overly complex, and error-prone
Solution Approach 1:
The patent replaces mechanical programming approaches (string matching, regular expressions) with a machine learning-based natural language processing system. The NLP model automatically learns to interpret sig codes through training on annotated data, eliminating the need for complex hand-crafted rules and regular expressions while improving accuracy and maintainability
2Productivity
If conventional programming methods are used to handle unstandardized sig codes, then the system can process prescriptions, but it requires extreme ongoing maintenance and is difficult to debug
Solution Approach 1:
The machine learning model performs self-learning and adaptation through training on annotated sig code data. The system automatically improves its interpretation capabilities without requiring manual updates to complex rule sets, reducing ongoing maintenance burden and making the system more self-sufficient
3Adaptability or versatility
If unstandardized natural language sig codes are used in prescriptions, then physicians can express dosing instructions flexibly, but it causes duplicative efforts and error-prone techniques in digitized pharmacy systems
Solution Approach 1:
The patent introduces a natural language processing model as an intermediary between the unstandardized sig code input and the pharmacy system processing. This intermediary automatically standardizes and interprets various natural language expressions of dosing instructions, enabling flexible input while ensuring reliable and consistent processing downstream
Data Source
AI summary
A pharmacy management system includes a processor and a memory storing instructions that, when executed by processor, cause the pharmacy management system to train a machine learning model; receive a sig code utterance; analyze the sig code utterance using the trained machine learning model; and generate an output. A computer-implemented method includes training a machine learning model; receiving a sig code utterance; analyzing the sig code utterance using the trained machine learning model; and generating an output. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to train a machine learning model; receive a sig code utterance; analyze the sig code utterance using the trained machine learning model; and generate an output.